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BIOMD0000000655
@ v0.0.1
Padala2017- ERK, PI3K/Akt and Wnt signalling network (PTEN mutation)Crosstalk model of the ERK, Wnt and Akt Signalling p…
DetailsPerturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation. link: http://identifiers.org/pubmed/28367561
Parameters:
Name | Description |
---|---|
k33a2 = 0.8333; k33a1 = 0.01667 |
Reaction: APC + Axin => APCAxin, Rate Law: Cell*(k33a1*Axin*APC-k33a2*APCAxin)/Cell |
Kcat24 = 32.344; Km24 = 35954.3 |
Reaction: pC3G + Rap1 => pC3G + pRap1, Rate Law: Cell*Kcat24*pC3G*Rap1/(Rap1+Km24)/Cell |
Kcat17b = 15.1212; Km17b = 119355.0 |
Reaction: pAkt + pBRaf => pAkt + BRaf, Rate Law: Cell*Kcat17b*pBRaf*pAkt/(Km17b+pBRaf)/Cell |
Km16b = 62464.6; Kcat16b = 0.8841 |
Reaction: pRap1 + BRaf => pRap1 + pBRaf, Rate Law: Cell*Kcat16b*pRap1*BRaf/(BRaf+Km16b)/Cell |
k6a = 2.5 |
Reaction: pSOS => SOS, Rate Law: Cell*k6a*pSOS/Cell |
k3 = 0.00125 |
Reaction: fEGFR => null, Rate Law: Cell*k3*fEGFR/Cell |
Km22b = 100.0; Kcat22b = 48.667 |
Reaction: pAkt => Akt, Rate Law: Cell*Kcat22b*pAkt/(Km22b+pAkt)/Cell |
Kcat23a = 694.73; Km23a = 6086100.0 |
Reaction: bEGFR + C3G => bEGFR + pC3G, Rate Law: Cell*Kcat23a*bEGFR*C3G/(C3G+Km23a)/Cell |
Kcat8b = 1509.36; Km8b = 1432410.0 |
Reaction: RasGap + pRas => RasGap + Ras, Rate Law: Cell*Kcat8b*RasGap*pRas/(pRas+Km8b)/Cell |
k18a = 0.005 |
Reaction: pP90Rsk => P90Rsk, Rate Law: Cell*k18a*pP90Rsk/Cell |
Km20 = 4.0; Kcat20 = 4.0 |
Reaction: pPI3K + PIP2 => pPI3K + PIP3, Rate Law: Cell*Kcat20*pPI3K*PIP2/(PIP2+Km20)/Cell |
Km22a = 100.0; Kcat22a = 0.33 |
Reaction: PIP3 + Akt => PIP3 + pAkt, Rate Law: Cell*Kcat22a*PIP3*Akt/(Akt+Km22a)/Cell |
Km7 = 35954.3; Kcat7 = 32.644 |
Reaction: pSOS + Ras => pSOS + pRas, Rate Law: Cell*Kcat7*pSOS*Ras/(Ras+Km7)/Cell |
Kcat13 = 9.8537; Km13 = 1007300.0 |
Reaction: pMEK + ERK => pMEK + pERK, Rate Law: Cell*Kcat13*pMEK*ERK/(ERK+Km13)/Cell |
k35 = 3.433 |
Reaction: pAPCpAxinGSK3BBCatenin => pAPCpAxinGSK3BpBCatenin, Rate Law: Cell*k35*pAPCpAxinGSK3BBCatenin/Cell |
k19c = 0.005 |
Reaction: pPI3K => PI3K, Rate Law: Cell*k19c*pPI3K/Cell |
k26b = 3.85E-4 |
Reaction: PKCD => null, Rate Law: Cell*k26b*PKCD/Cell |
k36 = 3.433 |
Reaction: pAPCpAxinGSK3BpBCatenin => pBCatenin + pAPCpAxinGSK3B, Rate Law: Cell*k36*pAPCpAxinGSK3BpBCatenin/Cell |
Km9a = 62464.6; Kcat9a = 0.884096 |
Reaction: pRas + Raf1 => pRas + pRaf1, Rate Law: Cell*Kcat9a*pRas*Raf1/(Raf1+Km9a)/Cell |
Kcat12 = 2.8324; Km12 = 518750.0 |
Reaction: pMEK + PP2A => MEK + PP2A, Rate Law: Cell*Kcat12*PP2A*pMEK/(pMEK+Km12)/Cell |
k53 = 2.8833E-4; k52 = 3.85E-5; k54 = 1.5; k51 = 0.003465 |
Reaction: PKCD + pERK + bEGFR + SOS => PKCD + pERK + bEGFR + pSOS, Rate Law: Cell*(k51*bEGFR+k52+k53*PKCD)/(1+pERK/k54)/Cell |
k37a2 = 20.0; k37a1 = 0.01667 |
Reaction: BCatenin + APC => APCBCatenin, Rate Law: Cell*(k37a1*APC*BCatenin-k37a2*APCBCatenin)/Cell |
Kcat14 = 8.8912; Km14 = 3496500.0 |
Reaction: pERK + PP2A => ERK + PP2A, Rate Law: Cell*Kcat14*PP2A*pERK/(pERK+Km14)/Cell |
k312 = 0.01515; k311 = 0.001515 |
Reaction: APCAxin + GSK3B => APCAxinGSK3B, Rate Law: Cell*(k311*GSK3B*APCAxin-k312*APCAxinGSK3B)/Cell |
Kcat19b = 0.07711; Km19b = 272056.0 |
Reaction: PI3K => pPI3K; pRas, Rate Law: Cell*Kcat19b*pRas*PI3K/(PI3K+Km19b)/Cell |
k23b = 2.5 |
Reaction: pC3G => C3G, Rate Law: Cell*k23b*pC3G/Cell |
k37c = 4.283E-6 |
Reaction: BCatenin => null, Rate Law: Cell*k37c*BCatenin/Cell |
V37b = 0.00705 |
Reaction: null => BCatenin, Rate Law: Cell*V37b/Cell |
Km9b = 15.0; W = 0.0; k9b = 0.025 |
Reaction: X + Raf1 => X + pRaf1, Rate Law: Cell*k9b*W*X*Raf1/(Km9b+Raf1)/Cell |
Kcat6b = 1611.97; Km6b = 896896.0 |
Reaction: pP90Rsk + pSOS => pP90Rsk + SOS, Rate Law: Cell*Kcat6b*pP90Rsk*pSOS/(pSOS+Km6b)/Cell |
Kcat18b = 0.02137; Km18b = 763523.0 |
Reaction: pERK + P90Rsk => pERK + pP90Rsk, Rate Law: Cell*Kcat18b*pERK*P90Rsk/(P90Rsk+Km18b)/Cell |
k32b = 0.002217 |
Reaction: pAPCpAxinGSK3B => APCAxinGSK3B, Rate Law: Cell*k32b*pAPCpAxinGSK3B/Cell |
k22 = 0.121008; k21 = 2.18503E-5 |
Reaction: fEGFR + EGF => bEGFR, Rate Law: Cell*(k21*EGF*fEGFR-k22*bEGFR)/Cell |
V15b = 4.0 |
Reaction: pRKIP => RKIP, Rate Law: Cell*V15b*pRKIP/Cell |
k32a = 0.00445 |
Reaction: APCAxinGSK3B => pAPCpAxinGSK3B, Rate Law: Cell*k32a*APCAxinGSK3B/Cell |
Kcat10b = 15.1212; Km10b = 119355.0 |
Reaction: pAkt + pRaf1 => pAkt + Raf1, Rate Law: Cell*Kcat10b*pAkt*pRaf1/(pRaf1+Km10b)/Cell |
k11b2 = 120.0; k11b1 = 1.1167E-5 |
Reaction: pRKIP + pRaf1 + MEK => pRKIP + pRaf1 + pMEK; RKIP, Rate Law: Cell*k11b1*pRaf1*MEK/(1+((RKIP-pRKIP)/k11b2)^2)/Cell |
k342 = 2.0; k341 = 0.01667 |
Reaction: BCatenin + pAPCpAxinGSK3B => pAPCpAxinGSK3BBCatenin, Rate Law: Cell*(k341*pAPCpAxinGSK3B*BCatenin-k342*pAPCpAxinGSK3BBCatenin)/Cell |
Km10a = 1061.7; Kcat10a = 0.12633 |
Reaction: RafPPtase + pRaf1 => RafPPtase + Raf1, Rate Law: Cell*Kcat10a*RafPPtase*pRaf1/(pRaf1+Km10a)/Cell |
Km19a = 184912.0; Kcat19a = 10.6737 |
Reaction: bEGFR + PI3K => bEGFR + pPI3K, Rate Law: Cell*Kcat19a*bEGFR*PI3K/(PI3K+Km19a)/Cell |
Kcat11a = 185.76; Km11a = 4768400.0 |
Reaction: pBRaf + MEK => pBRaf + pMEK, Rate Law: Cell*Kcat11a*pBRaf*MEK/(MEK+Km11a)/Cell |
k4 = 0.2 |
Reaction: bEGFR => null, Rate Law: Cell*k4*bEGFR/Cell |
k381 = 0.01667; k382 = 0.5 |
Reaction: TCF + BCatenin => TCFBCatenin, Rate Law: Cell*(k381*BCatenin*TCF-k382*TCFBCatenin)/Cell |
Kcat16a = 0.8841; Km16a = 62645.0 |
Reaction: pRas + BRaf => pRas + pBRaf, Rate Law: Cell*Kcat16a*pRas*BRaf/(BRaf+Km16a)/Cell |
V1 = 100.0 |
Reaction: pEGFR => fEGFR, Rate Law: Cell*V1/Cell |
Km25 = 1432400.0; Kcat25 = 1509.4 |
Reaction: Rap1Gap + pRap1 => Rap1Gap + Rap1, Rate Law: Cell*Kcat25*Rap1Gap*pRap1/(pRap1+Km25)/Cell |
Kcat17a = 0.12633; Km17a = 1061.71 |
Reaction: RafPPtase + pBRaf => RafPPtase + BRaf, Rate Law: Cell*Kcat17a*RafPPtase*pBRaf/(Km17a+RafPPtase)/Cell |
V26a = 0.00154; k26a = 20.0 |
Reaction: GSK3B => GSK3B + PKCD, Rate Law: Cell*V26a/(1+(GSK3B/k26a)^2.5)/Cell |
Kcat27d = 0.01541 |
Reaction: pGSK3B => GSK3B, Rate Law: Cell*Kcat27d*pGSK3B/Cell |
k41 = 0.00695 |
Reaction: pBCatenin => null, Rate Law: Cell*k41*pBCatenin/Cell |
Kcat27b = 0.04596 |
Reaction: pAkt + GSK3B => pAkt + pGSK3B, Rate Law: Cell*Kcat27b*GSK3B*pAkt/Cell |
k33b = 0.002783 |
Reaction: Axin => null, Rate Law: Cell*k33b*Axin/Cell |
k30 = 8.33E-4 |
Reaction: Dsha + APCAxinGSK3B => GSK3B + APCAxin + Dsha, Rate Law: Cell*k30*Dsha*APCAxinGSK3B/Cell |
k15a = 1.3 |
Reaction: pERK + RKIP => pERK + pRKIP, Rate Law: Cell*k15a*pERK*(RKIP-pRKIP)/Cell |
k33c1 = 1.37E-6; k33c2 = 1.667E-8 |
Reaction: BCatenin + TCFBCatenin => BCatenin + TCFBCatenin + Axin, Rate Law: Cell*(k33c1+k33c2*(TCFBCatenin+BCatenin))/Cell |
States:
Name | Description |
---|---|
pC3G |
[Complement C3; phosphorylated] |
pBCatenin |
[Catenin beta-1; phosphorylated] |
bEGFR |
[Epidermal growth factor receptor; Pro-epidermal growth factor] |
pPI3K |
[0027264; phosphorylated] |
APCAxin |
[Axin-1; Adenomatous polyposis coli protein] |
Akt |
[RAC-alpha serine/threonine-protein kinase] |
pSOS |
[Son of sevenless homolog 1; phosphorylated] |
pAPCpAxinGSK3BBCatenin |
[Axin-1; Glycogen synthase kinase-3 beta; Catenin beta-1; Adenomatous polyposis coli protein; phosphorylated] |
EGF |
[Pro-epidermal growth factor] |
pP90Rsk |
[Ribosomal protein S6 kinase alpha-1; phosphorylated] |
pMEK |
[Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated] |
BCatenin |
[Catenin beta-1] |
pAkt |
[RAC-alpha serine/threonine-protein kinase; phosphorylated] |
pRKIP |
[Phosphatidylethanolamine-binding protein 1; phosphorylated] |
BRaf |
[Serine/threonine-protein kinase B-raf] |
RKIP |
[Phosphatidylethanolamine-binding protein 1] |
PIP3 |
[0016618] |
pEGFR |
[Epidermal growth factor receptor; phosphorylated] |
PKCD |
[Protein kinase C delta type] |
pRaf1 |
[RAF proto-oncogene serine/threonine-protein kinase; phosphorylated] |
MEK |
[Dual specificity mitogen-activated protein kinase kinase 1] |
C3G |
[Rap guanine nucleotide exchange factor 1] |
Dsha |
[Segment polarity protein dishevelled homolog DVL-1; phosphorylated] |
pAPCpAxinGSK3B |
[Adenomatous polyposis coli protein; Axin-1; Glycogen synthase kinase-3 beta; phosphorylated] |
P90Rsk |
[Ribosomal protein S6 kinase alpha-1] |
pGSK3B |
[Glycogen synthase kinase-3 beta; phosphorylated] |
Raf1 |
[RAF proto-oncogene serine/threonine-protein kinase] |
PP2A |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
pRas |
[Ras-related protein R-Ras2; phosphorylated] |
APCBCatenin |
[Catenin beta-1; Adenomatous polyposis coli protein] |
pERK |
[Mitogen-activated protein kinase 1; phosphorylated] |
fEGFR |
[Epidermal growth factor receptor] |
APC |
[Adenomatous polyposis coli protein] |
pBRaf |
[Serine/threonine-protein kinase B-raf; phosphorylated] |
GSK3B |
[Glycogen synthase kinase-3 beta] |
ERK |
[Mitogen-activated protein kinase 3] |
pRap1 |
[Ras-related protein Rap-1A; phosphorylated] |
APCAxinGSK3B |
[Axin-1; Adenomatous polyposis coli protein; Glycogen synthase kinase-3 beta] |
Axin |
[Axin-1] |
pAPCpAxinGSK3BpBCatenin |
[Catenin beta-1; Glycogen synthase kinase-3 beta; Axin-1; Adenomatous polyposis coli protein; phosphorylated] |
Observables: none
BIOMD0000000654
@ v0.0.1
Padala2017- ERK, PI3K/Akt and Wnt signalling network (Ras mutated)Crosstalk model of the ERK, Wnt and Akt signalling pat…
DetailsPerturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation. link: http://identifiers.org/pubmed/28367561
Parameters:
Name | Description |
---|---|
k27c = 1.5E-4 |
Reaction: RKIP => RKIP + GSK3B, Rate Law: Cell*k27c*RKIP/Cell |
k33a2 = 0.8333; k33a1 = 0.01667 |
Reaction: APC + Axin => APCAxin, Rate Law: Cell*(k33a1*Axin*APC-k33a2*APCAxin)/Cell |
W = 0.0; k28 = 0.003 |
Reaction: Dshi => Dsha, Rate Law: Cell*k28*Dshi*W/Cell |
Kcat24 = 32.344; Km24 = 35954.3 |
Reaction: pC3G + Rap1 => pC3G + pRap1, Rate Law: Cell*Kcat24*pC3G*Rap1/(Rap1+Km24)/Cell |
Kcat17b = 15.1212; Km17b = 119355.0 |
Reaction: pAkt + pBRaf => pAkt + BRaf, Rate Law: Cell*Kcat17b*pBRaf*pAkt/(Km17b+pBRaf)/Cell |
Km16b = 62464.6; Kcat16b = 0.8841 |
Reaction: pRap1 + BRaf => pRap1 + pBRaf, Rate Law: Cell*Kcat16b*pRap1*BRaf/(BRaf+Km16b)/Cell |
k6a = 2.5 |
Reaction: pSOS => SOS, Rate Law: Cell*k6a*pSOS/Cell |
Kcat23a = 694.73; Km23a = 6086100.0 |
Reaction: bEGFR + C3G => bEGFR + pC3G, Rate Law: Cell*Kcat23a*bEGFR*C3G/(C3G+Km23a)/Cell |
Km22b = 100.0; Kcat22b = 48.667 |
Reaction: pAkt => Akt, Rate Law: Cell*Kcat22b*pAkt/(Km22b+pAkt)/Cell |
k18a = 0.005 |
Reaction: pP90Rsk => P90Rsk, Rate Law: Cell*k18a*pP90Rsk/Cell |
Km20 = 4.0; Kcat20 = 4.0 |
Reaction: pPI3K + PIP2 => pPI3K + PIP3, Rate Law: Cell*Kcat20*pPI3K*PIP2/(PIP2+Km20)/Cell |
Km22a = 100.0; Kcat22a = 0.33 |
Reaction: PIP3 + Akt => PIP3 + pAkt, Rate Law: Cell*Kcat22a*PIP3*Akt/(Akt+Km22a)/Cell |
Km7 = 35954.3; Kcat7 = 32.644 |
Reaction: pSOS + Ras => pSOS + pRas, Rate Law: Cell*Kcat7*pSOS*Ras/(Ras+Km7)/Cell |
k35 = 3.433 |
Reaction: pAPCpAxinGSK3BBCatenin => pAPCpAxinGSK3BpBCatenin, Rate Law: Cell*k35*pAPCpAxinGSK3BBCatenin/Cell |
k19c = 0.005 |
Reaction: pPI3K => PI3K, Rate Law: Cell*k19c*pPI3K/Cell |
k26b = 3.85E-4 |
Reaction: PKCD => null, Rate Law: Cell*k26b*PKCD/Cell |
k36 = 3.433 |
Reaction: pAPCpAxinGSK3BpBCatenin => pBCatenin + pAPCpAxinGSK3B, Rate Law: Cell*k36*pAPCpAxinGSK3BpBCatenin/Cell |
V8a = 0.0717 |
Reaction: null => Ras, Rate Law: Cell*V8a/Cell |
Km9a = 62464.6; Kcat9a = 0.884096 |
Reaction: pRas + Raf1 => pRas + pRaf1, Rate Law: Cell*Kcat9a*pRas*Raf1/(Raf1+Km9a)/Cell |
Kcat12 = 2.8324; Km12 = 518750.0 |
Reaction: pMEK + PP2A => MEK + PP2A, Rate Law: Cell*Kcat12*PP2A*pMEK/(pMEK+Km12)/Cell |
k53 = 2.8833E-4; k52 = 3.85E-5; k54 = 1.5; k51 = 0.003465 |
Reaction: PKCD + pERK + bEGFR + SOS => PKCD + pERK + bEGFR + pSOS, Rate Law: Cell*(k51*bEGFR+k52+k53*PKCD)/(1+pERK/k54)/Cell |
k37a2 = 20.0; k37a1 = 0.01667 |
Reaction: BCatenin + APC => APCBCatenin, Rate Law: Cell*(k37a1*APC*BCatenin-k37a2*APCBCatenin)/Cell |
Kcat14 = 8.8912; Km14 = 3496500.0 |
Reaction: pERK + PP2A => ERK + PP2A, Rate Law: Cell*Kcat14*PP2A*pERK/(pERK+Km14)/Cell |
k312 = 0.01515; k311 = 0.001515 |
Reaction: APCAxin + GSK3B => APCAxinGSK3B, Rate Law: Cell*(k311*GSK3B*APCAxin-k312*APCAxinGSK3B)/Cell |
Kcat19b = 0.07711; Km19b = 272056.0 |
Reaction: PI3K => pPI3K; pRas, Rate Law: Cell*Kcat19b*pRas*PI3K/(PI3K+Km19b)/Cell |
k23b = 2.5 |
Reaction: pC3G => C3G, Rate Law: Cell*k23b*pC3G/Cell |
Km9b = 15.0; W = 0.0; k9b = 0.025 |
Reaction: X + Raf1 => X + pRaf1, Rate Law: Cell*k9b*W*X*Raf1/(Km9b+Raf1)/Cell |
Kcat6b = 1611.97; Km6b = 896896.0 |
Reaction: pP90Rsk + pSOS => pP90Rsk + SOS, Rate Law: Cell*Kcat6b*pP90Rsk*pSOS/(pSOS+Km6b)/Cell |
Kcat18b = 0.02137; Km18b = 763523.0 |
Reaction: pERK + P90Rsk => pERK + pP90Rsk, Rate Law: Cell*Kcat18b*pERK*P90Rsk/(P90Rsk+Km18b)/Cell |
k22 = 0.121008; k21 = 2.18503E-5 |
Reaction: fEGFR + EGF => bEGFR, Rate Law: Cell*(k21*EGF*fEGFR-k22*bEGFR)/Cell |
V15b = 4.0 |
Reaction: pRKIP => RKIP, Rate Law: Cell*V15b*pRKIP/Cell |
k32a = 0.00445 |
Reaction: APCAxinGSK3B => pAPCpAxinGSK3B, Rate Law: Cell*k32a*APCAxinGSK3B/Cell |
Kcat21 = 5.5; Km21 = 0.08 |
Reaction: PTEN + PIP3 => PTEN + PIP2, Rate Law: Cell*Kcat21*PTEN*PIP3/(PIP3+Km21)/Cell |
Kcat10b = 15.1212; Km10b = 119355.0 |
Reaction: pAkt + pRaf1 => pAkt + Raf1, Rate Law: Cell*Kcat10b*pAkt*pRaf1/(pRaf1+Km10b)/Cell |
k11b2 = 120.0; k11b1 = 1.1167E-5 |
Reaction: pRKIP + pRaf1 + MEK => pRKIP + pRaf1 + pMEK; RKIP, Rate Law: Cell*k11b1*pRaf1*MEK/(1+((RKIP-pRKIP)/k11b2)^2)/Cell |
Km10a = 1061.7; Kcat10a = 0.12633 |
Reaction: RafPPtase + pRaf1 => RafPPtase + Raf1, Rate Law: Cell*Kcat10a*RafPPtase*pRaf1/(pRaf1+Km10a)/Cell |
Kcat11a = 185.76; Km11a = 4768400.0 |
Reaction: pBRaf + MEK => pBRaf + pMEK, Rate Law: Cell*Kcat11a*pBRaf*MEK/(MEK+Km11a)/Cell |
Km19a = 184912.0; Kcat19a = 10.6737 |
Reaction: bEGFR + PI3K => bEGFR + pPI3K, Rate Law: Cell*Kcat19a*bEGFR*PI3K/(PI3K+Km19a)/Cell |
k29 = 0.003 |
Reaction: Dsha => Dshi, Rate Law: Cell*k29*Dsha/Cell |
k381 = 0.01667; k382 = 0.5 |
Reaction: TCF + BCatenin => TCFBCatenin, Rate Law: Cell*(k381*BCatenin*TCF-k382*TCFBCatenin)/Cell |
Kcat16a = 0.8841; Km16a = 62645.0 |
Reaction: pRas + BRaf => pRas + pBRaf, Rate Law: Cell*Kcat16a*pRas*BRaf/(BRaf+Km16a)/Cell |
Kcat17a = 0.12633; Km17a = 1061.71 |
Reaction: RafPPtase + pBRaf => RafPPtase + BRaf, Rate Law: Cell*Kcat17a*RafPPtase*pBRaf/(Km17a+RafPPtase)/Cell |
Km25 = 1432400.0; Kcat25 = 1509.4 |
Reaction: Rap1Gap + pRap1 => Rap1Gap + Rap1, Rate Law: Cell*Kcat25*Rap1Gap*pRap1/(pRap1+Km25)/Cell |
V26a = 0.00154; k26a = 20.0 |
Reaction: GSK3B => GSK3B + PKCD, Rate Law: Cell*V26a/(1+(GSK3B/k26a)^2.5)/Cell |
Kcat27d = 0.01541 |
Reaction: pGSK3B => GSK3B, Rate Law: Cell*Kcat27d*pGSK3B/Cell |
k41 = 0.00695 |
Reaction: pBCatenin => null, Rate Law: Cell*k41*pBCatenin/Cell |
Kcat27b = 0.04596 |
Reaction: pAkt + GSK3B => pAkt + pGSK3B, Rate Law: Cell*Kcat27b*GSK3B*pAkt/Cell |
Km39 = 15.0; k39 = 0.01 |
Reaction: TCFBCatenin => X + TCFBCatenin, Rate Law: Cell*k39*TCFBCatenin^2/(Km39^2+TCFBCatenin^2)/Cell |
k30 = 8.33E-4 |
Reaction: Dsha + APCAxinGSK3B => GSK3B + APCAxin + Dsha, Rate Law: Cell*k30*Dsha*APCAxinGSK3B/Cell |
k15a = 1.3 |
Reaction: pERK + RKIP => pERK + pRKIP, Rate Law: Cell*k15a*pERK*(RKIP-pRKIP)/Cell |
k33c1 = 1.37E-6; k33c2 = 1.667E-8 |
Reaction: BCatenin + TCFBCatenin => BCatenin + TCFBCatenin + Axin, Rate Law: Cell*(k33c1+k33c2*(TCFBCatenin+BCatenin))/Cell |
States:
Name | Description |
---|---|
pC3G |
[Complement C3; phosphorylated] |
pBCatenin |
[Catenin beta-1; phosphorylated] |
PIP2 |
[0018348] |
PTEN |
[Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN] |
pPI3K |
[0027264; phosphorylated] |
bEGFR |
[Epidermal growth factor receptor; Pro-epidermal growth factor] |
APCAxin |
[Axin-1; Adenomatous polyposis coli protein] |
pSOS |
[Son of sevenless homolog 1; phosphorylated] |
EGF |
[Pro-epidermal growth factor] |
RafPPtase |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
pP90Rsk |
[Ribosomal protein S6 kinase alpha-1; phosphorylated] |
BRaf |
[Serine/threonine-protein kinase B-raf] |
pAkt |
[RAC-alpha serine/threonine-protein kinase; phosphorylated] |
pRKIP |
[Phosphatidylethanolamine-binding protein 1; phosphorylated] |
PIP3 |
[0016618] |
PKCD |
[Protein kinase C delta type] |
RKIP |
[Phosphatidylethanolamine-binding protein 1] |
Ras |
[GTPase HRas; 0010192] |
SOS |
[Son of sevenless homolog 1] |
pRaf1 |
[RAF proto-oncogene serine/threonine-protein kinase; phosphorylated] |
MEK |
[Dual specificity mitogen-activated protein kinase kinase 1] |
PI3K |
[0027264] |
C3G |
[Rap guanine nucleotide exchange factor 1] |
Rap1 |
[Ras-related protein Rap-1A] |
TCFBCatenin |
[Lymphoid enhancer-binding factor 1; Catenin beta-1] |
Dsha |
[Segment polarity protein dishevelled homolog DVL-1; phosphorylated] |
X |
X |
TCF |
[Lymphoid enhancer-binding factor 1] |
P90Rsk |
[Ribosomal protein S6 kinase alpha-1] |
Raf1 |
[RAF proto-oncogene serine/threonine-protein kinase] |
pAPCpAxinGSK3B |
[Adenomatous polyposis coli protein; Axin-1; Glycogen synthase kinase-3 beta; phosphorylated] |
PP2A |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
Dshi |
[Segment polarity protein dishevelled homolog DVL-1] |
pRas |
[Ras-related protein R-Ras2; phosphorylated; 0010192] |
APCBCatenin |
[Catenin beta-1; Adenomatous polyposis coli protein] |
APC |
[Adenomatous polyposis coli protein] |
pBRaf |
[Serine/threonine-protein kinase B-raf; phosphorylated] |
GSK3B |
[Glycogen synthase kinase-3 beta] |
pRap1 |
[Ras-related protein Rap-1A; phosphorylated] |
pAPCpAxinGSK3BpBCatenin |
[Catenin beta-1; Glycogen synthase kinase-3 beta; Axin-1; Adenomatous polyposis coli protein; phosphorylated] |
APCAxinGSK3B |
[Axin-1; Adenomatous polyposis coli protein; Glycogen synthase kinase-3 beta] |
Observables: none
BIOMD0000000960
@ v0.0.1
This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmissi…
DetailsThis paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions. link: http://identifiers.org/pubmed/32735581
Parameters: none
States: none
Observables: none
BIOMD0000000325
@ v0.0.1
This is the model of the minmal 2 feedback switch described in the article: **Synthetic conversion of a graded recepto…
DetailsThe ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network 'switches' have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (n(H)>20), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties. Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways. link: http://identifiers.org/pubmed/21451590
Parameters:
Name | Description |
---|---|
kdegR = 0.005 |
Reaction: R =>, Rate Law: cell*kdegR*R |
kdegC = 0.01 |
Reaction: C =>, Rate Law: cell*kdegC*C |
kdegX = 0.005 |
Reaction: X =>, Rate Law: cell*kdegX*X |
Rs = 3.0; BR = 0.005; KD = 200.0 |
Reaction: => R; A, Rate Law: cell*(BR+Rs*A/(KD+A)) |
k3 = 45.0 |
Reaction: X => C + A, Rate Law: cell*k3*X |
kdegA = 0.005 |
Reaction: A =>, Rate Law: cell*kdegA*A |
koff = 0.05; kon = 0.001 |
Reaction: R + L => C, Rate Law: cell*(kon*L*R-koff*C) |
kdegI = 0.005 |
Reaction: I =>, Rate Law: cell*kdegI*I |
k2 = 5.0; k1 = 1.0 |
Reaction: C + I => X, Rate Law: cell*(k1*C*I-k2*X) |
BI = 0.005; TFs = 3.0; KD = 200.0 |
Reaction: => I; A, Rate Law: cell*(BI+TFs*A/(KD+A)) |
States:
Name | Description |
---|---|
I |
[Transcription factor SKN7] |
A |
[obsolete transcription activator activity; phosphorylated L-histidine; Transcription factor SKN7] |
X |
[transmembrane histidine kinase cytokinin receptor activity; Transcription factor SKN7; Histidine kinase 4; N(6)-isopentenyladenosine] |
C |
[transmembrane histidine kinase cytokinin receptor activity; Histidine kinase 4; N(6)-isopentenyladenosine] |
L |
[cytokinin; N(6)-isopentenyladenosine; CHEMBL1163500] |
R |
[Histidine kinase 4] |
Observables: none
BIOMD0000000968
@ v0.0.1
Interleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Bi…
DetailsInterleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Binding of IL-7 to its cognate receptor, the IL-7 receptor (IL-7R), activates multiple pathways that regulate lymphocyte survival, glucose uptake, proliferation and differentiation. There has been much interest in understanding how IL-7 receptor signaling is modulated at multiple interconnected network levels. This review examines how the strength of the signal through the IL-7 receptor is modulated in T and B cells, including the use of shared receptor components, signaling crosstalk, shared interaction domains, feedback loops, integrated gene regulation, multimerization and ligand competition. We discuss how these network control mechanisms could integrate to govern the properties of IL-7R signaling in lymphocytes in health and disease. Analysis of IL-7 receptor signaling at a network level in a systematic manner will allow for a comprehensive approach to understanding the impact of multiple signaling pathways on lymphocyte biology. link: http://identifiers.org/pubmed/18445337
Parameters: none
States: none
Observables: none
BIOMD0000000620
@ v0.0.1
Palmer2014 - Effect of IL-1β-Blocking therapies in T2DM - Disease Condition This is the model with disease state initia…
DetailsRecent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment. link: http://identifiers.org/pubmed/24918743
Parameters:
Name | Description |
---|---|
taus = 0.5 |
Reaction: TigB =>, Rate Law: taus*TigB |
Kxg = 1.6E-5 |
Reaction: Glucose =>, Rate Law: Kxg*Glucose |
placebo_on = 0.0; kplacebo = 0.00137 |
Reaction: => IL1b, Rate Law: placebo_on*kplacebo |
vfg = 4.0; tauf = 0.5; kmf = 0.021; IL1R = 0.02341920375; kf = 0.00957754; kmfg = 9.0; xfg = 4.0; vf = 0.4 |
Reaction: => f; Glucose, Rate Law: tauf*kf*(1+vfg*Glucose^xfg/(kmfg^xfg+Glucose^xfg))*(1+vf*IL1R/(kmf+IL1R)) |
replication = 5.12314779E-4 |
Reaction: => B, Rate Law: replication*B |
Kxgi = 2.24E-5 |
Reaction: Glucose => ; Insulin, Rate Law: Kxgi*Insulin*Glucose |
Tgl = 0.025405 |
Reaction: => Glucose, Rate Law: Tgl |
Kglucose = 2.92E-4; lambda = 0.743; Kin = 1.05; Ktr = 0.12 |
Reaction: rbc1 = (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1, Rate Law: (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1 |
Gh = 9.0; vh = 4.0 |
Reaction: => Proinsulin; TigB, B, f, Glucose, Rate Law: f*(Glucose/Gh)^vh/(1+(Glucose/Gh)^vh)*TigB*B |
apoptosis = 7.543653797E-4 |
Reaction: B =>, Rate Law: apoptosis*B |
kab = 3.94; Vp = 48.0 |
Reaction: => Anakinra; Anakinrasc, Rate Law: kab*Anakinrasc/Vp |
k1 = 0.2; placebo_on = 0.0; k2 = 0.0025 |
Reaction: IL1b =>, Rate Law: piecewise((1-placebo_on)*k1*IL1b, time < 91, (1-placebo_on)*k2*IL1b) |
k1 = 0.2; il1bH = 0.05; placebo_on = 0.0; il1b0 = 5.0; kplacebo = 0.00137; k2 = 0.0025 |
Reaction: => IL1b, Rate Law: piecewise((1-placebo_on)*k1*il1bH, time < 91, (1-placebo_on)*k2*(il1b0+kplacebo*time)) |
CL = 432.0; Vp = 48.0 |
Reaction: Anakinra =>, Rate Law: CL/Vp*Anakinra |
IL1R = 0.02341920375; taus = 0.5; vs = 0.7; ks = 0.291008; kms = 0.021 |
Reaction: => TigB, Rate Law: taus*ks*(1-vs*IL1R/(kms+IL1R)) |
Kglucose = 2.92E-4; lambda = 0.743; Ktr = 0.12 |
Reaction: rbc5 = (Ktr*rbc4-Ktr*rbc5)-Kglucose*Glucose^lambda*rbc5, Rate Law: (Ktr*rbc4-Ktr*rbc5)-Kglucose*Glucose^lambda*rbc5 |
Kxi = 0.05 |
Reaction: Proinsulin =>, Rate Law: 0.1*Kxi*Proinsulin |
kab = 3.94 |
Reaction: Anakinrasc =>, Rate Law: kab*Anakinrasc |
tauf = 0.5 |
Reaction: f =>, Rate Law: tauf*f |
States:
Name | Description |
---|---|
Glucose |
[glucose] |
f |
[insulin secretion] |
a1c5 |
[urn:miriam:efo:EFO%3A0004541] |
a1c1 |
[urn:miriam:efo:EFO%3A0004541] |
a1c8 |
[urn:miriam:efo:EFO%3A0004541] |
a1c3 |
[urn:miriam:efo:EFO%3A0004541] |
rbc12 |
[erythrocyte] |
IL1b |
[Interleukin-1 beta] |
rbc6 |
[erythrocyte] |
B |
[pancreatic beta cell] |
a1c12 |
[urn:miriam:efo:EFO%3A0004541] |
rbc3 |
[erythrocyte] |
rbc1 |
[erythrocyte] |
Proinsulin |
[Insulin] |
Anakinra |
[Interleukin-1 receptor antagonist protein; pharmaceutical] |
a1c7 |
[urn:miriam:efo:EFO%3A0004541] |
a1c11 |
[urn:miriam:efo:EFO%3A0004541] |
rbc2 |
[erythrocyte] |
rbc11 |
[erythrocyte] |
a1c6 |
[urn:miriam:efo:EFO%3A0004541] |
rbc9 |
[erythrocyte] |
TigB |
[insulin secretion] |
rbc5 |
[erythrocyte] |
Anakinrasc |
[Interleukin-1 receptor antagonist protein] |
rbc7 |
[erythrocyte] |
a1c9 |
[urn:miriam:efo:EFO%3A0004541] |
Insulin |
[Insulin] |
a1c10 |
[urn:miriam:efo:EFO%3A0004541] |
a1c4 |
[urn:miriam:efo:EFO%3A0004541] |
rbc4 |
[erythrocyte] |
rbc8 |
[erythrocyte] |
rbc10 |
[erythrocyte] |
a1c2 |
[urn:miriam:efo:EFO%3A0004541] |
hba1c |
[urn:miriam:efo:EFO%3A0004541] |
Observables: none
BIOMD0000000621
@ v0.0.1
Palmer2014 - Effect of IL-1β-Blocking therapies in T2DM - Healthy Condition This is the model with healthy state initia…
DetailsRecent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment. link: http://identifiers.org/pubmed/24918743
Parameters:
Name | Description |
---|---|
taus = 0.5 |
Reaction: TigB =>, Rate Law: taus*TigB |
Kxg = 1.6E-5 |
Reaction: Glucose =>, Rate Law: Kxg*Glucose |
placebo_on = 0.0; kplacebo = 0.00137 |
Reaction: => IL1b, Rate Law: placebo_on*kplacebo |
il1b0 = 0.05; k1 = 0.2; il1bH = 0.05; placebo_on = 0.0; kplacebo = 0.00137; k2 = 0.0025 |
Reaction: => IL1b, Rate Law: piecewise((1-placebo_on)*k1*il1bH, time < 91, (1-placebo_on)*k2*(il1b0+kplacebo*time)) |
Tgl = 0.025405 |
Reaction: => Glucose, Rate Law: Tgl |
Kglucose = 2.92E-4; lambda = 0.743; Kin = 1.05; Ktr = 0.12 |
Reaction: rbc1 = (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1, Rate Law: (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1 |
IL1R = 3.743916136E-4; taus = 0.5; vs = 0.7; ks = 0.291008; kms = 0.021 |
Reaction: => TigB, Rate Law: taus*ks*(1-vs*IL1R/(kms+IL1R)) |
Gh = 9.0; vh = 4.0 |
Reaction: => Proinsulin; TigB, B, f, Glucose, Rate Law: f*(Glucose/Gh)^vh/(1+(Glucose/Gh)^vh)*TigB*B |
kab = 3.94; Vp = 48.0 |
Reaction: => Anakinra; Anakinrasc, Rate Law: kab*Anakinrasc/Vp |
k1 = 0.2; placebo_on = 0.0; k2 = 0.0025 |
Reaction: IL1b =>, Rate Law: piecewise((1-placebo_on)*k1*IL1b, time < 91, (1-placebo_on)*k2*IL1b) |
CL = 432.0; Vp = 48.0 |
Reaction: Anakinra =>, Rate Law: CL/Vp*Anakinra |
Kxgi = 1.0E-4 |
Reaction: Glucose => ; Insulin, Rate Law: Kxgi*Insulin*Glucose |
Kglucose = 2.92E-4; lambda = 0.743; Ktr = 0.12 |
Reaction: a1c5 = (Kglucose*Glucose^lambda*rbc5+Ktr*a1c4)-Ktr*a1c5, Rate Law: (Kglucose*Glucose^lambda*rbc5+Ktr*a1c4)-Ktr*a1c5 |
replication = 2.740001106E-4 |
Reaction: => B, Rate Law: replication*B |
Kxi = 0.05 |
Reaction: Proinsulin =>, Rate Law: 0.1*Kxi*Proinsulin |
vfg = 4.0; tauf = 0.5; kmf = 0.021; IL1R = 3.743916136E-4; kf = 0.00957754; kmfg = 9.0; xfg = 4.0; vf = 0.4 |
Reaction: => f; Glucose, Rate Law: tauf*kf*(1+vfg*Glucose^xfg/(kmfg^xfg+Glucose^xfg))*(1+vf*IL1R/(kmf+IL1R)) |
kab = 3.94 |
Reaction: Anakinrasc =>, Rate Law: kab*Anakinrasc |
apoptosis = 2.740002397E-4 |
Reaction: B =>, Rate Law: apoptosis*B |
tauf = 0.5 |
Reaction: f =>, Rate Law: tauf*f |
States:
Name | Description |
---|---|
Glucose |
[glucose] |
f |
[insulin secretion] |
a1c5 |
[urn:miriam:efo:EFO%3A0004541] |
a1c1 |
[urn:miriam:efo:EFO%3A0004541] |
a1c8 |
[urn:miriam:efo:EFO%3A0004541] |
rbc12 |
[erythrocyte] |
a1c3 |
[urn:miriam:efo:EFO%3A0004541] |
IL1b |
[Interleukin-1 beta] |
rbc6 |
[erythrocyte] |
B |
[pancreatic beta cell] |
a1c12 |
[urn:miriam:efo:EFO%3A0004541] |
rbc3 |
[erythrocyte] |
rbc1 |
[erythrocyte] |
Proinsulin |
[Insulin] |
Anakinra |
[Interleukin-1 receptor antagonist protein; pharmaceutical] |
a1c7 |
[urn:miriam:efo:EFO%3A0004541] |
a1c11 |
[urn:miriam:efo:EFO%3A0004541] |
rbc2 |
[erythrocyte] |
rbc11 |
[erythrocyte] |
a1c6 |
[urn:miriam:efo:EFO%3A0004541] |
rbc9 |
[erythrocyte] |
rbc5 |
[erythrocyte] |
TigB |
[insulin secretion] |
Anakinrasc |
[Interleukin-1 receptor antagonist protein] |
a1c9 |
[urn:miriam:efo:EFO%3A0004541] |
Insulin |
[Insulin] |
a1c2 |
[urn:miriam:efo:EFO%3A0004541] |
a1c10 |
[urn:miriam:efo:EFO%3A0004541] |
rbc10 |
[erythrocyte] |
a1c4 |
[urn:miriam:efo:EFO%3A0004541] |
rbc4 |
[erythrocyte] |
rbc7 |
[erythrocyte] |
rbc8 |
[erythrocyte] |
hba1c |
[urn:miriam:efo:EFO%3A0004541] |
Observables: none
BIOMD0000000608
@ v0.0.1
Palsson2013 - Fully-integration immune response model (FIRM)FIRM (The Fully-integrated Immune Response Modeling) is a hy…
DetailsBACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances. link: http://identifiers.org/pubmed/24074340
Parameters:
Name | Description |
---|---|
q72e = 1.0E-4 |
Reaction: x_3 => x_3 + x_35, Rate Law: q72e*x_3 |
volLymphT = 10.0; Rho21 = 100.0 |
Reaction: x_11 => x_12, Rate Law: Rho21*volLymphT |
w9 = 0.14; Mu9 = 0.04 |
Reaction: x_3 => x_1 + x_3, Rate Law: Mu9*w9*x_3 |
q72b = 5.0E-5 |
Reaction: x_8 => x_35 + x_8, Rate Law: q72b*x_8 |
Eta47 = 0.0024 |
Reaction: x_19 => x_16 + x_43, Rate Law: Eta47*x_19 |
volLymphT = 10.0; Mu22 = 0.9; c22 = 3000.0 |
Reaction: x_12 => x_12, Rate Law: x_12*Mu22/(c22+(x_12/volLymphT)^2) |
Mu8 = 0.1; volLung = 1000.0; v14 = 0.0; k3 = 0.11; K3s = 50.0 |
Reaction: x_3 + x_5 => x_3, Rate Law: Mu8*x_3*x_5*(x_5/x_3)^2/((x_5/x_3)^2+K3s^2)/x_3+x_3*x_5*volLung*v14/x_3/x_3+x_3*x_5*x_3/x_3*(x_5/x_3)^2*k3/((x_5/x_3)^2+K3s^2)/x_3 |
c12 = 500000.0; volLung = 1000.0; Delta12 = 0.4; cf12 = 150.0; fi12 = 2.333 |
Reaction: x_1 + x_33 + x_39 + x_4 + x_5 => x_1 + x_2 + x_33 + x_39 + x_4 + x_5, Rate Law: Delta12*x_1*x_39/(x_39+fi12*x_33+cf12*volLung)*x_4/(c12*volLung+x_4+x_5) |
Mu86 = 1.0; volLung = 1000.0; FACTOR = NaN; cf86 = 50.0; cF = 1000.0 |
Reaction: x_29 + x_8 => x_29 + x_8, Rate Law: x_8*x_8/volLung*x_29*volLung*Mu86/(cF+x_29)/(cf86+FACTOR) |
volBlood = 4500.0; Rho34 = 10.0 |
Reaction: => x_16 + x_43, Rate Law: Rho34*volBlood |
Delta11 = 0.36; volLung = 1000.0; cf11 = 100.0 |
Reaction: x_2 + x_35 => x_1 + x_35, Rate Law: Delta11*x_2*x_35/(x_35+cf11*volLung) |
Delta54 = 0.001; volLung = 1000.0 |
Reaction: x_26 + x_29 => x_27 + x_29, Rate Law: x_26*x_29*Delta54/volLung |
Delta43 = 2.4 |
Reaction: x_20 + x_47 + x_48 => x_21 + x_47 + x_48, Rate Law: Delta43*x_20*x_47/(x_47+x_48+1E-5) |
Alpha61 = 10.0 |
Reaction: x_2 + x_29 => x_2 + x_30, Rate Law: Alpha61*x_2*x_29/(100000+x_29) |
volLung = 1000.0; Mu15 = 0.02; c15 = 150000.0 |
Reaction: x_4 => x_4 + x_6, Rate Law: volLung*x_4*Mu15/(c15*volLung+x_4) |
Eta13 = 1.0 |
Reaction: x_2 =>, Rate Law: Eta13*x_2 |
Delta36 = 2.4 |
Reaction: x_16 + x_43 + x_44 => x_17 + x_44 + x_45, Rate Law: Delta36*x_16*x_44/(x_43+x_44+1E-5) |
c25 = 100000.0; volLung = 1000.0; Mu25 = 0.4 |
Reaction: x_2 + x_7 => x_2 + x_7, Rate Law: Mu25*x_7*x_2/(c25*volLung+x_2) |
Gamma31 = 0.3333 |
Reaction: x_9 => x_14, Rate Law: Gamma31*x_9 |
k3 = 0.11; K3s = 50.0 |
Reaction: x_3 + x_5 => x_5, Rate Law: k3*x_3/((x_5/x_3)^2+K3s^2)*(x_5/x_3)^2 |
ci12 = 1000.0; Deltai12 = 0.009 |
Reaction: x_1 + x_29 => x_2 + x_29, Rate Law: Deltai12*x_1*x_29/(ci12+x_29) |
volLung = 1000.0; MuI = 9.0; cF = 1000.0; cI = 50.0; Gamma17 = 0.2 |
Reaction: x_29 + x_6 => x_29, Rate Law: x_6/volLung*x_29*volLung*MuI*Gamma17/(cF+x_29)/(cI+x_29/(cF+x_29)) |
Gamma32 = 0.9 |
Reaction: x_14 => x_15, Rate Law: Gamma32*x_14 |
Eta16 = 0.01 |
Reaction: x_6 =>, Rate Law: Eta16*x_6 |
Mu8 = 0.1 |
Reaction: x_3 + x_5 => x_3 + x_5, Rate Law: Mu8*x_3*x_5/x_3 |
volLung = 1000.0; c4 = 0.15; Alpha4 = 0.5 |
Reaction: x_3 + x_5 + x_8 => x_3 + x_8, Rate Law: x_3*x_5*x_3/x_3*x_8*Alpha4/x_3/(x_8/x_3+c4*volLung)/x_3 |
fi27 = 4.1; Delta27 = 0.1; fii27 = 4.8; volLung = 1000.0; cf27 = 30.0 |
Reaction: x_33 + x_35 + x_36 + x_38 + x_7 => x_33 + x_35 + x_36 + x_38 + x_8, Rate Law: x_36*x_38*Delta27/volLung*x_7/(x_36+fi27*x_33+fii27*x_35+cf27*volLung) |
Alpha5 = 1.25E-7; volLung = 1000.0 |
Reaction: x_2 + x_4 => x_2, Rate Law: x_2*x_4*Alpha5/volLung |
Rho19 = 1000.0; volLymphT = 10.0 |
Reaction: => x_11, Rate Law: Rho19*volLymphT |
Delta38 = 2.4 |
Reaction: x_18 + x_47 + x_48 => x_19 + x_48, Rate Law: Delta38*x_18*x_47/(x_47+x_48+1E-5) |
q68a = 0.0029 |
Reaction: x_7 => x_33 + x_7, Rate Law: q68a*x_7 |
Eta53 = 0.02 |
Reaction: x_26 =>, Rate Law: Eta53*x_26 |
volLung = 1000.0; Rho21 = 100.0; MuI = 9.0; cF = 1000.0; cI = 50.0; Rho50 = 100.0 |
Reaction: x_29 => x_29 + x_6, Rate Law: x_29*volLung*Rho21*MuI/(cF+x_29)/(cI+x_29/(cF+x_29))+x_29*volLung*Rho50*MuI/(cF+x_29)/(cI+x_29/(cF+x_29)) |
volLung = 1000.0; k7 = 1.0E-7 |
Reaction: x_4 + x_6 => x_6, Rate Law: x_4*x_6*k7/volLung |
Eta10 = 0.05 |
Reaction: x_1 =>, Rate Law: Eta10*x_1 |
c24 = 15000.0; volLung = 1000.0; Gamma24 = 0.9 |
Reaction: x_13 + x_2 => x_2 + x_7, Rate Law: Gamma24*x_13*x_2/(c24*volLung+x_2) |
volLung = 1000.0; v14 = 0.0; k3 = 0.11; K3s = 50.0 |
Reaction: x_3 + x_5 => x_3 + x_4 + x_5, Rate Law: volLung*x_5*v14/x_3+k3*x_3*1/((x_5/x_3)^2+K3s^2)*x_5/x_3*(x_5/x_3)^2 |
volLymphT = 10.0; Delta21 = 1.0E-4 |
Reaction: x_10 + x_11 => x_10 + x_12, Rate Law: x_10*x_11*Delta21/volLymphT |
Mui9 = 125000.0; volLung = 1000.0; MuI = 9.0; cF = 1000.0; cI = 50.0 |
Reaction: x_29 => x_1 + x_29, Rate Law: x_29*volLung*Mui9*MuI/(cF+x_29)/(cI+x_29/(cF+x_29)) |
K93 = 100.0; Beta90 = 1000.0 |
Reaction: x_52 => x_4 + x_51, Rate Law: Beta90*x_52/K93 |
q68b = 0.0218 |
Reaction: x_9 => x_33 + x_9, Rate Law: q68b*x_9 |
Gamma40 = 0.9 |
Reaction: x_18 + x_47 => x_20, Rate Law: Gamma40*x_18 |
q72d = 1.0E-4 |
Reaction: x_7 => x_35 + x_7, Rate Law: q72d*x_7 |
volLung = 1000.0; Rho80 = 700.0; c80 = 5000.0; ci80 = 50.0 |
Reaction: x_38 + x_4 + x_5 => x_38 + x_39 + x_4 + x_5, Rate Law: Rho80*volLung*x_38*x_4/(ci80*volLung+x_38)/(c80*volLung+x_4+x_5)+Rho80*volLung*x_38*x_5/(ci80*volLung+x_38)/(c80*volLung+x_4+x_5) |
volLung = 1000.0; Rho9 = 5000.0 |
Reaction: => x_1, Rate Law: Rho9*volLung |
k2 = 0.4; c2 = 1000000.0; volLung = 1000.0 |
Reaction: x_1 + x_4 => x_3, Rate Law: k2*x_1*x_4/(x_4+c2*volLung) |
volLung = 1000.0; Beta90 = 1000.0 |
Reaction: x_4 + x_51 => x_52, Rate Law: Beta90*x_4*x_51/volLung |
Deltai27 = 0.001 |
Reaction: x_2 + x_30 + x_7 => x_2 + x_30 + x_7 + x_8, Rate Law: Deltai27*x_2*x_30*x_7/(10000000+x_30) |
v67 = 0.0; volLung = 1000.0 |
Reaction: x_32 =>, Rate Law: v67*volLung |
Eta28 = 0.3333 |
Reaction: x_8 =>, Rate Law: Eta28*x_8 |
Mu9 = 0.04 |
Reaction: x_2 => x_1 + x_2, Rate Law: Mu9*x_2 |
volLung = 1000.0; cf29 = 2.0; Delta29 = 0.05; fi29 = 0.12 |
Reaction: x_33 + x_39 + x_7 => x_33 + x_39 + x_9, Rate Law: Delta29*x_7*x_33/(x_33+fi29*x_39+cf29*volLung) |
volLung = 1000.0; v30 = 0.0 |
Reaction: x_9 =>, Rate Law: v30*volLung |
volLung = 1000.0; c17 = 10000.0; Gamma17 = 0.2 |
Reaction: x_4 + x_6 => x_10 + x_4, Rate Law: Gamma17*x_6*x_4/(c17*volLung+x_4) |
volLung = 1000.0; c52 = 15000.0; Gamma52 = 0.9 |
Reaction: x_2 + x_25 => x_2 + x_26, Rate Law: Gamma52*x_25*x_2/(c52*volLung+x_2) |
Eta33 = 0.3333 |
Reaction: x_15 =>, Rate Law: Eta33*x_15 |
Gamma23 = 0.9 |
Reaction: x_12 => x_13, Rate Law: Gamma23*x_12 |
volLung = 1000.0; Mu55 = 1.0; FACTOR = NaN; cF = 1000.0 |
Reaction: x_27 + x_29 + x_8 => x_27 + x_29 + x_8, Rate Law: x_8*x_27/volLung*x_29*volLung*Mu55/(cF+x_29)/(cF+FACTOR) |
ci72 = 0.05; volLung = 1000.0; c72 = 51.0; q72a = 0.006 |
Reaction: x_2 + x_35 + x_39 => x_2 + x_35 + x_39, Rate Law: c72*q72a*x_2*1/(x_35+ci72*x_39+c72*volLung) |
Eta26 = 0.3333 |
Reaction: x_7 =>, Rate Law: Eta26*x_7 |
v41 = 0.0; volLymphB = 150.0 |
Reaction: x_20 => x_18 + x_47, Rate Law: v41*volLymphB |
volLung = 1000.0; cii80 = 100000.0; q80 = 0.02 |
Reaction: x_2 + x_8 => x_2 + x_39 + x_8, Rate Law: volLung*x_2*x_8*q80/(cii80*volLung+x_2) |
States:
Name | Description |
---|---|
x 6 |
x_6 |
x 3 |
x_3 |
x 33 |
x_33 |
x 29 |
x_29 |
x 22 |
x_22 |
x 11 |
x_11 |
x 17 |
x_17 |
x 5 |
x_5 |
x 15 |
x_15 |
x 18 |
x_18 |
x 32 |
x_32 |
x 14 |
x_14 |
x 16 |
x_16 |
x 35 |
x_35 |
x 1 |
x_1 |
x 8 |
x_8 |
x 21 |
x_21 |
x 7 |
x_7 |
x 4 |
x_4 |
x 19 |
x_19 |
x 9 |
x_9 |
x 10 |
x_10 |
x 12 |
x_12 |
x 27 |
x_27 |
x 20 |
x_20 |
x 2 |
x_2 |
x 26 |
x_26 |
x 13 |
x_13 |
Observables: none
BIOMD0000000954
@ v0.0.1
Cells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the res…
DetailsCells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the restriction point (R-point), cells become irreversibly committed to the completion of the cell cycle independent of mitogen. The mechanism involving hyper-phosphorylation of retinoblastoma (Rb) and activation of transcription factor E2F is linked to the R-point passage. However, stress stimuli trigger exit from the cell cycle back to the mitogen-sensitive quiescent state after Rb hyper-phosphorylation but only until APC/CCdh1 inactivation. In this study, we developed a mathematical model to investigate the reversible transition between quiescence and proliferation in mammalian cells with respect to mitogen and stress signals. The model integrates the current mechanistic knowledge and accounts for the recent experimental observations with cells exiting quiescence and proliferating cells. We show that Cyclin E:Cdk2 couples Rb-E2F and APC/CCdh1 bistable switches and temporally segregates the R-point and the G1/S transition. A redox-dependent mutual antagonism between APC/CCdh1 and its inhibitor Emi1 makes the inactivation of APC/CCdh1 bistable. We show that the levels of Cdk inhibitor (CKI) and mitogen control the reversible transition between quiescence and proliferation. Further, we propose that shifting of the mitogen-induced transcriptional program to G2-phase in proliferating cells might result in an intermediate Cdk2 activity at the mitotic exit and in the immediate inactivation of APC/CCdh1. Our study builds a coherent framework and generates hypotheses that can be further explored by experiments. link: http://identifiers.org/pubmed/29856829
Parameters: none
States: none
Observables: none
MODEL8685104549
@ v0.0.1
This a model from the article: A mathematical model of the electrophysiological alterations in rat ventricular myocyte…
DetailsOur mathematical model of the rat ventricular myocyte (Pandit et al., 2001) was utilized to explore the ionic mechanism(s) that underlie the altered electrophysiological characteristics associated with the short-term model of streptozotocin-induced, type-I diabetes. The simulations show that the observed reductions in the Ca(2+)-independent transient outward K(+) current (I(t)) and the steady-state outward K(+) current (I(ss)), along with slowed inactivation of the L-type Ca(2+) current (I(CaL)), can result in the prolongation of the action potential duration, a well-known experimental finding. In addition, the model demonstrates that the slowed reactivation kinetics of I(t) in diabetic myocytes can account for the more pronounced rate-dependent action potential duration prolongation in diabetes, and that a decrease in the electrogenic Na(+)-K(+) pump current (I(NaK)) results in a small depolarization in the resting membrane potential (V(rest)). This depolarization reduces the availability of the Na(+) channels (I(Na)), thereby resulting in a slower upstroke (dV/dt(max)) of the diabetic action potential. Additional simulations suggest that a reduction in the magnitude of I(CaL), in combination with impaired sarcoplasmic reticulum uptake can lead to a decreased sarcoplasmic reticulum Ca(2+) load. These factors contribute to characteristic abnormal Ca(2+) homeostasis (reduced peak systolic value and rate of decay) in myocytes from diabetic animals. In combination, these simulation results provide novel information and integrative insights concerning plausible ionic mechanisms for the observed changes in cardiac repolarization and excitation-contraction coupling in rat ventricular myocytes in the setting of streptozotocin-induced, type-I diabetes. link: http://identifiers.org/pubmed/12547767
Parameters: none
States: none
Observables: none
MODEL1708210000
@ v0.0.1
Hass2017-PanRTK model for single cell lineThe model structure comprises heterodimerization and receptor trafficking as d…
DetailsTargeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo. link: http://identifiers.org/pubmed/28944080
Parameters: none
States: none
Observables: none
BIOMD0000000359
@ v0.0.1
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…
DetailsWe have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested. link: http://identifiers.org/pubmed/11985578
Parameters:
Name | Description |
---|---|
k1=6.0; k2=0.02 |
Reaction: VIIa_TF_Xa + TFPI => VIIa_TF_Xa_TFPI, Rate Law: compartment*(k1*VIIa_TF_Xa*TFPI-k2*VIIa_TF_Xa_TFPI) |
k2=0.02; k1=0.054 |
Reaction: Xa + TFPI => Xa_TFPI, Rate Law: compartment*(k1*Xa*TFPI-k2*Xa_TFPI) |
k1=420.0 |
Reaction: VIIa_TF_X => VIIa_TF_Xa, Rate Law: compartment*k1*VIIa_TF_X |
k1=0.44; k2=0.0 |
Reaction: VIIa_TF + Xa_TFPI => Xa_TFPI_VIIa_TF, Rate Law: compartment*(k1*VIIa_TF*Xa_TFPI-k2*Xa_TFPI_VIIa_TF) |
k2=770.0; k1=5.0 |
Reaction: X + VIIa_TF => VIIa_TF_X, Rate Law: compartment*(k1*X*VIIa_TF-k2*VIIa_TF_X) |
k2=5.0; k1=770.0 |
Reaction: VIIa_TF_Xa => Xa + VIIa_TF, Rate Law: compartment*(k1*VIIa_TF_Xa-k2*Xa*VIIa_TF) |
k1=0.0; k2=0.0 |
Reaction: VIIa_TF_Xa_TFPI => Xa_TFPI_VIIa_TF, Rate Law: compartment*(k1*VIIa_TF_Xa_TFPI-k2*Xa_TFPI_VIIa_TF) |
k1=20.0; k2=0.0 |
Reaction: VIIa_TF_X + Xa_TFPI => X + VIIa_TF_Xa_TFPI, Rate Law: compartment*(k1*VIIa_TF_X*Xa_TFPI-k2*X*VIIa_TF_Xa_TFPI) |
States:
Name | Description |
---|---|
Xa TFPI |
[Tissue factor pathway inhibitor; Coagulation factor X] |
Xa TFPI VIIa TF |
[Tissue factor; Coagulation factor VII; Tissue factor pathway inhibitor; Coagulation factor X] |
VIIa TF X |
[Coagulation factor X; Tissue factor; Coagulation factor VII] |
X |
[Coagulation factor X] |
TFPI |
[Tissue factor pathway inhibitor] |
VIIa TF Xa TFPI |
[Tissue factor; Coagulation factor VII; Coagulation factor X; Tissue factor pathway inhibitor] |
VIIa TF |
[Tissue factor; Coagulation factor VII] |
Xa |
[Coagulation factor X] |
VIIa TF Xa |
[Coagulation factor X; Tissue factor; Coagulation factor VII] |
Observables: none
BIOMD0000000740
@ v0.0.1
Full and reduced mathematical model of blood coagulation focusing on fibrin formation and the response to varied TF and…
DetailsAnalysis of complex time-dependent biological networks is an important challenge in the current postgenomic era. We propose a middle-out approach for decomposition and analysis of complex time-dependent biological networks based on: 1), creation of a detailed mechanism-driven mathematical model of the network; 2), network response decomposition into several physiologically relevant subtasks; and 3), subsequent decomposition of the model, with the help of task-oriented necessity and sensitivity analysis into several modules that each control a single specific subtask, which is followed by further simplification employing temporal hierarchy reduction. The technique is tested and illustrated by studying blood coagulation. Five subtasks (threshold, triggering, control by blood flow velocity, spatial propagation, and localization), together with responsible modules, can be identified for the coagulation network. We show that the task of coagulation triggering is completely regulated by a two-step pathway containing a single positive feedback of factor V activation by thrombin. These theoretical predictions are experimentally confirmed by studies of fibrin generation in normal, factor V-, and factor VIII-deficient plasmas. The function of the factor V-dependent feedback is to minimize temporal and parametrical intervals of fibrin clot instability. We speculate that this pathway serves to lessen possibility of fibrin clot disruption by flow and subsequent thromboembolism. link: http://identifiers.org/pubmed/20441738
Parameters:
Name | Description |
---|---|
k_01 = 1.1; k03 = 0.4; k02 = 0.0014; k01 = 4.2 |
Reaction: VII_TF = ((k01*VII*TF-k_01*VII_TF)-k02*VII_TF*IIa_F)-k03*VII_TF*Xa_F, Rate Law: ((k01*VII*TF-k_01*VII_TF)-k02*VII_TF*IIa_F)-k03*VII_TF*Xa_F |
k15 = 54.0; K15 = 147.0; h14 = 0.35 |
Reaction: VIIIa = k15*VIII*IIa_F/(K15+IIa_F)-h14*VIIIa, Rate Law: k15*VIII*IIa_F/(K15+IIa_F)-h14*VIIIa |
h17 = 2.6E-5; h20 = 1.4E-4; k17 = 0.03; h18 = 6.0E-6; h16 = 1.9E-5; h19 = 0.0054 |
Reaction: XIa = k17*Phospholipid*XI*IIa_F-(h16*AT_III+h17*alpha2_antiplasmin+h18*alpha1_antitrypsin+h19*ProteinC_Inhibitor+h20*C1_inhibitor)*XIa, Rate Law: k17*Phospholipid*XI*IIa_F-(h16*AT_III+h17*alpha2_antiplasmin+h18*alpha1_antitrypsin+h19*ProteinC_Inhibitor+h20*C1_inhibitor)*XIa |
K26 = 470.0; n25 = 16000.0; K25 = 320.0 |
Reaction: X_B = X*Phospholipid*n25/(K25*(1+X/K25+II/K26)), Rate Law: missing |
K05 = 200.0; k04 = 15.0; K04 = 210.0; k05 = 5.8 |
Reaction: IX = (-k04/K04)*IX*VIIa_TF_F-k05*IX*XIa/(K05+IX), Rate Law: (-k04/K04)*IX*VIIa_TF_F-k05*IX*XIa/(K05+IX) |
k07 = 0.06; k08 = 6350.0; k06 = 435.0; K06 = 238.0; K07 = 230.0; K09 = 278.0 |
Reaction: X = ((-k06/K06)*X*VIIa_TF_F-k07*IXa_B_F*X_B/(Phospholipid*K07))-k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08), Rate Law: ((-k06/K06)*X*VIIa_TF_F-k07*IXa_B_F*X_B/(Phospholipid*K07))-k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08) |
h21 = 6.0E-6; h22 = 6.0E-6; h23 = 7.0E-7; k18 = 2.0E-5; h24 = 3.9E-4 |
Reaction: APC = k18*PC*IIa_F-(h21*alpha2_macroglobulin+h22*alpha2_antiplasmin+h23*alpha1_antitrypsin+h24*ProteinC_Inhibitor)*APC, Rate Law: k18*PC*IIa_F-(h21*alpha2_macroglobulin+h22*alpha2_antiplasmin+h23*alpha1_antitrypsin+h24*ProteinC_Inhibitor)*APC |
K20 = 2.57; n20 = 260.0 |
Reaction: IXa_B_F = IXa*Phospholipid*n20/(K20+IXa), Rate Law: missing |
k_01 = 1.1; k02 = 0.0014; k01 = 4.2 |
Reaction: VII = (-(k01*VII*TF-k_01*VII_TF))-k02*VII*IIa_F, Rate Law: (-(k01*VII*TF-k_01*VII_TF))-k02*VII*IIa_F |
k11 = 0.052; k_11 = 0.02; h02 = 6.0 |
Reaction: TFPI = (-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-h02*Xa_VIIa_TF*TFPI, Rate Law: (-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-h02*Xa_VIIa_TF*TFPI |
K21 = 1.5; K10 = 1655.0; K22 = 150.0; n21 = 750.0 |
Reaction: VIIIa_B_F = VIIIa*Phospholipid*n21/((K21+VIIIa)*(1+X_B/(Phospholipid*K10)*(1+ProteinS_inhibitor/K22))), Rate Law: missing |
k_01 = 1.1; h01 = 0.44; k03 = 0.4; k02 = 0.0014; k01 = 4.2; h02 = 6.0 |
Reaction: VIIa_TF = (((k01*VIIa*TF-k_01*VIIa_TF_F)+k02*VII_TF*IIa_F+k03*VII_TF*Xa_F)-h01*VIIa_TF_F*Xa_TFPI)-h02*Xa_VIIa_TF*TFPI, Rate Law: (((k01*VIIa*TF-k_01*VIIa_TF_F)+k02*VII_TF*IIa_F+k03*VII_TF*Xa_F)-h01*VIIa_TF_F*Xa_TFPI)-h02*Xa_VIIa_TF*TFPI |
k15 = 54.0; K15 = 147.0 |
Reaction: VIII = (-k15)*VIII*IIa_F/(K15+IIa_F), Rate Law: (-k15)*VIII*IIa_F/(K15+IIa_F) |
n27 = 2700.0; K27 = 2.9 |
Reaction: Va_B = Va*Phospholipid*n27/(K27+Va), Rate Law: missing |
h03 = 8.2E-6; h08 = 2.2E-5; h04 = 1.5E-4; h09 = 4.1E-4; h16 = 1.9E-5 |
Reaction: AT_III = (-(h03*IXa+h04*Xa_F+h08*Xa_Va_b+h09*IIa_F+h16*XIa))*AT_III, Rate Law: (-(h03*IXa+h04*Xa_F+h08*Xa_Va_b+h09*IIa_F+h16*XIa))*AT_III |
k_01 = 1.1; k01 = 4.2 |
Reaction: TF = (-(k01*VIIa*TF-k_01*VIIa_TF_F))-(k01*VII*TF-k_01*VII_TF), Rate Law: (-(k01*VIIa*TF-k_01*VIIa_TF_F))-(k01*VII*TF-k_01*VII_TF) |
K23 = 0.118; K24 = 200.0 |
Reaction: Xa_Va_b = Xa*Va_B/(K23*(1+ProteinS_inhibitor/K24+Xa/K23)+Va_B), Rate Law: missing |
k17 = 0.03 |
Reaction: XI = (-k17)*Phospholipid*XI*IIa_F, Rate Law: (-k17)*Phospholipid*XI*IIa_F |
K14 = 7200.0 |
Reaction: IIa_F = IIa/(1+(fibrin+fibrinogen)/K14), Rate Law: missing |
k11 = 0.052; h08 = 2.2E-5; h07 = 0.0012; k_11 = 0.02; K06 = 238.0; K07 = 230.0; h05 = 4.0E-5; k07 = 0.06; k08 = 6350.0; h04 = 1.5E-4; k06 = 435.0; h06 = 1.36E-5; K09 = 278.0 |
Reaction: Xa = (((k06/K06*X*VIIa_TF_F+k07*IXa_B_F*X_B/(Phospholipid*K07)+k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08))-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-(h04*AT_III+h05*alpha2_macroglobulin+h06*alpha1_antitrypsin+h07*ProteinC_Inhibitor)*Xa_F)-h08*AT_III*Xa_Va_b, Rate Law: (((k06/K06*X*VIIa_TF_F+k07*IXa_B_F*X_B/(Phospholipid*K07)+k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08))-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-(h04*AT_III+h05*alpha2_macroglobulin+h06*alpha1_antitrypsin+h07*ProteinC_Inhibitor)*Xa_F)-h08*AT_III*Xa_Va_b |
k11 = 0.052; h01 = 0.44; k_11 = 0.02 |
Reaction: Xa_TFPI = (k11*Xa_F*TFPI-k_11*Xa_TFPI)-h01*VIIa_TF_F*Xa_TFPI, Rate Law: (k11*Xa_F*TFPI-k_11*Xa_TFPI)-h01*VIIa_TF_F*Xa_TFPI |
k16 = 14.0; K16 = 71.7 |
Reaction: V = (-k16)*V*IIa_F/(K16+IIa_F), Rate Law: (-k16)*V*IIa_F/(K16+IIa_F) |
K06 = 238.0; K04 = 210.0 |
Reaction: VIIa_TF_F = VIIa_TF/(1+IX/K04+X/K06), Rate Law: missing |
k13 = 1.44; k12 = 45.0 |
Reaction: II = (-k12)*Phospholipid*Xa_F*II-k13*Xa_Va_b*II_B/Phospholipid, Rate Law: (-k12)*Phospholipid*Xa_F*II-k13*Xa_Va_b*II_B/Phospholipid |
k18 = 2.0E-5 |
Reaction: PC = (-k18)*PC*IIa_F, Rate Law: (-k18)*PC*IIa_F |
k14 = 5040.0; K14 = 7200.0 |
Reaction: fibrin = k14/K14*fibrinogen*IIa_F, Rate Law: k14/K14*fibrinogen*IIa_F |
k06 = 435.0; k_19 = 770.0; K06 = 238.0 |
Reaction: Xa_VIIa_TF = k06/(K06*k_19)*X*VIIa_TF_F, Rate Law: missing |
h03 = 8.2E-6; K05 = 200.0; k04 = 15.0; K04 = 210.0; k05 = 5.8 |
Reaction: IXa = (k04/K04*IX*VIIa_TF_F+k05*IX*XIa/(K05+IX))-h03*Xa_TFPI*IXa, Rate Law: (k04/K04*IX*VIIa_TF_F+k05*IX*XIa/(K05+IX))-h03*Xa_TFPI*IXa |
k16 = 14.0; K16 = 71.7; h15 = 7.7 |
Reaction: Va = k16*V*IIa_F/(K16+IIa_F)-h15*APC*Va_B_F, Rate Law: k16*V*IIa_F/(K16+IIa_F)-h15*APC*Va_B_F |
k13 = 1.44; h10 = 1.0E-4; h12 = 3.7E-4; h11 = 3.0E-6; h09 = 4.1E-4; k12 = 45.0; h13 = 6.3E-5 |
Reaction: IIa = (k12*Phospholipid*Xa_F*II+k13*Xa_Va_b*II_B/Phospholipid)-(h09*AT_III+h10*alpha2_macroglobulin+h11*alpha1_antitrypsin+h12*ProteinC_Inhibitor+h13*heparin_cofactor2)*IIa_F, Rate Law: (k12*Phospholipid*Xa_F*II+k13*Xa_Va_b*II_B/Phospholipid)-(h09*AT_III+h10*alpha2_macroglobulin+h11*alpha1_antitrypsin+h12*ProteinC_Inhibitor+h13*heparin_cofactor2)*IIa_F |
States:
Name | Description |
---|---|
fibrin |
[Fibrin] |
VIII |
[Coagulation Factor VIII] |
TFPI |
[TFPI Gene] |
X B |
[Coagulation Factor X] |
II B |
[Thrombin] |
V |
[Coagulation Factor V] |
Xa VIIa TF |
[Coagulation Factor VII; Coagulation Factor X; Tissue Factor; Coagulation Factor VII; Coagulation Factor X] |
Xa |
[Coagulation Factor X] |
Va B |
[Coagulation Factor V] |
VIIIa B F |
[Coagulation Factor VIII] |
PC |
[Protein C] |
VII TF |
[Coagulation Factor VII; Tissue Factor] |
TF |
[Tissue Factor] |
XIa |
[121660] |
X |
[Coagulation Factor X] |
IIa F |
[Thrombin] |
VIIIa |
[Coagulation Factor VIII] |
AT III |
[Therapeutic Human Antithrombin-III] |
Va |
[Coagulation Factor V] |
IIa |
[Thrombin] |
Xa TFPI |
[Coagulation Factor X; TFPI Gene; Coagulation Factor X] |
VIIa |
[Coagulation Factor VII] |
Xa Va b |
[Coagulation Factor V; Coagulation Factor X] |
fibrinogen |
[Fibrinogen] |
XI |
[121660] |
APC |
[Protein C] |
VIIa TF F |
[Tissue Factor; Coagulation Factor VII] |
VIIa TF |
[Coagulation Factor VII; Tissue Factor] |
IXa |
[Coagulation Factor IX] |
Xa F |
[Coagulation Factor X] |
Va B F |
[Coagulation Factor V] |
VII |
[Coagulation Factor VII] |
II |
[Thrombin] |
IX |
[Coagulation Factor IX] |
IXa B F |
[Coagulation Factor IX] |
Observables: none
BIOMD0000000666
@ v0.0.1
Pappalardo2016 - PI3K/AKT and MAPK Signaling Pathways in Melanoma CancerThis model is described in the article: [Comput…
DetailsMalignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive. link: http://identifiers.org/pubmed/27015094
Parameters:
Name | Description |
---|---|
Kcat=8.8912; km=3496490.0 |
Reaction: species_10 => species_11; species_26, Rate Law: compartment_0*Kcat*species_26*species_10/(km+species_10) |
km=62464.6; Kcat=0.884096 |
Reaction: species_7 => species_6; species_4, Rate Law: compartment_0*Kcat*species_4*species_7/(km+species_7) |
Kcat=10.6737; km=184912.0 |
Reaction: species_15 => species_14; species_0, Rate Law: compartment_0*Kcat*species_0*species_15/(km+species_15) |
Kcat=3.19E13; km=3200.0 |
Reaction: bRafMutated => bRafMutatedInactive; Dabrafenib, Rate Law: compartment_0*Kcat*Dabrafenib*bRafMutated/(km+bRafMutated) |
Kcat=0.0213697; km=763523.0 |
Reaction: species_2 => species_3; species_10, Rate Law: compartment_0*Kcat*species_10*species_2/(km+species_2) |
Kcat=0.126329; km=1061.71 |
Reaction: species_6 => species_7; species_27, Rate Law: compartment_0*Kcat*species_27*species_6/(km+species_6) |
Kcat=2.83243; km=518753.0 |
Reaction: species_8 => species_9; species_26, Rate Law: compartment_0*Kcat*species_26*species_8/(km+species_8) |
v=100.0 |
Reaction: probRafMutated => bRafMutated, Rate Law: compartment_0*v |
km=896896.0; Kcat=1611.97 |
Reaction: species_2 => species_3; species_12, Rate Law: compartment_0*Kcat*species_12*species_2/(km+species_2) |
Kcat=185.759; km=4768350.0 |
Reaction: species_9 => species_8; species_6, Rate Law: compartment_0*Kcat*species_6*species_9/(km+species_9) |
k1=2.18503E-5; k2=0.121008 |
Reaction: species_25 + species_1 => species_0, Rate Law: compartment_0*(k1*species_25*species_1-k2*species_0) |
km=1432410.0; Kcat=1509.36 |
Reaction: species_4 => species_5; species_28, Rate Law: compartment_0*Kcat*species_28*species_4/(km+species_4) |
k1=1.92527E-5 |
Reaction: Dabrafenib =>, Rate Law: compartment_0*k1*Dabrafenib |
Kcat=32.344; km=35954.3 |
Reaction: species_5 => species_4; species_2, Rate Law: compartment_0*Kcat*species_2*species_5/(km+species_5) |
Kcat=9.85367; km=1007340.0 |
Reaction: species_11 => species_10; species_8, Rate Law: compartment_0*Kcat*species_8*species_11/(km+species_11) |
k1=2.5 |
Reaction: species_19 => species_20, Rate Law: compartment_0*k1*species_19 |
k1=0.2 |
Reaction: species_0 =>, Rate Law: compartment_0*k1*species_0 |
k1=0.005 |
Reaction: species_12 => species_13, Rate Law: compartment_0*k1*species_12 |
km=6086070.0; Kcat=694.731 |
Reaction: species_20 => species_19; species_0, Rate Law: compartment_0*Kcat*species_0*species_20/(km+species_20) |
Kcat=15.1212; km=119355.0 |
Reaction: species_6 => species_7; species_16, Rate Law: compartment_0*Kcat*species_16*species_6/(km+species_6) |
Kcat=0.0566279; km=653951.0 |
Reaction: species_17 => species_16; species_14, Rate Law: compartment_0*Kcat*species_14*species_17/(km+species_17) |
k1=0.00125 |
Reaction: species_1 =>, Rate Law: compartment_0*k1*species_1 |
Kcat=0.0771067; km=272056.0 |
Reaction: species_15 => species_14; species_4, Rate Law: compartment_0*Kcat*species_4*species_15/(km+species_15) |
States:
Name | Description |
---|---|
species 9 |
[Dual specificity mitogen-activated protein kinase kinase 1; K04368; Protein kinase byr1] |
species 1 |
[Receptor Tyrosine Kinase; Protein sevenless] |
species 20 |
[Rap guanine nucleotide exchange factor 1; K06277] |
species 4 |
[K07829; Ras-related protein R-Ras] |
species 16 |
[AKT kinase; Putative serine/threonine-protein kinase-like protein CCR3] |
PIP3Active |
[Phosphatidylinositol-3,4,5-trisphosphate] |
PDK1Inactive |
[[Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 1, mitochondrial; Probable [pyruvate dehydrogenase (acetyl-transferring)] kinase, mitochondrial; K12077] |
IRS1Active |
[K16172; Insulin receptor substrate 1] |
species 0 |
[Receptor Tyrosine Kinase; Protein sevenless] |
species 21 |
[Ras-related protein Rap-1A; K04353] |
species 8 |
[Dual specificity mitogen-activated protein kinase kinase 1; Protein kinase byr1; K04368] |
species 17 |
[Putative serine/threonine-protein kinase-like protein CCR3; AKT kinase] |
species 12 |
[ribosomal protein S6 kinase alpha; Putative serine/threonine-protein kinase-like protein CCR3] |
species 25 |
[Growth Factor] |
species 5 |
[K07829; Ras-related protein R-Ras] |
species 15 |
[K00914; Phosphatidylinositol 3-kinase age-1] |
S6K1Active |
[Ribosomal protein S6 kinase beta-1; Putative serine/threonine-protein kinase-like protein CCR3; K04688] |
Dabrafenib |
[D10064; dabrafenib] |
species 2 |
[K03099; Son of sevenless homolog 1] |
species 6 |
[Putative serine/threonine-protein kinase-like protein CCR3; RAF proto-oncogene serine/threonine-protein kinase; K04366] |
mTORC1Inactive |
[Serine/threonine-protein kinase mTOR; TORC1 complex] |
species 19 |
[Rap guanine nucleotide exchange factor 1; K06277] |
species 10 |
[Mitogen-activated protein kinase 1; K05111] |
S6K1Inactive |
[Ribosomal protein S6 kinase beta-1; Putative serine/threonine-protein kinase-like protein CCR3; K04688] |
species 11 |
[Mitogen-activated protein kinase 1; K05111] |
bRafMutatedInactive |
[Putative serine/threonine-protein kinase-like protein CCR3; K04365; Serine/threonine-protein kinase B-raf; BRAF Gene Mutation] |
species 30 |
proRTK |
IRS1Inactive |
[Insulin receptor substrate 1; K16172] |
mTORC1Active |
[Serine/threonine-protein kinase mTOR; TORC1 complex] |
species 14 |
[Phosphatidylinositol 3-kinase age-1; K00914] |
species 22 |
[K04353; Ras-related protein Rap-1A] |
PDK1Active |
[K12077; [Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 1, mitochondrial; Probable [pyruvate dehydrogenase (acetyl-transferring)] kinase, mitochondrial] |
bRafMutated |
[K04365; Serine/threonine-protein kinase B-raf; Putative serine/threonine-protein kinase-like protein CCR3; BRAF Gene Mutation] |
species 3 |
[Son of sevenless homolog 1; K03099] |
PIP3Inactive |
[Phosphatidylinositol-3,4,5-trisphosphate] |
species 7 |
[RAF proto-oncogene serine/threonine-protein kinase; K04366; Putative serine/threonine-protein kinase-like protein CCR3] |
probRafMutated |
probRafMutated |
species 13 |
[Putative serine/threonine-protein kinase-like protein CCR3; ribosomal protein S6 kinase alpha] |
Observables: none
MODEL1909300004
@ v0.0.1
It's an experimental + mathematical paper explaining probable targets for Cetixumab resistance in colorectal cancer.
DetailsCetuximab (CTX), a monoclonal antibody against epidermal growth factor receptor, is being widely used for colorectal cancer (CRC) with wild-type (WT) KRAS. However, its responsiveness is still very limited and WT KRAS is not enough to indicate such responsiveness. Here, by analyzing the gene expression data of CRC patients treated with CTX monotherapy, we have identified DUSP4, ETV5, GNB5, NT5E, and PHLDA1 as potential targets to overcome CTX resistance. We found that knockdown of any of these five genes can increase CTX sensitivity in KRAS WT cells. Interestingly, we further found that GNB5 knockdown can increase CTX sensitivity even for KRAS mutant cells. We unraveled that GNB5 overexpression contributes to CTX resistance by modulating the Akt signaling pathway from experiments and mathematical simulation. Overall, these results indicate that GNB5 might be a promising target for combination therapy with CTX irrespective of KRAS mutation. link: http://identifiers.org/pubmed/30719834
Parameters: none
States: none
Observables: none
BIOMD0000000803
@ v0.0.1
This model is an attempt to provide a mathematical description of IL-7 dependent T cell homeostasis at the molecular and…
DetailsInterleukin-7 (IL7) plays a nonredundant role in T cell survival and homeostasis, which is illustrated in the severe T cell lymphopenia of IL7-deficient mice, or demonstrated in animals or humans that lack expression of either the IL7Rα or γ c chain, the two subunits that constitute the functional IL7 receptor. Remarkably, IL7 is not expressed by T cells themselves, but produced in limited amounts by radio-resistant stromal cells. Thus, T cells need to constantly compete for IL7 to survive. How T cells maintain homeostasis and further maximize the size of the peripheral T cell pool in face of such competition are important questions that have fascinated both immunologists and mathematicians for a long time. Exceptionally, IL7 downregulates expression of its own receptor, so that IL7-signaled T cells do not consume extracellular IL7, and thus, the remaining extracellular IL7 can be shared among unsignaled T cells. Such an altruistic behavior of the IL7Rα chain is quite unique among members of the γ c cytokine receptor family. However, the consequences of this altruistic signaling behavior at the molecular, single cell and population levels are less well understood and require further investigation. In this regard, mathematical modeling of how a limited resource can be shared, while maintaining the clonal diversity of the T cell pool, can help decipher the molecular or cellular mechanisms that regulate T cell homeostasis. Thus, the current review aims to provide a mathematical modeling perspective of IL7-dependent T cell homeostasis at the molecular, cellular and population levels, in the context of recent advances in our understanding of the IL7 biology. This article is categorized under: Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods. link: http://identifiers.org/pubmed/31137085
Parameters:
Name | Description |
---|---|
k_f_4 = 1.66054E-5 |
Reaction: IL15 + IL15Ru => IL15Rb, Rate Law: compartment*k_f_4*IL15*IL15Ru |
k_f_3 = 1.66054E-4 |
Reaction: IL7 + IL7Ru => IL7Rb, Rate Law: compartment*k_f_3*IL7*IL7Ru |
k_f_2 = 1.66054E-4 |
Reaction: IL15Rbeta + gamma_c => IL15Ru, Rate Law: compartment*k_f_2*IL15Rbeta*gamma_c |
k_r_4 = 0.1 |
Reaction: IL15Rb => IL15 + IL15Ru, Rate Law: compartment*k_r_4*IL15Rb |
k_r_3 = 0.1 |
Reaction: IL7Rb => IL7 + IL7Ru, Rate Law: compartment*k_r_3*IL7Rb |
k_r_2 = 0.1 |
Reaction: IL15Ru => IL15Rbeta + gamma_c, Rate Law: compartment*k_r_2*IL15Ru |
k_r_1 = 0.1 |
Reaction: IL7Ru => IL7Ra + gamma_c, Rate Law: compartment*k_r_1*IL7Ru |
k_f_1 = 1.66054E-4 |
Reaction: IL7Ra + gamma_c => IL7Ru, Rate Law: compartment*k_f_1*IL7Ra*gamma_c |
States:
Name | Description |
---|---|
IL7Ra |
[Interleukin-7 Receptor Subunit Alpha] |
IL15Ru |
[Interleukin-15 Receptor] |
IL15Rbeta |
[Interleukin-2 Receptor Subunit Beta] |
gamma c |
[PR:P31785] |
IL15Rb |
[interleukin-15 receptor complex] |
IL7Ru |
[159734] |
IL15 |
[Interleukin-15] |
IL7 |
[Interleukin-7] |
IL7Rb |
[interleukin-7 receptor complex] |
Observables: none
BIOMD0000000914
@ v0.0.1
Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to im…
DetailsImmunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies. link: http://identifiers.org/pubmed/23605806
Parameters:
Name | Description |
---|---|
k1 = 0.0907 |
Reaction: VAC =>, Rate Law: compartment*k1*VAC |
gamma = 5.24; REG_50 = 3.18; k3 = 1.08 |
Reaction: Ts => ; REG, SVAC, Rate Law: compartment*k3*REG_50^gamma/(REG_50^gamma+REG^gamma)*Ts*SVAC |
k2_pop2 = 0.0907 |
Reaction: SVAC =>, Rate Law: compartment*k2_pop2*SVAC |
gamma = 5.24 |
Reaction: => Ts, Rate Law: compartment*gamma |
k4 = 0.039 |
Reaction: => REG; Ts, Rate Law: compartment*k4*Ts |
States:
Name | Description |
---|---|
VAC |
[Vaccine] |
Ts |
[Tumor Mass] |
SVAC |
[Signal; Vaccine; Signal] |
TRAN |
TRAN |
REG |
[Regulator] |
Observables: none
MODEL1812100001
@ v0.0.1
SBML and SBGN-ML models of atherosclerosis and atheroma formation. This model is described in the publication: New mod…
DetailsMotivation Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here we develop the first detailed, computational model of atherosclerosis and use it to develop multi-drug therapeutic hypotheses.
Results We assembled a network describing atheroma development from the literature. Maps and mathematical models were produced using the Systems Biology Graphical Notation (SBGN) and Systems Biology Markup Language (SBML), respectively. The model was constrained against clinical and laboratory data. We identified five drugs that together potentially reverse advanced atheroma formation.
Availability and Implementation The map is available in the supplementary information in SBGN-ML format. The model is available in the supplementary material and from BioModels, a repository of SBML models, containing CellDesigner markup.
Supplementary Information Available from Bioinformatics online. link: http://identifiers.org/doi/10.1093/bioinformatics/bty980
Parameters: none
States: none
Observables: none
MODEL0406553884
@ v0.0.1
This a model from the article: The functional role of cardiac T-tubules explored in a model of rat ventricular myocyte…
DetailsThe morphology of the cardiac transverse-axial tubular system (TATS) has been known for decades, but its function has received little attention. To explore the possible role of this system in the physiological modulation of electrical and contractile activity, we have developed a mathematical model of rat ventricular cardiomyocytes in which the TATS is described as a single compartment. The geometrical characteristics of the TATS, the biophysical characteristics of ion transporters and their distribution between surface and tubular membranes were based on available experimental data. Biophysically realistic values of mean access resistance to the tubular lumen and time constants for ion exchange with the bulk extracellular solution were included. The fraction of membrane in the TATS was set to 56%. The action potentials initiated in current-clamp mode are accompanied by transient K+ accumulation and transient Ca2+ depletion in the TATS lumen. The amplitude of these changes relative to external ion concentrations was studied at steady-state stimulation frequencies of 1-5 Hz. Ca2+ depletion increased from 7 to 13.1% with stimulation frequency, while K+ accumulation decreased from 4.1 to 2.7%. These ionic changes (particularly Ca2+ depletion) implicated significant decrease of intracellular Ca2+ load at frequencies natural for rat heart. link: http://identifiers.org/pubmed/16608703
Parameters: none
States: none
Observables: none
MODEL0406793751
@ v0.0.1
This a model from the article: A model of the guinea-pig ventricular cardiac myocyte incorporating a transverse-axial…
DetailsA model of the guinea-pig cardiac ventricular myocyte has been developed that includes a representation of the transverse-axial tubular system (TATS), including heterogeneous distribution of ion flux pathways between the surface and tubular membranes. The model reproduces frequency-dependent changes of action potential shape and intracellular ion concentrations and can replicate experimental data showing ion diffusion between the tubular lumen and external solution in guinea-pig myocytes. The model is stable at rest and during activity and returns to rested state after perturbation. Theoretical analysis and model simulations show that, due to tight electrical coupling, tubular and surface membranes behave as a homogeneous whole during voltage and current clamp (maximum difference 0.9 mV at peak tubular INa of -38 nA). However, during action potentials, restricted diffusion and ionic currents in TATS cause depletion of tubular Ca2+ and accumulation of tubular K+ (up to -19.8% and +3.4%, respectively, of bulk extracellular values, at 6 Hz). These changes, in turn, decrease ion fluxes across the TATS membrane and decrease sarcoplasmic reticulum (SR) Ca2+ load. Thus, the TATS plays a potentially important role in modulating the function of guinea-pig ventricular myocyte in physiological conditions. link: http://identifiers.org/pubmed/17888503
Parameters: none
States: none
Observables: none
BIOMD0000000287
@ v0.0.1
This is the model described in: **Feedback between p21 and reactive oxygen production is necessary for cell senescence.*…
DetailsCellular senescence–the permanent arrest of cycling in normally proliferating cells such as fibroblasts–contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of 'deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFbeta. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype. link: http://identifiers.org/pubmed/20160708
Parameters:
Name | Description |
---|---|
kdegp53 = 8.25E-4 |
Reaction: Mdm2_p53 => Mdm2, Rate Law: kdegp53*Mdm2_p53 |
kdephosp38 = 0.1 |
Reaction: p38_P => p38, Rate Law: kdephosp38*p38_P |
krepair = 6.0E-5 |
Reaction: damDNA => Sink, Rate Law: krepair*damDNA |
kphosp38 = 0.008 |
Reaction: p38 + GADD45 => p38_P + GADD45, Rate Law: kphosp38*p38*GADD45 |
krelMdm2p53 = 1.155E-6 |
Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53 |
kactATM = 2.0E-5 |
Reaction: damDNA + ATMI => damDNA + ATMA, Rate Law: kactATM*damDNA*ATMI |
kdegMdm2 = 4.33E-4 |
Reaction: Mdm2 => Sink, Rate Law: kdegMdm2*Mdm2 |
kdam = 0.007 |
Reaction: IR => IR + damDNA, Rate Law: kdam*IR |
kdephosMdm2 = 0.5 |
Reaction: Mdm2_P => Mdm2, Rate Law: kdephosMdm2*Mdm2_P |
kdamROS = 1.0E-5 |
Reaction: ROS => ROS + damDNA, Rate Law: kdamROS*ROS |
kdephosp53 = 0.5 |
Reaction: p53_P => p53, Rate Law: kdephosp53*p53_P |
ksynp21mRNAp53P = 6.0E-6 |
Reaction: p53_P => p53_P + p21_mRNA, Rate Law: ksynp21mRNAp53P*p53_P |
ksynp21step3 = 4.0E-5 |
Reaction: p21step2 => p21, Rate Law: ksynp21step3*p21step2 |
kdegp53mdm2ind = 8.25E-7 |
Reaction: p53 => Sink, Rate Law: kdegp53mdm2ind*p53 |
ksynp21mRNAp53 = 6.0E-8 |
Reaction: p53 => p53 + p21_mRNA, Rate Law: ksynp21mRNAp53*p53 |
kbinMdm2p53 = 0.001155 |
Reaction: p53 + Mdm2 => Mdm2_p53, Rate Law: kbinMdm2p53*p53*Mdm2 |
kremROS = 3.83E-4 |
Reaction: ROS => Sink, Rate Law: kremROS*ROS |
kinactATM = 5.0E-4 |
Reaction: ATMA => ATMI, Rate Law: kinactATM*ATMA |
kGADD45 = 4.0E-6 |
Reaction: p21 => p21 + GADD45, Rate Law: kGADD45*p21 |
kdamBasalROS = 1.0E-9 |
Reaction: basalROS => basalROS + damDNA, Rate Law: kdamBasalROS*basalROS |
kgenROSp38 = 4.5E-4; kp38ROS = 1.0 |
Reaction: p38_P => p38_P + ROS, Rate Law: kgenROSp38*p38_P*kp38ROS |
ksynp21step1 = 4.0E-4 |
Reaction: p21_mRNA => p21_mRNA + p21step1, Rate Law: ksynp21step1*p21_mRNA |
ksynp53 = 0.006 |
Reaction: p53_mRNA => p53 + p53_mRNA, Rate Law: ksynp53*p53_mRNA |
kphosMdm2 = 2.0 |
Reaction: Mdm2 + ATMA => Mdm2_P + ATMA, Rate Law: kphosMdm2*Mdm2*ATMA |
kdegGADD45 = 1.0E-5 |
Reaction: GADD45 => Sink, Rate Law: kdegGADD45*GADD45 |
kdegMdm2mRNA = 1.0E-4 |
Reaction: Mdm2_mRNA => Sink, Rate Law: kdegMdm2mRNA*Mdm2_mRNA |
kdegATMMdm2 = 4.0E-4 |
Reaction: Mdm2_P => Sink, Rate Law: kdegATMMdm2*Mdm2_P |
ksynp53mRNA = 0.001 |
Reaction: Source => p53_mRNA, Rate Law: ksynp53mRNA*Source |
ksynMdm2 = 4.95E-4 |
Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2, Rate Law: ksynMdm2*Mdm2_mRNA |
kphosp53 = 0.006 |
Reaction: p53 + ATMA => p53_P + ATMA, Rate Law: kphosp53*p53*ATMA |
ksynp21step2 = 4.0E-5 |
Reaction: p21step1 => p21step2, Rate Law: ksynp21step2*p21step1 |
kdegp53mRNA = 1.0E-4 |
Reaction: p53_mRNA => Sink, Rate Law: kdegp53mRNA*p53_mRNA |
kdegp21 = 1.9E-4 |
Reaction: p21 => Sink, Rate Law: kdegp21*p21 |
ksynMdm2mRNA = 1.0E-4 |
Reaction: p53 => p53 + Mdm2_mRNA, Rate Law: ksynMdm2mRNA*p53 |
kdegp21mRNA = 2.4E-5 |
Reaction: p21_mRNA => Sink, Rate Law: kdegp21mRNA*p21_mRNA |
States:
Name | Description |
---|---|
Mdm2 P |
[E3 ubiquitin-protein ligase Mdm2] |
p21 mRNA |
[Cyclin-dependent kinase inhibitor 1] |
basalROS |
[reactive oxygen species] |
GADD45 |
[Growth arrest and DNA damage-inducible protein GADD45 alpha; Growth arrest and DNA damage-inducible protein GADD45 beta; Growth arrest and DNA damage-inducible protein GADD45 gamma] |
p38 P |
[Mitogen-activated protein kinase 14] |
p53 |
[Cellular tumor antigen p53] |
Source |
Source |
p53 P |
[Cellular tumor antigen p53] |
IR |
IR |
Mdm2 |
[E3 ubiquitin-protein ligase Mdm2] |
ROS |
[reactive oxygen species] |
damDNA |
[deoxyribonucleic acid] |
p53 mRNA |
[Cellular tumor antigen p53] |
p38 |
[Mitogen-activated protein kinase 14] |
ATMA |
[Serine-protein kinase ATM] |
p21step2 |
[Cyclin-dependent kinase inhibitor 1] |
Mdm2 p53 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53] |
p21 |
[Cyclin-dependent kinase inhibitor 1] |
ATMI |
[Serine-protein kinase ATM] |
Sink |
Sink |
Mdm2 mRNA |
[E3 ubiquitin-protein ligase Mdm2] |
p21step1 |
[Cyclin-dependent kinase inhibitor 1] |
Observables: none
MODEL1507180063
@ v0.0.1
Pastick2009 - Genome-scale metabolic network of Streptococcus thermophilus (iMP429)This model is described in the articl…
DetailsIn this report, we describe the amino acid metabolism and amino acid dependency of the dairy bacterium Streptococcus thermophilus LMG18311 and compare them with those of two other characterized lactic acid bacteria, Lactococcus lactis and Lactobacillus plantarum. Through the construction of a genome-scale metabolic model of S. thermophilus, the metabolic differences between the three bacteria were visualized by direct projection on a metabolic map. The comparative analysis revealed the minimal amino acid auxotrophy (only histidine and methionine or cysteine) of S. thermophilus LMG18311 and the broad variety of volatiles produced from amino acids compared to the other two bacteria. It also revealed the limited number of pyruvate branches, forcing this strain to use the homofermentative metabolism for growth optimization. In addition, some industrially relevant features could be identified in S. thermophilus, such as the unique pathway for acetaldehyde (yogurt flavor) production and the absence of a complete pentose phosphate pathway. link: http://identifiers.org/pubmed/19346354
Parameters: none
States: none
Observables: none
BIOMD0000000491
@ v0.0.1
Pathak2013 - MAPK activation in response to various abiotic stressesMAPK activation mechanism in response to various abi…
DetailsMitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades. link: http://identifiers.org/pubmed/23847397
Parameters:
Name | Description |
---|---|
kass_re81 = 1.0 s^(-1); kdiss_re81 = 1.0 s^(-1) |
Reaction: s42 => s57, Rate Law: kass_re81*s42-kdiss_re81*s57 |
kass_re52 = 1.0 s^(-1); kdiss_re52 = 1.0 s^(-1) |
Reaction: s47 => s48, Rate Law: kass_re52*s47-kdiss_re52*s48 |
kdiss_re38 = 1.0 s^(-1); kass_re38 = 1.0 s^(-1) |
Reaction: s28 => s30, Rate Law: kass_re38*s28-kdiss_re38*s30 |
kdiss_re78 = 1.0 s^(-1); kass_re78 = 1.0 s^(-1) |
Reaction: s48 => s57, Rate Law: kass_re78*s48-kdiss_re78*s57 |
kass_re31 = 1.0 s^(-1); kdiss_re31 = 1.0 s^(-1) |
Reaction: s18 => s26, Rate Law: kass_re31*s18-kdiss_re31*s26 |
kdiss_re55 = 1.0 s^(-1); kass_re55 = 1.0 s^(-1) |
Reaction: s29 => s37, Rate Law: kass_re55*s29-kdiss_re55*s37 |
kdiss_re64 = 1.0 s^(-1); kass_re64 = 1.0 s^(-1) |
Reaction: s32 => s45, Rate Law: kass_re64*s32-kdiss_re64*s45 |
kass_re30 = 1.0 s^(-1); kdiss_re30 = 1.0 s^(-1) |
Reaction: s18 => s25, Rate Law: kass_re30*s18-kdiss_re30*s25 |
kass_re35 = 1.0 s^(-1); kdiss_re35 = 1.0 s^(-1) |
Reaction: s15 => s20, Rate Law: kass_re35*s15-kdiss_re35*s20 |
kass_re68 = 1.0 s^(-1); kdiss_re68 = 1.0 s^(-1) |
Reaction: s28 => s51, Rate Law: kass_re68*s28-kdiss_re68*s51 |
kass_re48 = 1.0 s^(-1); kdiss_re48 = 1.0 s^(-1) |
Reaction: s39 => s40, Rate Law: kass_re48*s39-kdiss_re48*s40 |
kass_re23 = 1.0 s^(-1); kdiss_re23 = 1.0 s^(-1) |
Reaction: s14 => s17, Rate Law: kass_re23*s14-kdiss_re23*s17 |
kass_re25 = 1.0 s^(-1); kdiss_re25 = 1.0 s^(-1) |
Reaction: s18 => s20, Rate Law: kass_re25*s18-kdiss_re25*s20 |
kdiss_re47 = 1.0 s^(-1); kass_re47 = 1.0 s^(-1) |
Reaction: s37 => s38, Rate Law: kass_re47*s37-kdiss_re47*s38 |
kass_re58 = 1.0 s^(-1); kdiss_re58 = 1.0 s^(-1) |
Reaction: s30 => s41, Rate Law: kass_re58*s30-kdiss_re58*s41 |
kass_re49 = 1.0 s^(-1); kdiss_re49 = 1.0 s^(-1) |
Reaction: s41 => s42, Rate Law: kass_re49*s41-kdiss_re49*s42 |
kass_re40 = 1.0 s^(-1); kdiss_re40 = 1.0 s^(-1) |
Reaction: s28 => s32, Rate Law: kass_re40*s28-kdiss_re40*s32 |
kdiss_re57 = 1.0 s^(-1); kass_re57 = 1.0 s^(-1) |
Reaction: s30 => s35, Rate Law: kass_re57*s30-kdiss_re57*s35 |
kdiss_re67 = 1.0 s^(-1); kass_re67 = 1.0 s^(-1) |
Reaction: s28 => s49, Rate Law: kass_re67*s28-kdiss_re67*s49 |
kdiss_re69 = 1.0 s^(-1); kass_re69 = 1.0 s^(-1) |
Reaction: s28 => s53, Rate Law: kass_re69*s28-kdiss_re69*s53 |
kass_re84 = 1.0 s^(-1); kdiss_re84 = 1.0 s^(-1) |
Reaction: s36 => s57, Rate Law: kass_re84*s36-kdiss_re84*s57 |
kass_re62 = 1.0 s^(-1); kdiss_re62 = 1.0 s^(-1) |
Reaction: s31 => s39, Rate Law: kass_re62*s31-kdiss_re62*s39 |
kass_re44 = 1.0 s^(-1); kdiss_re44 = 1.0 s^(-1) |
Reaction: s26 => s30, Rate Law: kass_re44*s26-kdiss_re44*s30 |
kdiss_re76 = 1.0 s^(-1); kass_re76 = 1.0 s^(-1) |
Reaction: s50 => s57, Rate Law: kass_re76*s50-kdiss_re76*s57 |
kass_re15 = 1.0 s^(-1); kdiss_re15 = 1.0 s^(-1) |
Reaction: s9 => s13, Rate Law: kass_re15*s9-kdiss_re15*s13 |
kdiss_re29 = 1.0 s^(-1); kass_re29 = 1.0 s^(-1) |
Reaction: s18 => s24, Rate Law: kass_re29*s18-kdiss_re29*s24 |
kdiss_re79 = 1.0 s^(-1); kass_re79 = 1.0 s^(-1) |
Reaction: s30 => s43, Rate Law: kass_re79*s30-kdiss_re79*s43 |
kdiss_re60 = 1.0 s^(-1); kass_re60 = 1.0 s^(-1) |
Reaction: s31 => s33, Rate Law: kass_re60*s31-kdiss_re60*s33 |
kass_re46 = 1.0 s^(-1); kdiss_re46 = 1.0 s^(-1) |
Reaction: s35 => s36, Rate Law: kass_re46*s35-kdiss_re46*s36 |
kdiss_re83 = 1.0 s^(-1); kass_re83 = 1.0 s^(-1) |
Reaction: s38 => s57, Rate Law: kass_re83*s38-kdiss_re83*s57 |
kass_re36 = 1.0 s^(-1); kdiss_re36 = 1.0 s^(-1) |
Reaction: s16 => s26, Rate Law: kass_re36*s16-kdiss_re36*s26 |
kdiss_re24 = 1.0 s^(-1); kass_re24 = 1.0 s^(-1) |
Reaction: s18 => s19, Rate Law: kass_re24*s18-kdiss_re24*s19 |
kdiss_re32 = 1.0 s^(-1); kass_re32 = 1.0 s^(-1) |
Reaction: s27 => s28, Rate Law: kass_re32*s27-kdiss_re32*s28 |
kdiss_re28 = 1.0 s^(-1); kass_re28 = 1.0 s^(-1) |
Reaction: s18 => s23, Rate Law: kass_re28*s18-kdiss_re28*s23 |
kass_re61 = 1.0 s^(-1); kdiss_re61 = 1.0 s^(-1) |
Reaction: s31 => s45, Rate Law: kass_re61*s31-kdiss_re61*s45 |
kdiss_re65 = 1.0 s^(-1); kass_re65 = 1.0 s^(-1) |
Reaction: s32 => s35, Rate Law: kass_re65*s32-kdiss_re65*s35 |
kdiss_re19 = 1.0 s^(-1); kass_re19 = 1.0 s^(-1) |
Reaction: s14 => s16, Rate Law: kass_re19*s14-kdiss_re19*s16 |
kass_re71 = 1.0 s^(-1); kdiss_re71 = 1.0 s^(-1) |
Reaction: s28 => s55, Rate Law: kass_re71*s28-kdiss_re71*s55 |
kass_re85 = 1.0 s^(-1); kdiss_re85 = 1.0 s^(-1) |
Reaction: s34 => s57, Rate Law: kass_re85*s34-kdiss_re85*s57 |
kass_re66 = 1.0 s^(-1); kdiss_re66 = 1.0 s^(-1) |
Reaction: s28 => s56, Rate Law: kass_re66*s28-kdiss_re66*s56 |
kass_re17 = 1.0 s^(-1); kdiss_re17 = 1.0 s^(-1) |
Reaction: s14 => s15, Rate Law: kass_re17*s14-kdiss_re17*s15 |
kdiss_re22 = 1.0 s^(-1); kass_re22 = 1.0 s^(-1) |
Reaction: s17 => s18, Rate Law: kass_re22*s17-kdiss_re22*s18 |
kass_re26 = 1.0 s^(-1); kdiss_re26 = 1.0 s^(-1) |
Reaction: s18 => s21, Rate Law: kass_re26*s18-kdiss_re26*s21 |
kass_re50 = 1.0 s^(-1); kdiss_re50 = 1.0 s^(-1) |
Reaction: s43 => s44, Rate Law: kass_re50*s43-kdiss_re50*s44 |
kass_re39 = 1.0 s^(-1); kdiss_re39 = 1.0 s^(-1) |
Reaction: s28 => s31, Rate Law: kass_re39*s28-kdiss_re39*s31 |
kass_re54 = 1.0 s^(-1); kdiss_re54 = 1.0 s^(-1) |
Reaction: s51 => s52, Rate Law: kass_re54*s51-kdiss_re54*s52 |
kass_re11 = 1.0 s^(-1); kdiss_re11 = 1.0 s^(-1) |
Reaction: s5 => s7, Rate Law: kass_re11*s5-kdiss_re11*s7 |
kass_re86 = 1.0 s^(-1); kdiss_re86 = 1.0 s^(-1) |
Reaction: s46 => s57, Rate Law: kass_re86*s46-kdiss_re86*s57 |
kdiss_re45 = 1.0 s^(-1); kass_re45 = 1.0 s^(-1) |
Reaction: s33 => s34, Rate Law: kass_re45*s33-kdiss_re45*s34 |
kdiss_re33 = 1.0 s^(-1); kass_re33 = 1.0 s^(-1) |
Reaction: s18 => s27, Rate Law: kass_re33*s18-kdiss_re33*s27 |
kass_re70 = 1.0 s^(-1); kdiss_re70 = 1.0 s^(-1) |
Reaction: s28 => s54, Rate Law: kass_re70*s28-kdiss_re70*s54 |
kass_re59 = 1.0 s^(-1); kdiss_re59 = 1.0 s^(-1) |
Reaction: s30 => s47, Rate Law: kass_re59*s30-kdiss_re59*s47 |
kdiss_re21 = 1.0 s^(-1); kass_re21 = 1.0 s^(-1) |
Reaction: s12 => s16, Rate Law: kass_re21*s12-kdiss_re21*s16 |
kdiss_re34 = 1.0 s^(-1); kass_re34 = 1.0 s^(-1) |
Reaction: s15 => s19, Rate Law: kass_re34*s15-kdiss_re34*s19 |
kdiss_re2 = 1.0 s^(-1); kass_re2 = 1.0 s^(-1) |
Reaction: s2 => s7, Rate Law: kass_re2*s2-kdiss_re2*s7 |
kass_re43 = 1.0 s^(-1); kdiss_re43 = 1.0 s^(-1) |
Reaction: s20 => s32, Rate Law: kass_re43*s20-kdiss_re43*s32 |
kass_re27 = 1.0 s^(-1); kdiss_re27 = 1.0 s^(-1) |
Reaction: s18 => s22, Rate Law: kass_re27*s18-kdiss_re27*s22 |
kdiss_re1 = 1.0 s^(-1); kass_re1 = 1.0 s^(-1) |
Reaction: s1 => s7, Rate Law: kass_re1*s1-kdiss_re1*s7 |
kdiss_re42 = 1.0 s^(-1); kass_re42 = 1.0 s^(-1) |
Reaction: s20 => s31, Rate Law: kass_re42*s20-kdiss_re42*s31 |
kass_re53 = 1.0 s^(-1); kdiss_re53 = 1.0 s^(-1) |
Reaction: s49 => s50, Rate Law: kass_re53*s49-kdiss_re53*s50 |
kass_re20 = 1.0 s^(-1); kdiss_re20 = 1.0 s^(-1) |
Reaction: s11 => s16, Rate Law: kass_re20*s11-kdiss_re20*s16 |
kass_re37 = 1.0 s^(-1); kdiss_re37 = 1.0 s^(-1) |
Reaction: s28 => s29, Rate Law: kass_re37*s28-kdiss_re37*s29 |
kdiss_re10 = 1.0 s^(-1); kass_re10 = 1.0 s^(-1) |
Reaction: s4 => s7, Rate Law: kass_re10*s4-kdiss_re10*s7 |
kdiss_re18 = 1.0 s^(-1); kass_re18 = 1.0 s^(-1) |
Reaction: s7 => s15, Rate Law: kass_re18*s7-kdiss_re18*s15 |
kdiss_re63 = 1.0 s^(-1); kass_re63 = 1.0 s^(-1) |
Reaction: s32 => s47, Rate Law: kass_re63*s32-kdiss_re63*s47 |
kdiss_re51 = 1.0 s^(-1); kass_re51 = 1.0 s^(-1) |
Reaction: s45 => s46, Rate Law: kass_re51*s45-kdiss_re51*s46 |
kdiss_re82 = 1.0 s^(-1); kass_re82 = 1.0 s^(-1) |
Reaction: s44 => s57, Rate Law: kass_re82*s44-kdiss_re82*s57 |
kass_re56 = 1.0 s^(-1); kdiss_re56 = 1.0 s^(-1) |
Reaction: s29 => s33, Rate Law: kass_re56*s29-kdiss_re56*s33 |
kass_re72 = 1.0 s^(-1); kdiss_re72 = 1.0 s^(-1) |
Reaction: s40 => s57, Rate Law: kass_re72*s40-kdiss_re72*s57 |
States:
Name | Description |
---|---|
s5 |
[cellular response to metal ion] |
s14 |
[Mitogen-activated protein kinase kinase kinase 5] |
s18 |
[Mitogen-activated protein kinase kinase 1] |
s37 |
[Probable WRKY transcription factor 8] |
s40 |
[Probable WRKY transcription factor 25] |
s20 |
[Mitogen-activated protein kinase kinase 2] |
s35 |
[Probable WRKY transcription factor 12] |
s44 |
[Probable WRKY transcription factor 29] |
s57 |
[cellular response to stress] |
s43 |
[Probable WRKY transcription factor 29] |
s19 |
[Mitogen-activated protein kinase kinase 1] |
s31 |
[Mitogen-activated protein kinase 4] |
s36 |
[Probable WRKY transcription factor 12] |
s34 |
[WRKY transcription factor 1] |
s50 |
[ATMYB2At2g47190MYB transcription factorMYB transcription factor (Atmyb2)MYB transcription factor Atmyb2Myb domain protein 2] |
s38 |
[Probable WRKY transcription factor 8] |
s47 |
[Probable WRKY transcription factor 28] |
s32 |
[Mitogen-activated protein kinase 6] |
s46 |
[Probable WRKY transcription factor 33] |
s15 |
[Mitogen-activated protein kinase kinase kinase 1] |
s51 |
[Transcription repressor MYB4] |
s45 |
[Probable WRKY transcription factor 33] |
s1 |
[decreased temperature] |
s48 |
[Probable WRKY transcription factor 28] |
s17 |
[Mitogen-activated protein kinase kinase 1] |
s41 |
[WRKY transcription factor 22] |
s25 |
[Dual specificity mitogen-activated protein kinase kinase 7] |
s13 |
[Mitogen-activated protein kinase kinase kinase 5] |
s2 |
[sodium chloride] |
s49 |
[ATMYB2At2g47190MYB transcription factorMYB transcription factor (Atmyb2)MYB transcription factor Atmyb2Myb domain protein 2] |
s33 |
[WRKY transcription factor 1] |
s16 |
[Serine/threonine-protein kinase CTR1] |
s4 |
[hydrogen peroxide] |
s30 |
[Mitogen-activated protein kinase 3] |
s26 |
[Dual specificity mitogen-activated protein kinase kinase 1] |
s42 |
[WRKY transcription factor 22] |
s28 |
[Mitogen-activated protein kinase 3] |
s39 |
[Probable WRKY transcription factor 25] |
s29 |
[Mitogen-activated protein kinase] |
s27 |
[Mitogen-activated protein kinase 3] |
Observables: none
BIOMD0000000492
@ v0.0.1
Pathak2013 - MAPK activation in response to various biotic stressesMAPK activation mechanism in response to various biot…
DetailsMitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades. link: http://identifiers.org/pubmed/23847397
Parameters:
Name | Description |
---|---|
kass_re81 = 1.0 s^(-1); kdiss_re81 = 1.0 s^(-1) |
Reaction: s37 => s52, Rate Law: kass_re81*s37-kdiss_re81*s52 |
kdiss_re38 = 1.0 s^(-1); kass_re38 = 1.0 s^(-1) |
Reaction: s16 => s22, Rate Law: kass_re38*s16-kdiss_re38*s22 |
kass_re31 = 1.0 s^(-1); kdiss_re31 = 1.0 s^(-1) |
Reaction: s15 => s20, Rate Law: kass_re31*s15-kdiss_re31*s20 |
kdiss_re55 = 1.0 s^(-1); kass_re55 = 1.0 s^(-1) |
Reaction: s22 => s28, Rate Law: kass_re55*s22-kdiss_re55*s28 |
kass_re5 = 1.0 s^(-1); kdiss_re5 = 1.0 s^(-1) |
Reaction: s2 => s5, Rate Law: kass_re5*s2-kdiss_re5*s5 |
kass_re30 = 1.0 s^(-1); kdiss_re30 = 1.0 s^(-1) |
Reaction: s20 => s21, Rate Law: kass_re30*s20-kdiss_re30*s21 |
kass_re35 = 1.0 s^(-1); kdiss_re35 = 1.0 s^(-1) |
Reaction: s21 => s25, Rate Law: kass_re35*s21-kdiss_re35*s25 |
kass_re68 = 1.0 s^(-1); kdiss_re68 = 1.0 s^(-1) |
Reaction: s25 => s36, Rate Law: kass_re68*s25-kdiss_re68*s36 |
kass_re14 = 1.0 s^(-1); kdiss_re14 = 1.0 s^(-1) |
Reaction: s8 => s11, Rate Law: kass_re14*s8-kdiss_re14*s11 |
kass_re23 = 1.0 s^(-1); kdiss_re23 = 1.0 s^(-1) |
Reaction: s15 => s17, Rate Law: kass_re23*s15-kdiss_re23*s17 |
kass_re48 = 1.0 s^(-1); kdiss_re48 = 1.0 s^(-1) |
Reaction: s34 => s35, Rate Law: kass_re48*s34-kdiss_re48*s35 |
kdiss_re13 = 1.0 s^(-1); kass_re13 = 1.0 s^(-1) |
Reaction: s8 => s10, Rate Law: kass_re13*s8-kdiss_re13*s10 |
kass_re25 = 1.0 s^(-1); kdiss_re25 = 1.0 s^(-1) |
Reaction: s15 => s19, Rate Law: kass_re25*s15-kdiss_re25*s19 |
kdiss_re47 = 1.0 s^(-1); kass_re47 = 1.0 s^(-1) |
Reaction: s32 => s33, Rate Law: kass_re47*s32-kdiss_re47*s33 |
kass_re49 = 1.0 s^(-1); kdiss_re49 = 1.0 s^(-1) |
Reaction: s36 => s37, Rate Law: kass_re49*s36-kdiss_re49*s37 |
kass_re40 = 1.0 s^(-1); kdiss_re40 = 1.0 s^(-1) |
Reaction: s17 => s23, Rate Law: kass_re40*s17-kdiss_re40*s23 |
kdiss_re69 = 1.0 s^(-1); kass_re69 = 1.0 s^(-1) |
Reaction: s21 => s30, Rate Law: kass_re69*s21-kdiss_re69*s30 |
kdiss_re41 = 1.0 s^(-1); kass_re41 = 1.0 s^(-1) |
Reaction: s18 => s23, Rate Law: kass_re41*s18-kdiss_re41*s23 |
kass_re62 = 1.0 s^(-1); kdiss_re62 = 1.0 s^(-1) |
Reaction: s25 => s46, Rate Law: kass_re62*s25-kdiss_re62*s46 |
kass_re12 = 1.0 s^(-1); kdiss_re12 = 1.0 s^(-1) |
Reaction: s8 => s9, Rate Law: kass_re12*s8-kdiss_re12*s9 |
kass_re44 = 1.0 s^(-1); kdiss_re44 = 1.0 s^(-1) |
Reaction: s18 => s25, Rate Law: kass_re44*s18-kdiss_re44*s25 |
kdiss_re76 = 1.0 s^(-1); kass_re76 = 1.0 s^(-1) |
Reaction: s31 => s52, Rate Law: kass_re76*s31-kdiss_re76*s52 |
kass_re15 = 1.0 s^(-1); kdiss_re15 = 1.0 s^(-1) |
Reaction: s8 => s12, Rate Law: kass_re15*s8-kdiss_re15*s12 |
kdiss_re29 = 1.0 s^(-1); kass_re29 = 1.0 s^(-1) |
Reaction: s11 => s19, Rate Law: kass_re29*s11-kdiss_re29*s19 |
kass_re6 = 1.0 s^(-1); kdiss_re6 = 1.0 s^(-1) |
Reaction: s2 => s6, Rate Law: kass_re6*s2-kdiss_re6*s6 |
kass_re74 = 1.0 s^(-1); kdiss_re74 = 1.0 s^(-1) |
Reaction: s24 => s34, Rate Law: kass_re74*s24-kdiss_re74*s34 |
kass_re16 = 1.0 s^(-1); kdiss_re16 = 1.0 s^(-1) |
Reaction: s6 => s9, Rate Law: kass_re16*s6-kdiss_re16*s9 |
kdiss_re88 = 1.0 s^(-1); kass_re88 = 1.0 s^(-1) |
Reaction: s33 => s52, Rate Law: kass_re88*s33-kdiss_re88*s52 |
kass_re36 = 1.0 s^(-1); kdiss_re36 = 1.0 s^(-1) |
Reaction: s21 => s26, Rate Law: kass_re36*s21-kdiss_re36*s26 |
kdiss_re24 = 1.0 s^(-1); kass_re24 = 1.0 s^(-1) |
Reaction: s15 => s18, Rate Law: kass_re24*s15-kdiss_re24*s18 |
kdiss_re32 = 1.0 s^(-1); kass_re32 = 1.0 s^(-1) |
Reaction: s21 => s22, Rate Law: kass_re32*s21-kdiss_re32*s22 |
kdiss_re28 = 1.0 s^(-1); kass_re28 = 1.0 s^(-1) |
Reaction: s9 => s18, Rate Law: kass_re28*s9-kdiss_re28*s18 |
kdiss_re19 = 1.0 s^(-1); kass_re19 = 1.0 s^(-1) |
Reaction: s5 => s11, Rate Law: kass_re19*s5-kdiss_re19*s11 |
kass_re66 = 1.0 s^(-1); kdiss_re66 = 1.0 s^(-1) |
Reaction: s25 => s44, Rate Law: kass_re66*s25-kdiss_re66*s44 |
kass_re71 = 1.0 s^(-1); kdiss_re71 = 1.0 s^(-1) |
Reaction: s21 => s49, Rate Law: kass_re71*s21-kdiss_re71*s49 |
kass_re17 = 1.0 s^(-1); kdiss_re17 = 1.0 s^(-1) |
Reaction: s8 => s13, Rate Law: kass_re17*s8-kdiss_re17*s13 |
kass_re73 = 1.0 s^(-1); kdiss_re73 = 1.0 s^(-1) |
Reaction: s21 => s50, Rate Law: kass_re73*s21-kdiss_re73*s50 |
kass_re3 = 1.0 s^(-1); kdiss_re3 = 1.0 s^(-1) |
Reaction: s1 => s5, Rate Law: kass_re3*s1-kdiss_re3*s5 |
kdiss_re22 = 1.0 s^(-1); kass_re22 = 1.0 s^(-1) |
Reaction: s15 => s16, Rate Law: kass_re22*s15-kdiss_re22*s16 |
kass_re26 = 1.0 s^(-1); kdiss_re26 = 1.0 s^(-1) |
Reaction: s9 => s16, Rate Law: kass_re26*s9-kdiss_re26*s16 |
kass_re50 = 1.0 s^(-1); kdiss_re50 = 1.0 s^(-1) |
Reaction: s38 => s39, Rate Law: kass_re50*s38-kdiss_re50*s39 |
kass_re39 = 1.0 s^(-1); kdiss_re39 = 1.0 s^(-1) |
Reaction: s17 => s22, Rate Law: kass_re39*s17-kdiss_re39*s22 |
kass_re11 = 1.0 s^(-1); kdiss_re11 = 1.0 s^(-1) |
Reaction: s6 => s7, Rate Law: kass_re11*s6-kdiss_re11*s7 |
kdiss_re33 = 1.0 s^(-1); kass_re33 = 1.0 s^(-1) |
Reaction: s21 => s23, Rate Law: kass_re33*s21-kdiss_re33*s23 |
kass_re8 = 1.0 s^(-1); kdiss_re8 = 1.0 s^(-1) |
Reaction: s3 => s7, Rate Law: kass_re8*s3-kdiss_re8*s7 |
kass_re70 = 1.0 s^(-1); kdiss_re70 = 1.0 s^(-1) |
Reaction: s21 => s48, Rate Law: kass_re70*s21-kdiss_re70*s48 |
kass_re59 = 1.0 s^(-1); kdiss_re59 = 1.0 s^(-1) |
Reaction: s24 => s38, Rate Law: kass_re59*s24-kdiss_re59*s38 |
kdiss_re7 = 1.0 s^(-1); kass_re7 = 1.0 s^(-1) |
Reaction: s7 => s8, Rate Law: kass_re7*s7-kdiss_re7*s8 |
kdiss_re21 = 1.0 s^(-1); kass_re21 = 1.0 s^(-1) |
Reaction: s8 => s14, Rate Law: kass_re21*s8-kdiss_re21*s14 |
kdiss_re34 = 1.0 s^(-1); kass_re34 = 1.0 s^(-1) |
Reaction: s21 => s24, Rate Law: kass_re34*s21-kdiss_re34*s24 |
kdiss_re2 = 1.0 s^(-1); kass_re2 = 1.0 s^(-1) |
Reaction: s1 => s4, Rate Law: kass_re2*s1-kdiss_re2*s4 |
kass_re43 = 1.0 s^(-1); kdiss_re43 = 1.0 s^(-1) |
Reaction: s16 => s24, Rate Law: kass_re43*s16-kdiss_re43*s24 |
kass_re27 = 1.0 s^(-1); kdiss_re27 = 1.0 s^(-1) |
Reaction: s9 => s17, Rate Law: kass_re27*s9-kdiss_re27*s17 |
kass_re9 = 1.0 s^(-1); kdiss_re9 = 1.0 s^(-1) |
Reaction: s4 => s7, Rate Law: kass_re9*s4-kdiss_re9*s7 |
kdiss_re1 = 1.0 s^(-1); kass_re1 = 1.0 s^(-1) |
Reaction: s1 => s3, Rate Law: kass_re1*s1-kdiss_re1*s3 |
kdiss_re42 = 1.0 s^(-1); kass_re42 = 1.0 s^(-1) |
Reaction: s17 => s25, Rate Law: kass_re42*s17-kdiss_re42*s25 |
kass_re20 = 1.0 s^(-1); kdiss_re20 = 1.0 s^(-1) |
Reaction: s14 => s15, Rate Law: kass_re20*s14-kdiss_re20*s15 |
kass_re37 = 1.0 s^(-1); kdiss_re37 = 1.0 s^(-1) |
Reaction: s21 => s27, Rate Law: kass_re37*s21-kdiss_re37*s27 |
kdiss_re10 = 1.0 s^(-1); kass_re10 = 1.0 s^(-1) |
Reaction: s5 => s7, Rate Law: kass_re10*s5-kdiss_re10*s7 |
kdiss_re18 = 1.0 s^(-1); kass_re18 = 1.0 s^(-1) |
Reaction: s5 => s13, Rate Law: kass_re18*s5-kdiss_re18*s13 |
kdiss_re63 = 1.0 s^(-1); kass_re63 = 1.0 s^(-1) |
Reaction: s25 => s32, Rate Law: kass_re63*s25-kdiss_re63*s32 |
kass_re56 = 1.0 s^(-1); kdiss_re56 = 1.0 s^(-1) |
Reaction: s24 => s28, Rate Law: kass_re56*s24-kdiss_re56*s28 |
kdiss_re4 = 1.0 s^(-1); kass_re4 = 1.0 s^(-1) |
Reaction: s2 => s4, Rate Law: kass_re4*s2-kdiss_re4*s4 |
kass_re72 = 1.0 s^(-1); kdiss_re72 = 1.0 s^(-1) |
Reaction: s21 => s51, Rate Law: kass_re72*s21-kdiss_re72*s51 |
States:
Name | Description |
---|---|
s8 |
[Mitogen-activated protein kinase kinase kinase 5] |
s5 |
[LRR receptor-like serine/threonine-protein kinase FLS2] |
s7 |
[Mitogen-activated protein kinase kinase kinase 5] |
s14 |
[Mannosyl-oligosaccharide 1,2-alpha-mannosidase MNS2] |
s18 |
[Dual specificity mitogen-activated protein kinase kinase 5] |
s20 |
[Mitogen-activated protein kinase 3] |
s23 |
[Mitogen-activated protein kinase 3] |
s24 |
[Mitogen-activated protein kinase 4] |
s37 |
[Transcription repressor MYB4] |
s9 |
[Mitogen-activated protein kinase kinase kinase 1] |
s19 |
[Dual specificity mitogen-activated protein kinase kinase 1] |
s31 |
[ATMYB2At2g47190MYB transcription factorMYB transcription factor (Atmyb2)MYB transcription factor Atmyb2Myb domain protein 2] |
s10 |
[Mitogen-activated protein kinase kinase kinase 18] |
s34 |
[WRKY transcription factor 6] |
s36 |
[Transcription repressor MYB4] |
s38 |
[Probable WRKY transcription factor 25] |
s6 |
[Probable leucine-rich repeat receptor-like serine/threonine-protein kinase At3g14840] |
s32 |
[Probable WRKY transcription factor 33] |
s22 |
[Mitogen-activated protein kinase] |
s11 |
[Mitogen-activated protein kinase kinase kinase 19Protein kinase-like protein] |
s15 |
[Mannosyl-oligosaccharide 1,2-alpha-mannosidase MNS2] |
s3 |
[LysM domain-containing GPI-anchored protein 1] |
s1 |
[173629; pathogen] |
s17 |
[Dual specificity mitogen-activated protein kinase kinase 4] |
s13 |
[Serine/threonine-protein kinase EDR1] |
s25 |
[Mitogen-activated protein kinase 6] |
s2 |
[Bacteria Latreille et al. 1825; pathogen] |
s4 |
[Pinoresinol reductase 1] |
s33 |
[Probable WRKY transcription factor 33] |
s16 |
[Mitogen-activated protein kinase kinase 2] |
s21 |
[Mitogen-activated protein kinase 3] |
s28 |
[WRKY transcription factor 1] |
s39 |
[Probable WRKY transcription factor 25] |
Observables: none
MODEL1812040006
@ v0.0.1
a possible mechanism of MP in determining HP versus LP outcomes, and how different interventions might affect infection…
DetailsThe World Health Organization identifies influenza as a major public health problem. While the strains commonly circulating in humans usually do not cause severe pathogenicity in healthy adults, some strains that have infected humans, such as H5N1, can cause high morbidity and mortality. Based on the severity of the disease, influenza viruses are sometimes categorized as either being highly pathogenic (HP) or having low pathogenicity (LP). The reasons why some strains are LP and others HP are not fully understood. While there are likely multiple mechanisms of interaction between the virus and the immune response that determine LP versus HP outcomes, we focus here on one component, namely macrophages (MP). There is some evidence that MP may both help fight the infection and become productively infected with HP influenza viruses. We developed mathematical models for influenza infections which explicitly included the dynamics and action of MP. We fit these models to viral load and macrophage count data from experimental infections of mice with LP and HP strains. Our results suggest that MP may not only help fight an influenza infection but may contribute to virus production in infections with HP viruses. We also explored the impact of combination therapies with antivirals and anti-inflammatory drugs on HP infections. Our study suggests a possible mechanism of MP in determining HP versus LP outcomes, and how different interventions might affect infection dynamics. link: http://identifiers.org/pubmed/26918620
Parameters: none
States: none
Observables: none
MODEL2003190006
@ v0.0.1
Physiologically based pharmacokinetic (PBPK) models were developed using MATLAB Simulink(®) to predict diurnal variation…
DetailsPhysiologically based pharmacokinetic (PBPK) models were developed using MATLAB Simulink(®) to predict diurnal variations of endogenous melatonin with light as well as pharmacokinetics of exogenous melatonin via different routes of administration. The model was structured using whole body, including pineal and saliva compartments, and parameterized based on the literature values for endogenous melatonin. It was then optimized by including various intensities of light and various dosage and formulation of melatonin. The model predictions generally have a good fit with available experimental data as evaluated by mean squared errors and ratios between model-predicted and observed values considering large variations in melatonin secretion and pharmacokinetics as reported in the literature. It also demonstrates the capability and usefulness in simulating plasma and salivary concentrations of melatonin under different light conditions and the interaction of endogenous melatonin with the pharmacokinetics of exogenous melatonin. Given the mechanistic approach and programming flexibility of MATLAB Simulink(®), the PBPK model could provide predictions of endogenous melatonin rhythms and pharmacokinetic changes in response to environmental (light) and experimental (dosage and route of administration) conditions. Furthermore, the model may be used to optimize the combined treatment using light exposure and exogenous melatonin for maximal phase advances or delays. link: http://identifiers.org/pubmed/24120727
Parameters: none
States: none
Observables: none
MODEL1001150000
@ v0.0.1
This the full model from the article: A dynamic model of interactions of Ca2+, calmodulin, and catalytic subunits of C…
DetailsDuring the acquisition of memories, influx of Ca2+ into the postsynaptic spine through the pores of activated N-methyl-D-aspartate-type glutamate receptors triggers processes that change the strength of excitatory synapses. The pattern of Ca2+influx during the first few seconds of activity is interpreted within the Ca2+-dependent signaling network such that synaptic strength is eventually either potentiated or depressed. Many of the critical signaling enzymes that control synaptic plasticity,including Ca2+/calmodulin-dependent protein kinase II (CaMKII), are regulated by calmodulin, a small protein that can bindup to 4 Ca2+ ions. As a first step toward clarifying how the Ca2+-signaling network decides between potentiation or depression, we have created a kinetic model of the interactions of Ca2+, calmodulin, and CaMKII that represents our best understanding of the dynamics of these interactions under conditions that resemble those in a postsynaptic spine. We constrained parameters of the model from data in the literature, or from our own measurements, and then predicted time courses of activation and autophosphorylation of CaMKII under a variety of conditions. Simulations showed that species of calmodulin with fewer than four bound Ca2+ play a significant role in activation of CaMKII in the physiological regime,supporting the notion that processing of Ca2+ signals in a spine involves competition among target enzymes for binding to unsaturated species of CaM in an environment in which the concentration of Ca2+ is fluctuating rapidly. Indeed, we showed that dependence of activation on the frequency of Ca2+ transients arises from the kinetics of interaction of fluctuating Ca2+with calmodulin/CaMKII complexes. We used parameter sensitivity analysis to identify which parameters will be most beneficial to measure more carefully to improve the accuracy of predictions. This model provides a quantitative base from which to build more complex dynamic models of postsynaptic signal transduction during learning. link: http://identifiers.org/pubmed/20168991
Parameters: none
States: none
Observables: none
MODEL1804130001
@ v0.0.1
The model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinog…
DetailsActinobacillus succinogenes is a promising bacterial catalyst for the bioproduction of succinic acid from low-cost raw materials. In this work, a genome-scale metabolic model was reconstructed and used to assess the metabolic capabilities of this microorganism under producing conditions.The model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinogenes 130Z and manual inspection of metabolic pathways, covering 1072 enzymatic reactions associated with 722 metabolic genes that involve 713 metabolites. The highly curated model was effective in capturing the growth of A. succinogenes on various carbon sources, as well as the SA production under various growth conditions with fair agreement between experimental and predicted data. Calculated flux distributions under different conditions show that a number of metabolic pathways are affected by the activity of some metabolic enzymes at key nodes in metabolism, including the transport mechanism of carbon sources and the ability to fix carbon dioxide.The established genome-scale metabolic model can be used for model-driven strain design and medium alteration to improve succinic acid yields. link: http://identifiers.org/pubmed/29843739
Parameters: none
States: none
Observables: none
BIOMD0000000874
@ v0.0.1
This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…
DetailsWe examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155
Parameters:
Name | Description |
---|---|
N = 1000.0; mu_b = 0.24 |
Reaction: => V; T_2, Rate Law: COMpartment*N*mu_b*T_2 |
k_1 = 2.4E-5; mu_V = 2.4 |
Reaction: V => ; T, Rate Law: COMpartment*(k_1*V*T+mu_V*V) |
s = 10.0; r = 0.03 |
Reaction: => T, Rate Law: COMpartment*(s+r*T) |
k_1 = 2.4E-5 |
Reaction: => T_1; V, T, Rate Law: COMpartment*k_1*V*T |
mu_T = 0.02; k_2 = 0.003 |
Reaction: T_1 =>, Rate Law: COMpartment*(mu_T*T_1+k_2*T_1) |
k_2 = 0.003 |
Reaction: => T_2; T_1, Rate Law: COMpartment*k_2*T_1 |
k_1 = 2.4E-5; mu_T = 0.02; T_max = 1500.0; r = 0.03 |
Reaction: T => ; V, T_1, T_2, Rate Law: COMpartment*(mu_T*T+k_1*V*T+r*T*(T+T_1+T_2)/T_max) |
mu_b = 0.24 |
Reaction: T_2 =>, Rate Law: COMpartment*mu_b*T_2 |
States:
Name | Description |
---|---|
T |
[P01730] |
T 2 |
[P01730] |
T 1 |
[P01730] |
V |
V |
Observables: none
MODEL1006230093
@ v0.0.1
This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…
DetailsWe examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155
Parameters: none
States: none
Observables: none
MODEL1006230075
@ v0.0.1
This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…
DetailsWe examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155
Parameters: none
States: none
Observables: none
MODEL1006230035
@ v0.0.1
This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…
DetailsWe examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155
Parameters: none
States: none
Observables: none
BIOMD0000000814
@ v0.0.1
This is a model built by COPASI4.24(Build 197) This a model from the article: Computational design of improved standa…
DetailsHere we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model describes the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutic strategies were tried computationally on the 'in-silico twins' of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large trial of 2000 virtual patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances. On the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. We used 220 sets of experiments in-silico to show that a clinical trial incorporating 100 patients per arm (standard intensive treatment versus 5 + 12 scheme) could demonstrate the superiority of the novel scheme after a follow-up period of 10 years. Thus, the proposed treatment plan could be the basis for a standardized TMZ treatment for LGO patients with survival benefits. link: http://identifiers.org/pubmed/31306418
Parameters:
Name | Description |
---|---|
K = 261.799 m^3; kappa = 1.0; rho = 0.002931927433 1/d |
Reaction: Damaged_Tumor_Cells_D => ; Tumor_Cell_Population_P, Rate Law: compartment*rho*Damaged_Tumor_Cells_D*(1-(Tumor_Cell_Population_P+Damaged_Tumor_Cells_D)/K)/kappa |
lambda = 8.3184 1/d |
Reaction: Drug_Concentration_C =>, Rate Law: compartment*lambda*Drug_Concentration_C |
alpha_2 = 0.1396877593 |
Reaction: Tumor_Cell_Population_P => ; Drug_Concentration_C, Rate Law: compartment*alpha_2*Tumor_Cell_Population_P*Drug_Concentration_C |
K = 261.799 m^3; rho = 0.002931927433 1/d |
Reaction: => Tumor_Cell_Population_P; Damaged_Tumor_Cells_D, Rate Law: compartment*rho*Tumor_Cell_Population_P*(1-(Tumor_Cell_Population_P+Damaged_Tumor_Cells_D)/K) |
alpha_1 = 0.1027971308 |
Reaction: Tumor_Cell_Population_P => Damaged_Tumor_Cells_D; Drug_Concentration_C, Rate Law: compartment*alpha_1*Tumor_Cell_Population_P*Drug_Concentration_C |
States:
Name | Description |
---|---|
Tumor Cell Population P |
[cancer] |
Drug Concentration C |
[Chemotherapy; Concentration] |
Damaged Tumor Cells D |
[cancer; Abnormal] |
Observables: none
BIOMD0000000610
@ v0.0.1
Petelenz-kurdzeil2013 - Osmo adaptation gpd1DThis model is described in the article: [Quantitative analysis of glycerol…
DetailsWe provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies. link: http://identifiers.org/pubmed/23762021
Parameters:
Name | Description |
---|---|
volchangespeed = 6.30627442832138E-7 |
Reaction: cin => ; cellvol, Rate Law: intra*cin*volchangespeed/cellvol |
CellSurface = 0.0296468313433281; Turgor = -0.580000000000118; vV_2 = 0.00116532; OsmoE = 0.355586; vV_T = 298.5; vV_R = 8.314; vV_1 = 3.56294E-5 |
Reaction: => cellvol; glycerol_e, glycerol_i, cin, Rate Law: intra*vV_1*CellSurface*(Turgor-vV_2*vV_R*vV_T*((glycerol_e+OsmoE)-(glycerol_i+cin))) |
kv7_2 = 0.317879; kv7_1 = 0.00983997 |
Reaction: trioseP => pyruvate, Rate Law: intra*kv7_1*trioseP/(kv7_2+trioseP) |
kv23r_1 = 2.09875E-4 |
Reaction: AOG => AOGi, Rate Law: intra*kv23r_1*AOG |
v10speed = 1.30212691282784E-11; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 |
Reaction: => trehalose_e, Rate Law: extra*(v10speed*cellnum/initcellnum-v10speed) |
v13aspeed = 5.0275596254809E-8; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 |
Reaction: => glycerol_e, Rate Law: extra*(v13aspeed*cellnum/initcellnum-v13aspeed) |
v1speed = 5.33353293880484E-7; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 |
Reaction: glucose_e =>, Rate Law: extra*(v1speed*cellnum/initcellnum-v1speed) |
kv18r_1 = 1.32549E-4 |
Reaction: Gpd1 =>, Rate Law: intra*kv18r_1*Gpd1 |
kv16r_1VARIABLE = 0.444296 |
Reaction: Hog1PP => Hog1, Rate Law: intra*kv16r_1VARIABLE*Hog1PP |
kv13a_1 = 6.28899E-6; CellSurface = 0.0296468313433281; kDiff=1.0 |
Reaction: glycerol_i => glycerol_e; Fps1r, Rate Law: kv13a_1*CellSurface*Fps1r*(glycerol_i-kDiff*glycerol_e) |
kv20r_1 = 7.05933E-4 |
Reaction: stl1mRNA =>, Rate Law: intra*kv20r_1*stl1mRNA |
kv13b_2 = 3.69196E-7; kv13b_1 = 1.27001E-7 |
Reaction: glycerol_e => glycerol_i; Stl1, Rate Law: glycerol_e*kv13b_1*Stl1/(kv13b_2+glycerol_e) |
kv4_3 = 0.00171631; kv4_1 = 0.0628885; kv4_4 = 2.67143; kv4_2 = 0.00230714; kv4_5 = 0.583865 |
Reaction: G6P => F16DP; F26DP, Rate Law: intra*(kv4_2*(1-F26DP^kv4_5/(F26DP+kv4_3)^kv4_5)+kv4_1*F26DP^kv4_5/(F26DP+kv4_3)^kv4_5)*(G6P/kv4_4)^2/(1+(G6P/kv4_4)^2) |
kv15r_2 = 3.3187E-5; kv15r_1 = 1.84829E-7 |
Reaction: F26DP => G6P, Rate Law: intra*kv15r_1*F26DP/(kv15r_2+F26DP) |
kv2_1 = 0.00303855; kv2_2 = 0.40864 |
Reaction: glucose_i => G6P, Rate Law: intra*kv2_1*glucose_i/(kv2_2+glucose_i) |
kv19r_1 = 0.0605655 |
Reaction: Pfk2627a => Pfk2627i, Rate Law: intra*kv19r_1*Pfk2627a |
kv22_2 = 0.0215179; Turgor = -0.580000000000118; kv22_1 = 8.0075; kv22_3 = 0.0554729 |
Reaction: => Fps1r; Hog1PP, Rate Law: intra*(kv22_1*(-Turgor)/(kv22_3+(-Turgor))*1.5*(1-Hog1PP/(Hog1PP+kv22_2))-kv22_1*Fps1r) |
kv8_2 = 1.50827; kv8_1 = 0.0135676 |
Reaction: pyruvate => acetate_i, Rate Law: intra*kv8_1*pyruvate/(kv8_2+pyruvate) |
Vm = 4.80000000000001E-4; kv16f_3 = 14.9448; OsmoE = 0.355586; kv16f_1 = 0.156118; kv16f_2 = 4.52424E-4 |
Reaction: Hog1 => Hog1PP, Rate Law: intra*Hog1*kv16f_1*OsmoE*(kv16f_2/Vm)^kv16f_3 |
kv19f_1 = 0.299127 |
Reaction: Pfk2627i => Pfk2627a; Hog1PP, Rate Law: intra*kv19f_1*Hog1PP*Pfk2627i |
v11speed = 9.21581643247704E-8; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 |
Reaction: => acetate_e, Rate Law: extra*(v11speed*cellnum/initcellnum-v11speed) |
kv18f_1 = 0.00646553 |
Reaction: => Gpd1; gpd1mRNA, Rate Law: intra*kv18f_1*gpd1mRNA |
kv23f_1 = 8.80535E-6; Vm = 4.80000000000001E-4; kv23f_3 = 6.95727; kv23f_2 = 5.1235E-4 |
Reaction: AOGi => AOG, Rate Law: intra*AOGi*kv23f_1*(kv23f_2/Vm)^kv23f_3 |
kv1_2 = 0.899814; kv1_1 = 5.05249E-6 |
Reaction: glucose_e => glucose_i, Rate Law: kv1_1*glucose_e/(kv1_2+glucose_e) |
kv5_1 = 0.00383315; kv5_2 = 1.74463; kv5_3 = 0.00656128; kv5_4 = 1.13994 |
Reaction: F16DP => trioseP, Rate Law: intra*(kv5_1*F16DP/kv5_2/(1+F16DP/kv5_2)-kv5_3*trioseP/kv5_4/(1+trioseP/kv5_4)) |
kv17r_1 = 0.00151498 |
Reaction: gpd1mRNA =>, Rate Law: intra*kv17r_1*gpd1mRNA |
kv9_1 = 0.214937; kv9_2 = 0.923665 |
Reaction: pyruvate => ethanol_i, Rate Law: intra*kv9_1*pyruvate/(kv9_2+pyruvate) |
CellSurface = 0.0296468313433281; kv12_2 = 0.148586; kv12_1 = 1.00927E-5 |
Reaction: ethanol_i => ethanol_e, Rate Law: kv12_1*CellSurface*(ethanol_i-kv12_2*ethanol_e) |
kv14_5 = 1.23049; kv14_2 = 6.05922E-6; OsmoE = 0.355586; kv14_4 = 0.420621; kv14_1 = 0.808051; kv14_3 = 2.05157 |
Reaction: G6P => biomass; cellvol, Rate Law: intra*kv14_1*cellvol^kv14_3/(cellvol^kv14_3+kv14_2)*(1-OsmoE/(OsmoE+kv14_4))*G6P/kv14_5/(1+G6P/kv14_5) |
kv20f_3 = 4.05843E-6; kv20f_2 = 0.0167845; kv20f_x = 1.55858; kv20f_1 = 9.81887E-5 |
Reaction: => stl1mRNA; Hog1PP, Rate Law: intra*(kv20f_1*Hog1PP^kv20f_x/(Hog1PP^kv20f_x+kv20f_2)+kv20f_3) |
kv6b_4 = 4.61918E-5; kv6b_x = 28.5; kv6b_5 = 0.292627 |
Reaction: trioseP => glycerol_i, Rate Law: intra*kv6b_x*kv6b_4*trioseP^2/kv6b_5/(1+trioseP^2/kv6b_5) |
initcellnum = 6954722.464; cellnum = 1.11543272115466E7; v13bspeed = 1.59327705289657E-11 |
Reaction: glycerol_e =>, Rate Law: extra*(v13bspeed*cellnum/initcellnum-v13bspeed) |
kv21r_1 = 2.14247E-4 |
Reaction: Stl1 =>, Rate Law: intra*kv21r_1*Stl1 |
kv10_1 = 1.83291E-7; CellSurface = 0.0296468313433281; kv10_2 = 4.26512 |
Reaction: trehalose => trehalose_e, Rate Law: kv10_1*CellSurface*(trehalose-kv10_2*trehalose_e) |
kv15f_2 = 6.95877; kv15f_1 = 4.99507E-5 |
Reaction: G6P => F26DP; Pfk2627a, Rate Law: intra*G6P*kv15f_1*Pfk2627a/(kv15f_2+G6P) |
kv21f_1 = 0.00121673 |
Reaction: => Stl1; stl1mRNA, Rate Law: intra*kv21f_1*stl1mRNA |
kv3_4 = 0.166996; kv3_1 = 6.17387E-6; kv3_3 = 7.37808E-4; kv3_2 = 0.81114 |
Reaction: G6P => trehalose, Rate Law: intra*(kv3_1*G6P/kv3_2-kv3_3*trehalose/kv3_4)/(1+G6P/kv3_2+trehalose/kv3_4) |
CellSurface = 0.0296468313433281; kv11_2 = 1.17279; kv11_1 = 3.2863E-6 |
Reaction: acetate_i => acetate_e, Rate Law: kv11_1*CellSurface*(acetate_i-kv11_2*acetate_e) |
initcellnum = 6954722.464; cellnum = 1.11543272115466E7; v12speed = 2.88652220351019E-6 |
Reaction: => ethanol_e, Rate Law: extra*(v12speed*cellnum/initcellnum-v12speed) |
States:
Name | Description |
---|---|
glucose i |
[glucose; intracellular] |
acetate e |
[acetate] |
trehalose e |
[trehalose] |
gpd1mRNA |
[S000002180] |
Stl1 |
[Sugar transporter STL1] |
Pfk2627i |
[S000005496; S000001369] |
Hog1PP |
[Mitogen-activated protein kinase HOG1] |
stl1mRNA |
[S000002944] |
glucose e |
[glucose] |
F26DP |
[105021] |
glycerol i |
[glycerol] |
pyruvate |
[pyruvate] |
ethanol e |
[ethanol] |
Pfk2627a |
[S000005496; S000001369] |
AOG |
[positive regulation of transcription, DNA-templated] |
acetate i |
[acetate] |
Hog1 |
[Mitogen-activated protein kinase HOG1] |
F16DP |
[keto-D-fructose 1,6-bisphosphate] |
cellvol |
cellvol |
biomass |
biomass |
ethanol i |
[ethanol] |
trioseP |
[4643300; 729] |
G6P |
[alpha-D-glucose 6-phosphate] |
AOGi |
[positive regulation of transcription, DNA-templated] |
Fps1r |
[Glycerol uptake/efflux facilitator protein] |
cin |
[osmolyte] |
glycerol e |
[glycerol] |
trehalose |
[trehalose] |
Gpd1 |
[Glycerol-3-phosphate dehydrogenase [NAD(+)] 1] |
Observables: none
BIOMD0000000613
@ v0.0.1
&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level3/version1/core&quot;&gt; &lt;body xmlns=&quot;http://www.w3.…
DetailsBone biology is physiologically complex and intimately linked to calcium homeostasis. The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research. link: http://identifiers.org/pubmed/19732857
Parameters:
Name | Description |
---|---|
J14 = NaN |
Reaction: Q => P, Rate Law: J14 |
koutRNK = 0.00323667 |
Reaction: RNK => ; RNK, Rate Law: koutRNK*RNK |
k2 = 0.112013 |
Reaction: N => ; N, Rate Law: k2*N |
kinOC2 = NaN |
Reaction: => OC, Rate Law: kinOC2 |
kbslow = NaN |
Reaction: OBslow => ; OBslow, Rate Law: kbslow*OBslow |
k3 = 6.24E-6 |
Reaction: L + RNK => M; RNK, L, Rate Law: k3*RNK*L |
pO = NaN |
Reaction: => O, Rate Law: pO |
TERIPK = NaN |
Reaction: TERISC => PTH, Rate Law: TERIPK |
koutL = 0.00293273 |
Reaction: L => ; L, Rate Law: koutL*L |
kO = 15.8885 |
Reaction: O => ; O, Rate Law: kO*O |
crebKout = 0.00279513 |
Reaction: CREB => ; CREB, Rate Law: crebKout*CREB |
T76 = NaN |
Reaction: => S; S, Rate Law: (1-S)*T76 |
J48 = NaN |
Reaction: ECCPhos =>, Rate Law: J48 |
RX2Kout = NaN |
Reaction: RX2 => ; RX2, Rate Law: RX2Kout*RX2 |
RX2Kin = NaN |
Reaction: => RX2, Rate Law: RX2Kin |
kout = NaN |
Reaction: PTH => ; PTH, Rate Law: kout*PTH |
J40 = NaN |
Reaction: T => P, Rate Law: J40 |
J53 = NaN |
Reaction: PhosGut => ECCPhos, Rate Law: J53 |
OralCa = NaN; F11 = NaN |
Reaction: => T, Rate Law: OralCa*F11 |
KPT = NaN |
Reaction: ROB1 => ; ROB1, Rate Law: KPT*ROB1 |
crebKin = NaN |
Reaction: => CREB, Rate Law: crebKin |
F12 = 0.7; OralPhos = NaN |
Reaction: => PhosGut, Rate Law: OralPhos*F12 |
IPTHint = 0.0 |
Reaction: => SC, Rate Law: IPTHint |
koutTGFact = NaN |
Reaction: TGFBact => ; TGFBact, Rate Law: koutTGFact*TGFBact |
SPTH = NaN |
Reaction: => PTH, Rate Law: SPTH |
J14a = NaN |
Reaction: Qbone => Q, Rate Law: J14a |
J15 = NaN |
Reaction: P => Q, Rate Law: J15 |
D = NaN; FracOBfast = 0.797629; Frackb = 0.313186; PicOB = NaN; bigDb = NaN |
Reaction: => OBslow, Rate Law: bigDb/PicOB*D*(1-FracOBfast)*Frackb |
J42 = NaN |
Reaction: ECCPhos =>, Rate Law: J42 |
koutTGFeqn = NaN |
Reaction: TGFB => TGFBact, Rate Law: koutTGFeqn |
kinL = NaN |
Reaction: => L, Rate Law: kinL |
J41 = NaN |
Reaction: => ECCPhos, Rate Law: J41 |
kbfast = NaN |
Reaction: OBfast => ; OBfast, Rate Law: kbfast*OBfast |
J27 = NaN |
Reaction: P =>, Rate Law: J27 |
SE = NaN |
Reaction: => A, Rate Law: SE |
J15a = NaN |
Reaction: Q => Qbone, Rate Law: J15a |
PTin = NaN |
Reaction: => PTmax, Rate Law: PTin |
PTout = 1.604E-4 |
Reaction: PTmax => ; PTmax, Rate Law: PTout*PTmax |
T64 = 0.05 |
Reaction: A => ; A, Rate Law: T64*A |
kinRNKgam = 0.151825; kinRNK = NaN |
Reaction: => RNK; TGFBact, TGFBact, Rate Law: kinRNK*TGFBact^kinRNKgam |
kLShap = NaN |
Reaction: HAp => ; HAp, Rate Law: kLShap*HAp |
T36 = NaN |
Reaction: => R; R, Rate Law: T36*(1-R) |
T37 = NaN |
Reaction: R => ; R, Rate Law: T37*R |
ROBin = NaN |
Reaction: => ROB1, Rate Law: ROBin |
J56 = NaN |
Reaction: IntraPO => ECCPhos, Rate Law: J56 |
k4 = 0.112013 |
Reaction: M => L + RNK; M, Rate Law: k4*M |
D = NaN; FracOBfast = 0.797629; PicOB = NaN; bigDb = NaN; Frackb2 = NaN |
Reaction: => OBfast, Rate Law: bigDb/PicOB*D*FracOBfast*Frackb2 |
J54 = NaN |
Reaction: ECCPhos => IntraPO, Rate Law: J54 |
bcl2Kout = 0.693 |
Reaction: BCL2 => ; BCL2, Rate Law: bcl2Kout*BCL2 |
k1 = 6.24E-6 |
Reaction: => N; O, L, O, L, Rate Law: k1*O*L |
T69 = 0.1 |
Reaction: B => ; B, Rate Law: T69*B |
Osteoblast = NaN; kinTGF = NaN; OB0 = NaN; OBtgfGAM = 0.0111319 |
Reaction: => TGFB, Rate Law: kinTGF*(Osteoblast/OB0)^OBtgfGAM |
Osteoblast = NaN; kHApIn = NaN |
Reaction: => HAp, Rate Law: kHApIn*Osteoblast |
T75 = NaN |
Reaction: S => ; S, Rate Law: S*T75 |
bcl2Kin = NaN |
Reaction: => BCL2, Rate Law: bcl2Kin |
KLSoc = NaN |
Reaction: OC => ; OC, Rate Law: KLSoc*OC |
States:
Name | Description |
---|---|
Q |
[calcium(2+); intracellular] |
TGFB |
[Transforming growth factor beta-1] |
IntraPO |
[phosphate ion] |
T |
[calcium(2+)] |
RNK |
[Tumor necrosis factor receptor superfamily member 11A] |
P |
[calcium(2+)] |
L |
[Tumor necrosis factor ligand superfamily member 11] |
PTH |
[Parathyroid hormone] |
OC |
[osteoclast] |
O |
[Tumor necrosis factor receptor superfamily member 11B] |
TGFBact |
[Transforming growth factor beta-1; active] |
B |
[calcitriol] |
M |
[protein complex; Tumor necrosis factor ligand superfamily member 11; Tumor necrosis factor receptor superfamily member 11A] |
N |
[protein complex; Tumor necrosis factor ligand superfamily member 11; Tumor necrosis factor receptor superfamily member 11B] |
ECCPhos |
[phosphate ion] |
A |
[25-hydroxyvitamin D-1 alpha hydroxylase, mitochondrial] |
CREB |
[Cyclic AMP-responsive element-binding protein 1] |
SC |
[subcutaneous adipose tissue; Parathyroid hormone; pharmaceutical] |
RX2 |
[Runt-related transcription factor 2] |
BCL2 |
[Apoptosis regulator Bcl-2] |
TERISC |
[16132393] |
OBslow |
[osteoblast] |
PTmax |
[Parathyroid hormone] |
S |
[Parathyroid hormone] |
OBfast |
[osteoblast] |
Qbone |
[calcium(2+); extracellular region] |
HAp |
[apatite] |
ROB1 |
[osteoclast; urn:miriam:pato:PATO%3A0000487+] |
R |
[intestine; calcium(2+)] |
PhosGut |
[phosphate ion] |
Observables: none
MODEL1910240001
@ v0.0.1
C-547, a candidate drug, is a potent slow-binding inhibitor of acetyl-cholinesterase, and the focus of this PK/PD model,…
DetailsC-547, a potent slow-binding inhibitor of acetylcholinesterase (AChE) was intravenously administered to rat (0.05 mg/kg). Pharmacokinetic profiles were determined in blood and different organs: extensor digitorum longus muscle, heart, liver, lungs and kidneys as a function of time. Pharmacokinetics (PK) was studied using non-compartmental and compartmental analyses. A 3-compartment model describes PK in blood. Most of injected C-547 binds to albumin in the bloodstream. The steady-state volume of distribution (3800 ml/kg) is 15 times larger than the distribution volume, indicating a good tissue distribution. C-547 is slowly eliminated (kel = 0.17 h-1; T1/2 = 4 h) from the bloodstream. Effect of C-547 on animal model of myasthenia gravis persists for more than 72 h, even though the drug is not analytically detectable in the blood. A PK/PD model was built to account for such a pharmacodynamical (PD) effect. Long-lasting effect results from micro-PD mechanisms: the slow-binding nature of inhibition, high affinity for AChE and long residence time on target at neuromuscular junction (NMJ). In addition, NMJ spatial constraints i.e. high concentration of AChE in a small volume, and slow diffusion rate of free C-547 out of NMJ, make possible effective rebinding of ligand. Thus, compared to other cholinesterase inhibitors used for palliative treatment of myasthenia gravis, C-547 is the most selective drug, displays a slow pharmacokinetics, and has the longest duration of action. This makes C-547 a promising drug leader for treatment of myasthenia gravis, and a template for development of other drugs against neurological diseases and for neuroprotection. link: http://identifiers.org/pubmed/29277489
Parameters: none
States: none
Observables: none
MODEL1612020000
@ v0.0.1
Peyraud2016 - Metabolic reconstruction (iRP1476) of Ralstonia solanacearum GMI1000This model is described in the article…
DetailsBacterial pathogenicity relies on a proficient metabolism and there is increasing evidence that metabolic adaptation to exploit host resources is a key property of infectious organisms. In many cases, colonization by the pathogen also implies an intensive multiplication and the necessity to produce a large array of virulence factors, which may represent a significant cost for the pathogen. We describe here the existence of a resource allocation trade-off mechanism in the plant pathogen R. solanacearum. We generated a genome-scale reconstruction of the metabolic network of R. solanacearum, together with a macromolecule network module accounting for the production and secretion of hundreds of virulence determinants. By using a combination of constraint-based modeling and metabolic flux analyses, we quantified the metabolic cost for production of exopolysaccharides, which are critical for disease symptom production, and other virulence factors. We demonstrated that this trade-off between virulence factor production and bacterial proliferation is controlled by the quorum-sensing-dependent regulatory protein PhcA. A phcA mutant is avirulent but has a better growth rate than the wild-type strain. Moreover, a phcA mutant has an expanded metabolic versatility, being able to metabolize 17 substrates more than the wild-type. Model predictions indicate that metabolic pathways are optimally oriented towards proliferation in a phcA mutant and we show that this enhanced metabolic versatility in phcA mutants is to a large extent a consequence of not paying the cost for virulence. This analysis allowed identifying candidate metabolic substrates having a substantial impact on bacterial growth during infection. Interestingly, the substrates supporting well both production of virulence factors and growth are those found in higher amount within the plant host. These findings also provide an explanatory basis to the well-known emergence of avirulent variants in R. solanacearum populations in planta or in stressful environments. link: http://identifiers.org/pubmed/27732672
Parameters: none
States: none
Observables: none
BIOMD0000000337
@ v0.0.1
This model is from the article: Cooperation and Competition in the Evolution of ATP-Producing Pathways Thomas Pfeiff…
DetailsHeterotrophic organisms generally face a trade-off between rate and yield of adenosine triphosphate (ATP) production. This trade-off may result in an evolutionary dilemma, because cells with a higher rate but lower yield of ATP production may gain a selective advantage when competing for shared energy resources. Using an analysis of model simulations and biochemical observations, we show that ATP production with a low rate and high yield can be viewed as a form of cooperative resource use and may evolve in spatially structured environments. Furthermore, we argue that the high ATP yield of respiration may have facilitated the evolutionary transition from unicellular to undifferentiated multicellular organisms. link: http://identifiers.org/pubmed/11283355
Parameters:
Name | Description |
---|---|
v = 10.0 dimensionless |
Reaction: => S, Rate Law: v |
d = 1.0 dimensionless |
Reaction: N1 =>, Rate Law: d*N1 |
States:
Name | Description |
---|---|
S |
[energy] |
N1 |
[cell] |
N2 |
[cell] |
Observables: none
BIOMD0000000748
@ v0.0.1
The paper describes a model on the key components for tumor–immune dynamics in multiple myeloma. Created by COPASI 4.2…
DetailsThe complexity of the immune responses is a major challenge in current virotherapy. This study incorporates the innate immune response into our basic model for virotherapy and investigates how the innate immunity affects the outcome of virotherapy. The viral therapeutic dynamics is largely determined by the viral burst size, relative innate immune killing rate, and relative innate immunity decay rate. The innate immunity may complicate virotherapy in the way of creating more equilibria when the viral burst size is not too big, while the dynamics is similar to the system without innate immunity when the viral burst size is big. link: http://identifiers.org/pubmed/29379572
Parameters:
Name | Description |
---|---|
c = 0.48 1 |
Reaction: y => ; z, Rate Law: tumor_microenvironment*c*y*z |
e = 0.2 1 |
Reaction: v =>, Rate Law: tumor_microenvironment*e*v |
m = 0.6 1 |
Reaction: => z; y, Rate Law: tumor_microenvironment*m*y*z |
a = 0.11 1 |
Reaction: x + v => y, Rate Law: tumor_microenvironment*a*x*v |
d = 0.16 1 |
Reaction: v => ; z, Rate Law: tumor_microenvironment*d*v*z |
n = 0.036 1 |
Reaction: z =>, Rate Law: tumor_microenvironment*n*z |
r = 0.36 1 |
Reaction: => x, Rate Law: tumor_microenvironment*r*x |
b = 9.0 1 |
Reaction: => v; y, Rate Law: tumor_microenvironment*b*y |
States:
Name | Description |
---|---|
v |
[Oncolytic Virus] |
x |
[neoplastic cell] |
z |
[Effector Immune Cell] |
y |
[neoplastic cell] |
Observables: none
BIOMD0000000692
@ v0.0.1
Phillips2003 - The Mechanism of Ras GTPase Activation by NeurofibrominA mathematical model for Ras-GTP activation by neu…
DetailsIndividual rate constants have been determined for each step of the Ras.GTP hydrolysis mechanism, activated by neurofibromin. Fluorescence intensity and anisotropy stopped-flow measurements used the fluorescent GTP analogue, mantGTP (2'(3')-O-(N-methylanthraniloyl)GTP), to determine rate constants for binding and release of neurofibromin. Quenched flow measurements provided the kinetics of the hydrolytic cleavage step. The fluorescent phosphate sensor, MDCC-PBP was used to measure phosphate release kinetics. Phosphate-water oxygen exchange, using (18)O-substituted GTP and inorganic phosphate (P(i)), was used to determine the extent of reversal of the hydrolysis step and of P(i) binding. The data show that neurofibromin and P(i) dissociate from the NF1.Ras.GDP.P(i) complex with identical kinetics, which are 3-fold slower than the preceding cleavage step. A model is presented in which the P(i) release is associated with the change of Ras from "GTP" to "GDP" conformation. In this model, the conformation change on P(i) release causes the large change in affinity of neurofibromin, which then dissociates rapidly. link: http://identifiers.org/pubmed/12667087
Parameters:
Name | Description |
---|---|
kf=1.02102E-11; kb=1.15192E-13 |
Reaction: RasGTP_minus_NF1_star_ => RasGDP_minus_NF1_Pi, Rate Law: geometry*(kf*RasGTP_minus_NF1_star_-kb*RasGDP_minus_NF1_Pi)/geometry |
kb=2.8798E-12; kf=2.18865E-10 |
Reaction: RasGTP_minus_NF1 => RasGTP_minus_NF1_star_, Rate Law: geometry*(kf*RasGTP_minus_NF1-kb*RasGTP_minus_NF1_star_)/geometry |
kb=5.65482E-17; kf=2.0944E-11 |
Reaction: RasGDP_minus_NF1_Pi => Pi + RasGDP_NF1, Rate Law: geometry*(kf*RasGDP_minus_NF1_Pi-kb*Pi*RasGDP_NF1)/geometry |
kf=6.28318E-13; kb=3.3301E-12 |
Reaction: RasGTP + NF1 => RasGTP_minus_NF1, Rate Law: geometry*(kf*RasGTP*NF1-kb*RasGTP_minus_NF1)/geometry |
kb=6.28318E-13; kf=2.43474E-11 |
Reaction: RasGDP_NF1 => RasGDP + NF1, Rate Law: geometry*(kf*RasGDP_NF1-kb*RasGDP*NF1)/geometry |
States:
Name | Description |
---|---|
RasGDP |
[GDP; 43873] |
RasGDP minus NF1 Pi |
[GDP; inorganic phosphate; K08052; 43873] |
RasGDP NF1 |
[K08052; GDP; 43873] |
Pi |
[inorganic phosphate] |
NF1 |
[K08052] |
RasGTP |
[GTP; 43873] |
RasGTP minus NF1 |
[K08052; GTP; 43873] |
RasGTP minus NF1 star |
[K08052; GTP; 43873] |
Observables: none
BIOMD0000000917
@ v0.0.1
This a model from the article: A quantitative model of sleep-wake dynamics based on the physiology of the brainstem as…
DetailsA quantitative, physiology-based model of the ascending arousal system is developed, using continuum neuronal population modeling, which involves averaging properties such as firing rates across neurons in each population. The model includes the ventrolateral preoptic area (VLPO), where circadian and homeostatic drives enter the system, the monoaminergic and cholinergic nuclei of the ascending arousal system, and their interconnections. The human sleep-wake cycle is governed by the activities of these nuclei, which modulate the behavioral state of the brain via diffuse neuromodulatory projections. The model parameters are not free since they correspond to physiological observables. Approximate parameter bounds are obtained by requiring consistency with physiological and behavioral measures, and the model replicates the human sleep-wake cycle, with physiologically reasonable voltages and firing rates. Mutual inhibition between the wake-promoting monoaminergic group and sleep-promoting VLPO causes ;;flip-flop'' behavior, with most time spent in 2 stable steady states corresponding to wake and sleep, with transitions between them on a timescale of a few minutes. The model predicts hysteresis in the sleep-wake cycle, with a region of bistability of the wake and sleep states. Reducing the monoaminergic-VLPO mutual inhibition results in a smaller hysteresis loop. This makes the model more prone to wake-sleep transitions in both directions and makes the states less distinguishable, as in narcolepsy. The model behavior is robust across the constrained parameter ranges, but with sufficient flexibility to describe a wide range of observed phenomena. link: http://identifiers.org/pubmed/17440218
Parameters:
Name | Description |
---|---|
chi = 10.8; Qm = 4.74258731775668; mu = 3.6 |
Reaction: => Somnogen_level_H, Rate Law: COMpartment*(mu*Qm-Somnogen_level_H)/chi |
Qm = 4.74258731775668; tau_v = 10.0; D = -10.7; v_vm = -1.9 |
Reaction: => Ventrolateral_preopticarea__VLPO__voltage, Rate Law: COMpartment*((v_vm*Qm+D)-Ventrolateral_preopticarea__VLPO__voltage)/(tau_v/3600) |
v_mv = -1.9; Qv = 0.127101626308136; v_maQao = 1.0; tau_m = 10.0 |
Reaction: => Monoaminergic__MA__voltage, Rate Law: COMpartment*((v_maQao+v_mv*Qv)-Monoaminergic__MA__voltage)/(tau_m/3600) |
States:
Name | Description |
---|---|
Ventrolateral preopticarea VLPO voltage |
[OMIT_0027571; OMIT_0026787; Signal; C70813] |
Somnogen level H |
[C207] |
Monoaminergic MA voltage |
[C70813; OMIT_0026787; C73238; C62025; Signal; C2321] |
Observables: none
MODEL1006230110
@ v0.0.1
This a model from the article: Sleep deprivation in a quantitative physiologically based model of the ascending arousa…
DetailsA physiologically based quantitative model of the human ascending arousal system is used to study sleep deprivation after being calibrated on a small set of experimentally based criteria. The model includes the sleep-wake switch of mutual inhibition between nuclei which use monoaminergic neuromodulators, and the ventrolateral preoptic area. The system is driven by the circadian rhythm and sleep homeostasis. We use a small number of experimentally derived criteria to calibrate the model for sleep deprivation, then investigate model predictions for other experiments, demonstrating the scope of application. Calibration gives an improved parameter set, in which the form of the homeostatic drive is better constrained, and its weighting relative to the circadian drive is increased. Within the newly constrained parameter ranges, the model predicts repayment of sleep debt consistent with experiment in both quantity and distribution, asymptoting to a maximum repayment for very long deprivations. Recovery is found to depend on circadian phase, and the model predicts that it is most efficient to recover during normal sleeping phases of the circadian cycle, in terms of the amount of recovery sleep required. The form of the homeostatic drive suggests that periods of wake during recovery from sleep deprivation are phases of relative recovery, in the sense that the homeostatic drive continues to converge toward baseline levels. This undermines the concept of sleep debt, and is in agreement with experimentally restricted recovery protocols. Finally, we compare our model to the two-process model, and demonstrate the power of physiologically based modeling by correctly predicting sleep latency times following deprivation from experimental data. link: http://identifiers.org/pubmed/18805427
Parameters: none
States: none
Observables: none
MODEL1006230115
@ v0.0.1
This a model from the article: Sleep deprivation in a quantitative physiologically based model of the ascending arousa…
DetailsA physiologically based quantitative model of the human ascending arousal system is used to study sleep deprivation after being calibrated on a small set of experimentally based criteria. The model includes the sleep-wake switch of mutual inhibition between nuclei which use monoaminergic neuromodulators, and the ventrolateral preoptic area. The system is driven by the circadian rhythm and sleep homeostasis. We use a small number of experimentally derived criteria to calibrate the model for sleep deprivation, then investigate model predictions for other experiments, demonstrating the scope of application. Calibration gives an improved parameter set, in which the form of the homeostatic drive is better constrained, and its weighting relative to the circadian drive is increased. Within the newly constrained parameter ranges, the model predicts repayment of sleep debt consistent with experiment in both quantity and distribution, asymptoting to a maximum repayment for very long deprivations. Recovery is found to depend on circadian phase, and the model predicts that it is most efficient to recover during normal sleeping phases of the circadian cycle, in terms of the amount of recovery sleep required. The form of the homeostatic drive suggests that periods of wake during recovery from sleep deprivation are phases of relative recovery, in the sense that the homeostatic drive continues to converge toward baseline levels. This undermines the concept of sleep debt, and is in agreement with experimentally restricted recovery protocols. Finally, we compare our model to the two-process model, and demonstrate the power of physiologically based modeling by correctly predicting sleep latency times following deprivation from experimental data. link: http://identifiers.org/pubmed/18805427
Parameters: none
States: none
Observables: none
MODEL1704190000
@ v0.0.1
Phosphatase activities on PI(3,4,5)P3 and PI(3,4)P2This model describes the action of various phosphatases on PI(3,4,5)P…
DetailsThe PI3K signaling pathway regulates cell growth and movement and is heavily mutated in cancer. Class I PI3Ks synthesize the lipid messenger PI(3,4,5)P3. PI(3,4,5)P3 can be dephosphorylated by 3- or 5-phosphatases, the latter producing PI(3,4)P2. The PTEN tumor suppressor is thought to function primarily as a PI(3,4,5)P3 3-phosphatase, limiting activation of this pathway. Here we show that PTEN also functions as a PI(3,4)P2 3-phosphatase, both in vitro and in vivo. PTEN is a major PI(3,4)P2 phosphatase in Mcf10a cytosol, and loss of PTEN and INPP4B, a known PI(3,4)P2 4-phosphatase, leads to synergistic accumulation of PI(3,4)P2, which correlated with increased invadopodia in epidermal growth factor (EGF)-stimulated cells. PTEN deletion increased PI(3,4)P2 levels in a mouse model of prostate cancer, and it inversely correlated with PI(3,4)P2 levels across several EGF-stimulated prostate and breast cancer lines. These results point to a role for PI(3,4)P2 in the phenotype caused by loss-of-function mutations or deletions in PTEN. link: http://identifiers.org/doi/10.1016/j.molcel.2017.09.024
Parameters: none
States: none
Observables: none
BIOMD0000000257
@ v0.0.1
This is the self maintaining metabolism model described in the article: A Simple Self-Maintaining Metabolic System:…
DetailsA living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. We show here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation. As long as at least one catalyst is present at a sufficient concentration in the initial state, the others can be produced and maintained. The system shows bistability, because if the amount of catalyst in the initial state is insufficient to reach the non-trivial steady state the system collapses to a trivial steady state in which all fluxes are zero. It is also robust, because if one catalyst is catastrophically lost when the system is in steady state it can recreate the same state. There are three elementary flux modes, but none of them is an enzyme-maintaining mode, the entire network being necessary to maintain the two catalysts. link: http://identifiers.org/pubmed/20700491
Parameters:
Name | Description |
---|---|
k10r = 0.05 per_time_per_M; k10 = 0.05 per_time |
Reaction: STUSU => STU + SU, Rate Law: env*(k10*STUSU-k10r*STU*SU) |
k2 = 10.0 per_time_per_M; k2r = 10.0 per_time |
Reaction: T + STUS => STUST, Rate Law: env*(k2*T*STUS-k2r*STUST) |
k6r = 1.0 per_time; k6 = 1.0 per_time_per_M |
Reaction: U + SUST => SUSTU, Rate Law: env*(k6*U*SUST-k6r*SUSTU) |
k1r = 10.0 per_time; k1 = 10.0 per_time_per_M |
Reaction: S + STU => STUS, Rate Law: env*(k1*S*STU-k1r*STUS) |
k5 = 1.0 per_time_per_M; k5r = 1.0 per_time |
Reaction: SU + ST => SUST, Rate Law: env*(k5*ST*SU-k5r*SUST) |
k4 = 0.3 per_time |
Reaction: STU =>, Rate Law: env*k4*STU |
k3 = 2.0 per_time; k3r = 1.0 per_time_per_M |
Reaction: STUST => ST + STU, Rate Law: env*(k3*STUST-k3r*ST*STU) |
k9 = 0.1 per_time_per_M; k9r = 0.05 per_time |
Reaction: U + STUS => STUSU, Rate Law: env*(k9*U*STUS-k9r*STUSU) |
k11 = NaN per_time |
Reaction: ST =>, Rate Law: env*k11*ST |
k8 = NaN per_time |
Reaction: SU =>, Rate Law: env*k8*SU |
k7 = 0.1 per_time; k7r = 0.1 per_time_per_M |
Reaction: SUSTU => STU + SU, Rate Law: env*(k7*SUSTU-k7r*STU*SU) |
States:
Name | Description |
---|---|
STUST |
STUST |
T |
T |
SUST |
SUST |
SU |
SU |
ST |
ST |
S |
S |
U |
U |
STUSU |
STUSU |
SUSTU |
SUSTU |
STUS |
STUS |
STU |
STU |
Observables: none
MODEL1507180036
@ v0.0.1
Pinchuck2010 - Genome-scale metabolic network of Shewanella oneidensis (iSO783)This model is described in the article:…
DetailsShewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level. link: http://identifiers.org/pubmed/20589080
Parameters: none
States: none
Observables: none
MODEL1302010038
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Ashbya gossypii using CoReCoThis model was reconstructed with the CoReCo meth…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010012
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Aspergillus clavatus using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010024
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Aspergillus fumigatus using CoReCoThis model was reconstructed with the CoReC…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010005
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Aspergillus nidulans using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010017
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Aspergillus niger using CoReCoThis model was reconstructed with the CoReCo me…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010008
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Aspergillus oryzae using CoReCoThis model was reconstructed with the CoReCo m…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010015
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Aspergillus terreus using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010042
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Batrachochytrium dendrobatidis using CoReCoThis model was reconstructed with…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010027
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Botrytis cinerea using CoReCoThis model was reconstructed with the CoReCo met…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010046
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Candida albicans using CoReCoThis model was reconstructed with the CoReCo met…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010028
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Candida glabrata using CoReCoThis model was reconstructed with the CoReCo met…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010037
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Candida lusitaniae using CoReCoThis model was reconstructed with the CoReCo m…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010003
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Candida tropicalis using CoReCoThis model was reconstructed with the CoReCo m…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010002
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Chaetomium globosum using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010007
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Coccidioides immitis using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010006
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Coprinus cinereus using CoReCoThis model was reconstructed with the CoReCo me…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010039
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Cryptococcus neoformans using CoReCoThis model was reconstructed with the CoR…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010023
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Debaryomyces hansenii using CoReCoThis model was reconstructed with the CoReC…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010030
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Encephalitozoon cuniculi using CoReCoThis model was reconstructed with the Co…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010026
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Fusarium graminearum using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010014
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Fusarium oxysporum using CoReCoThis model was reconstructed with the CoReCo m…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010001
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Fusarium verticillioides using CoReCoThis model was reconstructed with the Co…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010022
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Histoplasma capsulatum using CoReCoThis model was reconstructed with the CoRe…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010011
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Kluyveromyces lactis using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010041
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Laccaria bicolor using CoReCoThis model was reconstructed with the CoReCo met…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010033
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Lodderomyces elongisporus using CoReCoThis model was reconstructed with the C…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010009
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Magnaporthe grisea using CoReCoThis model was reconstructed with the CoReCo m…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010031
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Mycosphaerella graminicola using CoReCoThis model was reconstructed with the…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010020
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Nectria haematococca using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010047
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Neosartorya fischeri using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010040
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Neurospora crassa using CoReCoThis model was reconstructed with the CoReCo me…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010000
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Phaeosphaeria nodorum using CoReCoThis model was reconstructed with the CoReC…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010025
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Phanerochaete chrysosporium using CoReCoThis model was reconstructed with the…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010010
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Phycomyces blakesleeanus using CoReCoThis model was reconstructed with the Co…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010004
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Pichia guilliermondii using CoReCoThis model was reconstructed with the CoReC…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010048
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Pichia pastoris using CoReCoThis model was reconstructed with the CoReCo meth…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010043
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Pichia stipitis using CoReCoThis model was reconstructed with the CoReCo meth…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010032
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Postia placenta using CoReCoThis model was reconstructed with the CoReCo meth…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010045
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Puccinia graminis using CoReCoThis model was reconstructed with the CoReCo me…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010018
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Rhizopus oryzae using CoReCoThis model was reconstructed with the CoReCo meth…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010029
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Saccharomyces cerevisiae using CoReCoThis model was reconstructed with the Co…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010021
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Schizosaccharomyces japonicus using CoReCoThis model was reconstructed with t…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010035
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Schizosaccharomyces pombe using CoReCoThis model was reconstructed with the C…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010034
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Sclerotinia sclerotiorum using CoReCoThis model was reconstructed with the Co…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010036
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Sporobolomyces roseus using CoReCoThis model was reconstructed with the CoReC…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010019
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Trichoderma reesei using CoReCoThis model was reconstructed with the CoReCo m…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010044
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Uncinocarpus reesii using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010016
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Ustilago maydis using CoReCoThis model was reconstructed with the CoReCo meth…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
MODEL1302010013
@ v0.0.1
Pitkanen2014 - Metabolic reconstruction of Yarrowia lipolytica using CoReCoThis model was reconstructed with the CoReCo…
DetailsWe introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375
Parameters: none
States: none
Observables: none
BIOMD0000000304
@ v0.0.1
This a model from the article: Bifurcation and resonance in a model for bursting nerve cells. Plant RE J Math Biol…
DetailsIn this paper we consider a model for the phenomenon of bursting in nerve cells. Experimental evidence indicates that this phenomenon is due to the interaction of multiple conductances with very different kinetics, and the model incorporates this evidence. As a parameter is varied the model undergoes a transition between two oscillatory waveforms; a corresponding transition is observed experimentally. After establishing the periodicity of the subcritical oscillatory solution, the nature of the transition is studied. It is found to be a resonance bifurcation, with the solution branching at the critical point to another periodic solution of the same period. Using this result a comparison is made between the model and experimental observations. The model is found to predict and allow an interpretation of these observations. link: http://identifiers.org/pubmed/7252375
Parameters:
Name | Description |
---|---|
tau_n = NaN; n_infinity = NaN |
Reaction: n1 = (n_infinity-n1)/tau_n, Rate Law: (n_infinity-n1)/tau_n |
tau_h = NaN; h_infinity = NaN |
Reaction: h1 = (h_infinity-h1)/tau_h, Rate Law: (h_infinity-h1)/tau_h |
f = 3.0E-4; V_Ca = 140.0; K_c = 0.0085 |
Reaction: c = f*(K_c*x1*(V_Ca-V_membrane)-c), Rate Law: f*(K_c*x1*(V_Ca-V_membrane)-c) |
i_Na = NaN; i_K_Ca = NaN; i_K = NaN; i_Ca = NaN; i_L = NaN |
Reaction: V_membrane = i_Na+i_Ca+i_K+i_K_Ca+i_L, Rate Law: i_Na+i_Ca+i_K+i_K_Ca+i_L |
x_infinity = NaN; tau_x = 235.0 |
Reaction: x1 = (x_infinity-x1)/tau_x, Rate Law: (x_infinity-x1)/tau_x |
States:
Name | Description |
---|---|
h1 |
[sodium(1+)] |
x1 |
[calcium(2+)] |
c |
[calcium(2+)] |
V membrane |
[membrane potential] |
n1 |
[potassium(1+)] |
Observables: none
MODEL1007060000
@ v0.0.1
This is the genome-scale metabolic network of Plasmodium falciparum described in the article: Reconstruction and flux-…
DetailsGenome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme-gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor. link: http://identifiers.org/pubmed/20823846
Parameters: none
States: none
Observables: none
MODEL1807190001
@ v0.0.1
Mathematical model of platelet intracellular signaling network
DetailsBlood platelets need to undergo activation to carry out their function of stopping bleeding. Different activation degrees lead to a stepped hierarchy of responses: ability to aggregate, granule release, and, in a fraction of platelets, phosphatidylserine (PS) exposure. This suggests the existence of decision-making mechanisms in the platelet intracellular signaling network. To identify and investigate them, we developed a computational model of PAR1-stimulated platelet signal transduction that included a minimal set of major players in the calcium signaling network. The model comprised three intracellular compartments: cytosol, dense tubular system (DTS) and mitochondria and extracellular space. Computer simulations showed that the stable resting state of platelets is maintained via a balance between calcium pumps and leaks through the DTS and plasma membranes. Stimulation of PAR1 induced oscillations in the cytosolic calcium concentrations, in good agreement with experimental observations. Further increase in the agonist level activated the mitochondrial uniporter leading to calcium uptake by mitochondria, which caused the collapse of mitochondrial membrane potential in a fraction of platelets leading to the PS exposure. The formation of this subpopulation was shown to be a stochastic process determined by the small number of activated PAR1 receptors and by heterogeneity in the number of ion pumps. These results demonstrate how a gradual increase of the activation degree can be converted into a stepped response hierarchy ultimately leading to formation of two distinct subpopulations from an initially homogeneous population. link: http://identifiers.org/pubmed/25627921
Parameters: none
States: none
Observables: none
MODEL1808210003
@ v0.0.1
Mathematical model of intrinsic pathway activation consisting of XIIa, kallikrein and HMWKa.
DetailsA mathematical model of contact activation of blood coagulation was developed and analysed. The model variables are concentrations of factor XIIa, kallikrein and activated high-molecular-weight kininogen. Concentrations of active factors were shown to depend on the activating signal value in a hysteretic manner. Within a range of relatively small signals, two (activated and non-activated) stable states coexist (bistability). Signals of the natural environment (surfaces of endothelial and blood cells) seem to be in the range of bistability; therefore, contact activation that persists for a short time can induce a transition of the system to the activated state, and, correspondingly, the formation of a clot. The system cannot return to the initial state, which is characterized by low activation levels, until the activating signals decrease significantly below those present in the circulation. link: http://identifiers.org/doi/10.1006/jtbi.1997.0584
Parameters: none
States: none
Observables: none
BIOMD0000000273
@ v0.0.1
This a model from the article: Data assimilation constrains new connections and components in a complex, eukaryotic…
DetailsCircadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation. link: http://identifiers.org/pubmed/20865009
Parameters:
Name | Description |
---|---|
m6 = 0.25; m7 = 0.5; D = 0.5; p5 = 1.0; m8 = 0.1; L = 0.5 |
Reaction: cT => ; cZG, cZTL, Rate Law: def*((m6*L+m7*D)*cT*(p5*cZTL+cZG)+m8*cT)/def |
g9 = 0.3; n7 = 0.2; i = 3.0; g8 = 0.14; q3 = 2.9; h = 2.0; n4 = 0.0; L = 0.5 |
Reaction: => cP9_m; cL, cP, cT, Rate Law: def*(L*q3*cP+(n4*L+n7*cL^i/(cL^i+g9^i))*g8^h/(cT^h+g8^h))/def |
m16 = 0.5 |
Reaction: cNI_m =>, Rate Law: def*m16*cNI_m/def |
p10 = 0.36 |
Reaction: => cNI; cNI_m, Rate Law: def*p10*cNI_m/def |
m4 = 0.2 |
Reaction: cLm =>, Rate Law: def*m4*cLm/def |
m11 = 1.0; L = 0.5 |
Reaction: cP =>, Rate Law: def*m11*cP*L/def |
D = 0.5; m26 = 0.14; m25 = 0.28; L = 0.5 |
Reaction: cTm =>, Rate Law: def*(m25*L+m26*D)*cTm/def |
D = 0.5; p2 = 0.27; p1 = 0.4; L = 0.5 |
Reaction: => cL; cL_m, Rate Law: def*cL_m*(p1*L+p2*D)/def |
m10 = 0.3 |
Reaction: cY =>, Rate Law: def*m10*cY/def |
m9 = 1.0 |
Reaction: cY_m =>, Rate Law: def*m9*cY_m/def |
p4 = 0.268 |
Reaction: => cT; cT_m, Rate Law: def*p4*cT_m/def |
m21 = 0.2 |
Reaction: cZG =>, Rate Law: def*m21*cZG/def |
m24 = 0.405; D = 0.5; m17 = 0.3; L = 0.5 |
Reaction: cNI =>, Rate Law: def*(m17*L+m24*D)*cNI/def |
m18 = 1.0 |
Reaction: cG_m =>, Rate Law: def*m18*cG_m/def |
D = 0.5; p7 = 0.3 |
Reaction: => cP, Rate Law: def*p7*D*(1-cP)/def |
D = 0.5; m2 = 0.24; m1 = 0.54; L = 0.5 |
Reaction: cL_m =>, Rate Law: def*(m1*L+m2*D)*cL_m/def |
p6 = 0.44 |
Reaction: => cY; cY_m, Rate Law: def*p6*cY_m/def |
g3 = 0.4; m3 = 0.2; p3 = 0.1; c = 3.0 |
Reaction: cL =>, Rate Law: def*(m3*cL+p3*cL^c/(cL^c+g3^c))/def |
m13 = 0.32; m22 = 2.0; D = 0.5; L = 0.5 |
Reaction: cP9 =>, Rate Law: def*(m13*L+m22*D)*cP9/def |
n1 = 1.8; n0 = 0.4; g2 = 0.28; g1 = 0.1; q1 = 0.8; a = 2.0; b = 3.0; L = 0.5 |
Reaction: => cL_m; cNI, cP, cP7, cP9, cTm, Rate Law: def*(n0*L+L*q1*cP+n1*cTm^b/(cTm^b+g2^b))*g1^a/((cP9+cP7+cNI)^a+g1^a)/def |
p8 = 0.7 |
Reaction: => cP9; cP9_m, Rate Law: def*p8*cP9_m/def |
g4 = 0.91; n2 = 0.7; g5 = 0.3; e = 2.0; n3 = 0.06; d = 2.5 |
Reaction: => cT_m; cL, cY, Rate Law: def*(n2*cY^d/(cY^d+g4^d)+n3)*g5^e/(cL^e+g5^e)/def |
n8 = 0.42; g11 = 0.7; j = 3.0; n9 = 0.26; k = 3.0; g10 = 0.7 |
Reaction: => cP7_m; cL, cLm, cP9, Rate Law: def*(n8*(cLm+cL)^j/((cLm+cL)^j+g10^j)+n9*cP9^k/(cP9^k+g11^k))/def |
D = 0.5; p13 = 0.4; p12 = 30.0; L = 0.5 |
Reaction: cG + cZTL => cZG, Rate Law: def*(p12*L*cZTL*cG-p13*D*cZG)/def |
m12 = 1.0 |
Reaction: cP9_m =>, Rate Law: def*m12*cP9_m/def |
p14 = 0.45 |
Reaction: => cZTL, Rate Law: def*p14/def |
g3 = 0.4; p3 = 0.1; c = 3.0 |
Reaction: => cLm; cL, Rate Law: def*p3*cL^c/(cL^c+g3^c)/def |
m5 = 0.3 |
Reaction: cT_m =>, Rate Law: def*m5*cT_m/def |
m20 = 1.2 |
Reaction: cZTL =>, Rate Law: def*m20*cZTL/def |
g16 = 0.2; n5 = 3.4; D = 0.5; q2 = 0.5; s = 3.0; g7 = 0.18; n6 = 1.25; L = 0.5; g = 2.0 |
Reaction: => cY_m; cL, cP, cT, Rate Law: def*(L*q2*cP+(n5*L+n6*D)*g7^s/(cT^s+g7^s)*g16^g/(cL^g+g16^g))/def |
p15 = 0.05; f = 3.0; g6 = 0.3 |
Reaction: => cTm; cT, Rate Law: def*p15*cT^f/(cT^f+g6^f)/def |
p11 = 0.23 |
Reaction: => cG; cG_m, Rate Law: def*p11*cG_m/def |
m19 = 0.2 |
Reaction: cG =>, Rate Law: def*m19*cG/def |
m14 = 0.28 |
Reaction: cP7_m =>, Rate Law: def*m14*cP7_m/def |
p9 = 0.4 |
Reaction: => cP7; cP7_m, Rate Law: def*p9*cP7_m/def |
n10 = 0.18; g12 = 0.5; m = 2.0; n11 = 0.71; g13 = 0.6; l = 2.0 |
Reaction: => cNI_m; cLm, cP7, Rate Law: def*(n10*cLm^l/(cLm^l+g12^l)+n11*cP7^m/(cP7^m+g13^m))/def |
g14 = 0.17; g15 = 0.4; n = 1.0; q4 = 0.6; o = 2.0; n12 = 2.3; L = 0.5 |
Reaction: => cG_m; cL, cP, cT, Rate Law: def*(L*q4*cP+n12*L*g15^o/(cL^o+g15^o)*g14^n/(cT^n+g14^n))/def |
D = 0.5; m15 = 0.31; L = 0.5; m23 = 1.0 |
Reaction: cP7 =>, Rate Law: def*(m15*L+m23*D)*cP7/def |
States:
Name | Description |
---|---|
cL m |
[messenger RNA] |
cNI |
[inhibitor] |
cG |
[Protein GIGANTEA] |
cP9 |
[Two-component response regulator-like APRR9] |
cP9 m |
[messenger RNA] |
cZTL |
[Adagio protein 1] |
cP7 m |
[messenger RNA] |
cNI m |
[inhibitor; messenger RNA] |
cG m |
[messenger RNA] |
cY |
[protein] |
cY m |
[messenger RNA; RNA] |
cT m |
[messenger RNA] |
cP |
cP |
cLm |
[Protein CCA1; Protein LHY; protein modification] |
cP7 |
[Two-component response regulator-like APRR7] |
cT |
[Two-component response regulator-like APRR1] |
cZG |
[Protein GIGANTEA; Adagio protein 1] |
cTm |
[Two-component response regulator-like APRR1; protein modification] |
cL |
[Protein CCA1; Protein LHY] |
Observables: none
BIOMD0000000412
@ v0.0.1
This model is from the article: The clock gene circuit in Arabidopsis includes a repressilator with additional feedb…
DetailsCircadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene Pseudo-Response Regulator 9 was repressed by an evening gene, previously identified with Timing Of CAB Expression1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes Late Elongated Hypocotyl and Circadian Clock Associated1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure. link: http://identifiers.org/pubmed/22395476
Parameters:
Name | Description |
---|---|
twilightPeriod = 0.05 3600*s; n12 = 12.5; q2 = 1.56; e = 2.0; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; g15 = 0.4; g14 = 0.004; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: s42 => cG_m; cEC, cL, cP, Rate Law: def*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q2*cP+n12*g14/(cEC+g14)*g15^e/(cL^e+g15^e)) |
p16 = 0.62 |
Reaction: s31 => cE3; cE3_m, Rate Law: def*p16*cE3_m/def |
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m27 = 0.1; p15 = 3.0; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cCOP1n => s40, Rate Law: def*m27*cCOP1n*(1+p15*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))) |
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m11 = 1.0; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cP => s8, Rate Law: def*m11*cP*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))) |
m16 = 0.5 |
Reaction: cNI_m => s18, Rate Law: def*m16*cNI_m/def |
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; m33 = 13.0; lightOffset = 0.0 3600*s; m31 = 0.3; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cCOP1d => s41, Rate Law: def*m31*(1+m33*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cCOP1d |
p12 = 3.4; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; p13 = 0.1; lightAmplitude = 1.0 3600*s |
Reaction: cG + cZTL => cZG, Rate Law: def*(p12*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*cZTL*cG-p13*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*cZG) |
p8 = 0.6 |
Reaction: s11 => cP9; cP9_m, Rate Law: def*p8*cP9_m/def |
n2 = 0.64; g5 = 0.15; g4 = 0.01; e = 2.0 |
Reaction: s21 => cT_m; cEC, cL, Rate Law: def*n2*g4/(cEC+g4)*g5^e/(cL^e+g5^e)/def |
g16 = 0.3; e = 2.0; n3 = 0.29 |
Reaction: s29 => cE3_m; cL, Rate Law: def*n3*g16^e/(cL^e+g16^e)/def |
p23 = 0.37 |
Reaction: s27 => cE4; cE4_m, Rate Law: def*p23*cE4_m/def |
twilightPeriod = 0.05 3600*s; p2 = 0.27; cyclePeriod = 24.0 3600*s; p1 = 0.13; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: s3 => cL; cL_m, Rate Law: def*cL_m*(p1*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))+p2) |
p3 = 0.1; c = 2.0; g3 = 0.6 |
Reaction: s5 => cLm; cL, Rate Law: def*p3*cL^c/(cL^c+g3^c)/def |
n5 = 0.23 |
Reaction: s38 => cCOP1c, Rate Law: def*n5/def |
p11 = 0.51 |
Reaction: s44 => cG; cG_m, Rate Law: def*p11*cG_m/def |
p17 = 4.8 |
Reaction: cE3 + cG => cEG, Rate Law: def*p17*cE3*cG/def |
p27 = 0.8 |
Reaction: s36 => cLUX; cLUX_m, Rate Law: def*p27*cLUX_m/def |
m20 = 0.6 |
Reaction: cZTL => s47, Rate Law: def*m20*cZTL/def |
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m24 = 0.1; phase = 0.0 3600*s; m17 = 0.5; lightAmplitude = 1.0 3600*s |
Reaction: cNI => s20, Rate Law: def*(m17+m24*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cNI |
m14 = 0.4 |
Reaction: cP7_m => s14, Rate Law: def*m14*cP7_m/def |
p9 = 0.8 |
Reaction: s15 => cP7; cP7_m, Rate Law: def*p9*cP7_m/def |
m12 = 1.0 |
Reaction: cP9_m => s10, Rate Law: def*m12*cP9_m/def |
m19 = 0.2; p26 = 0.3; p28 = 2.0; m30 = 3.0; m29 = 5.0; p25 = 8.0; m37 = 0.8; p29 = 0.1; m36 = 0.1; p17 = 4.8; p21 = 1.0 |
Reaction: cE3n => s33; cCOP1d, cCOP1n, cE4, cG, cLUX, Rate Law: def*(((m29*cE3n*cCOP1n+m30*cE3n*cCOP1d+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n))+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/def |
m5 = 0.3 |
Reaction: cT_m => s22, Rate Law: def*m5*cT_m/def |
twilightPeriod = 0.05 3600*s; a = 2.0; cyclePeriod = 24.0 3600*s; n1 = 2.6; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; g1 = 0.1; q1 = 1.2; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: s1 => cL_m; cNI, cP, cP7, cP9, cT, Rate Law: def*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q1*cP+n1*g1^a/((cP9+cP7+cNI+cT)^a+g1^a)) |
m21 = 0.08 |
Reaction: cZG => s48, Rate Law: def*m21*cZG/def |
m26 = 0.5 |
Reaction: cE3_m => s30, Rate Law: def*m26*cE3_m/def |
m3 = 0.2; p3 = 0.1; c = 2.0; g3 = 0.6 |
Reaction: cL => s4, Rate Law: def*(m3*cL+p3*cL^c/(cL^c+g3^c))/def |
p4 = 0.56 |
Reaction: s23 => cT; cT_m, Rate Law: def*p4*cT_m/def |
p25 = 8.0; m37 = 0.8; m36 = 0.1; p26 = 0.3; m39 = 0.3; p21 = 1.0 |
Reaction: cLUX => s37; cCOP1d, cCOP1n, cE3n, cE4, Rate Law: def*(m39*cLUX+p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n))/def |
m4 = 0.2 |
Reaction: cLm => s6, Rate Law: def*m4*cLm/def |
twilightPeriod = 0.05 3600*s; g9 = 0.3; n7 = 0.2; e = 2.0; cyclePeriod = 24.0 3600*s; q3 = 2.8; g8 = 0.01; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s; n4 = 0.07 |
Reaction: s9 => cP9_m; cEC, cL, cP, Rate Law: def*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q3*cP+(n4+n7*cL^e/(cL^e+g9^e))*g8/(cEC+g8)) |
p14 = 0.14 |
Reaction: s46 => cZTL, Rate Law: def*p14/def |
m32 = 0.2; m19 = 0.2; m10 = 1.0; m36 = 0.1; p17 = 4.8; d = 2.0; p24 = 10.0; lightOffset = 0.0 3600*s; p18 = 4.0; g7 = 0.6; m37 = 0.8; m9 = 1.1; phase = 0.0 3600*s; twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; p29 = 0.1; p31 = 0.1; photoPeriod = 12.0 3600*s; p28 = 2.0; lightAmplitude = 1.0 3600*s |
Reaction: cEC => s51; cCOP1d, cCOP1n, cE3n, cEG, cG, Rate Law: def*(m36*cCOP1n*cEC+m37*cCOP1d*cEC+m32*cEC*(1+p24*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*(p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m9*cCOP1n+m10*cCOP1d+p31))^d/((p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m9*cCOP1n+m10*cCOP1d+p31))^d+g7^d))) |
m34 = 0.6 |
Reaction: cE4_m => s26, Rate Law: def*m34*cE4_m/def |
p25 = 8.0; m37 = 0.8; m36 = 0.1; p26 = 0.3; p21 = 1.0; m35 = 0.3 |
Reaction: cE4 => s28; cCOP1d, cCOP1n, cE3n, cLUX, Rate Law: def*((m35*cE4+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n))/def |
n10 = 0.4; g12 = 0.2; n11 = 0.6; b = 2.0; e = 2.0; g13 = 1.0 |
Reaction: s17 => cNI_m; cLm, cP7, Rate Law: def*(n10*cLm^e/(cLm^e+g12^e)+n11*cP7^b/(cP7^b+g13^b))/def |
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; m23 = 1.8; lightOffset = 0.0 3600*s; m15 = 0.7; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cP7 => s16, Rate Law: def*(m15+m23*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cP7 |
twilightPeriod = 0.05 3600*s; m22 = 0.1; cyclePeriod = 24.0 3600*s; m13 = 0.32; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cP9 => s12, Rate Law: def*(m13+m22*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cP9 |
m18 = 3.4 |
Reaction: cG_m => s43, Rate Law: def*m18*cG_m/def |
m19 = 0.2; p29 = 0.1; p17 = 4.8; p28 = 2.0 |
Reaction: cG => s45; cE3n, Rate Law: def*((m19*cG+p28*cG)-p29*p28*cG/(p29+m19+p17*cE3n))/def |
twilightPeriod = 0.05 3600*s; p5 = 4.0; cyclePeriod = 24.0 3600*s; m8 = 0.4; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m6 = 0.3; m7 = 0.7; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cT => s24; cZG, cZTL, Rate Law: def*((m6+m7*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cT*(p5*cZTL+cZG)+m8*cT) |
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; p7 = 0.3; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: s7 => cP, Rate Law: def*p7*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*(1-cP) |
m9 = 1.1 |
Reaction: cE3 => s32; cCOP1c, Rate Law: def*m9*cE3*cCOP1c/def |
twilightPeriod = 0.05 3600*s; n14 = 0.1; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; n6 = 20.0; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cCOP1n => cCOP1d; cP, Rate Law: def*(n6*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*cP*cCOP1n+n14*cCOP1n) |
twilightPeriod = 0.05 3600*s; m2 = 0.24; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m1 = 0.54; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s |
Reaction: cL_m => s2, Rate Law: def*(m2+(m1-m2)*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*cL_m |
m19 = 0.2; p18 = 4.0; p28 = 2.0; m10 = 1.0; p29 = 0.1; m9 = 1.1; p17 = 4.8; p31 = 0.1 |
Reaction: cEG => s49; cCOP1c, cCOP1d, cCOP1n, cE3n, cG, Rate Law: def*((m9*cEG*cCOP1c+p18*cEG)-p31*(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m9*cCOP1n+m10*cCOP1d+p31))/def |
p10 = 0.54 |
Reaction: s19 => cNI; cNI_m, Rate Law: def*p10*cNI_m/def |
p20 = 0.1; p19 = 1.0 |
Reaction: cE3 => cE3n, Rate Law: def*(p19*cE3-p20*cE3n)/def |
p25 = 8.0; m37 = 0.8; m36 = 0.1; p26 = 0.3; p21 = 1.0 |
Reaction: s50 => cEC; cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n)/def |
n13 = 1.3; g2 = 0.01; e = 2.0; g6 = 0.3 |
Reaction: s25 => cE4_m; cEC, cL, Rate Law: def*n13*g2/(cEC+g2)*g6^e/(cL^e+g6^e)/def |
g11 = 0.7; n9 = 0.2; f = 2.0; g10 = 0.5; n8 = 0.5; e = 2.0 |
Reaction: s13 => cP7_m; cL, cLm, cP9, Rate Law: def*(n8*(cLm+cL)^e/((cLm+cL)^e+g10^e)+n9*cP9^f/(cP9^f+g11^f))/def |
States:
Name | Description |
---|---|
cE4 |
[Protein EARLY FLOWERING 4] |
cNI |
[Two-component response regulator-like APRR5] |
cLUX |
[Homeodomain-like superfamily protein] |
s5 |
s5 |
s40 |
s40 |
cP9 m |
[Two-component response regulator-like APRR9; messenger RNA] |
s37 |
s37 |
s44 |
s44 |
s31 |
s31 |
cNI m |
[Two-component response regulator-like APRR3; messenger RNA] |
cEG |
[Protein GIGANTEA; Protein EARLY FLOWERING 3] |
s10 |
s10 |
s34 |
s34 |
s38 |
s38 |
s36 |
s36 |
s6 |
s6 |
s46 |
s46 |
s11 |
s11 |
cP |
[obsolete protein] |
s45 |
s45 |
cZG |
[Protein GIGANTEA; Adagio protein 1] |
s1 |
s1 |
cG |
[Protein GIGANTEA] |
cE3 |
[Protein EARLY FLOWERING 3] |
s17 |
s17 |
s41 |
s41 |
cP7 m |
[Two-component response regulator-like APRR7; messenger RNA] |
s25 |
s25 |
s2 |
s2 |
s49 |
s49 |
s33 |
s33 |
cCOP1c |
[E3 ubiquitin-protein ligase COP1; cytoplasm] |
cT m |
[Two-component response regulator-like APRR1; messenger RNA] |
s28 |
s28 |
cLm |
[Protein LHY; Protein CCA1; CCO:U0000010] |
cL |
[Protein LHY; Protein CCA1] |
s35 |
s35 |
s24 |
s24 |
cP9 |
[Two-component response regulator-like APRR9] |
s7 |
s7 |
cZTL |
[Adagio protein 1] |
s43 |
s43 |
cCOP1n |
[E3 ubiquitin-protein ligase COP1; nucleus] |
s47 |
s47 |
cE4 m |
[Protein EARLY FLOWERING 4; messenger RNA] |
cG m |
[Protein GIGANTEA; messenger RNA] |
s32 |
s32 |
s22 |
s22 |
cCOP1d |
[E3 ubiquitin-protein ligase COP1; nucleus] |
cE3n |
[Protein EARLY FLOWERING 3; nucleus] |
s51 |
s51 |
cP7 |
[Two-component response regulator-like APRR7] |
s3 |
s3 |
cE3 m |
[Protein EARLY FLOWERING 3; messenger RNA] |
s48 |
s48 |
cEC |
[Protein EARLY FLOWERING 3; Protein EARLY FLOWERING 4; Homeodomain-like superfamily protein] |
cL m |
[Protein CCA1; Protein LHY; messenger RNA] |
s12 |
s12 |
s4 |
s4 |
cLUX m |
[Homeodomain-like superfamily protein; messenger RNA] |
s30 |
s30 |
s26 |
s26 |
s42 |
s42 |
s39 |
s39 |
cT |
[Two-component response regulator-like APRR1] |
s29 |
s29 |
s27 |
s27 |
Observables: none
BIOMD0000000445
@ v0.0.1
Pokhilko2013 - TOC1 signalling in Arabidopsis circadian clockIn this model, Pokhilko et al. has incorporated the negat…
Details24-hour biological clocks are intimately connected to the cellular signalling network, which complicates the analysis of clock mechanisms. The transcriptional regulator TOC1 (TIMING OF CAB EXPRESSION 1) is a founding component of the gene circuit in the plant circadian clock. Recent results show that TOC1 suppresses transcription of multiple target genes within the clock circuit, far beyond its previously-described regulation of the morning transcription factors LHY (LATE ELONGATED HYPOCOTYL) and CCA1 (CIRCADIAN CLOCK ASSOCIATED 1). It is unclear how this pervasive effect of TOC1 affects the dynamics of the clock and its outputs. TOC1 also appears to function in a nested feedback loop that includes signalling by the plant hormone Abscisic Acid (ABA), which is upregulated by abiotic stresses, such as drought. ABA treatments both alter TOC1 levels and affect the clock's timing behaviour. Conversely, the clock rhythmically modulates physiological processes induced by ABA, such as the closing of stomata in the leaf epidermis. In order to understand the dynamics of the clock and its outputs under changing environmental conditions, the reciprocal interactions between the clock and other signalling pathways must be integrated.We extended the mathematical model of the plant clock gene circuit by incorporating the repression of multiple clock genes by TOC1, observed experimentally. The revised model more accurately matches the data on the clock's molecular profiles and timing behaviour, explaining the clock's responses in TOC1 over-expression and toc1 mutant plants. A simplified representation of ABA signalling allowed us to investigate the interactions of ABA and circadian pathways. Increased ABA levels lengthen the free-running period of the clock, consistent with the experimental data. Adding stomatal closure to the model, as a key ABA- and clock-regulated downstream process allowed to describe TOC1 effects on the rhythmic gating of stomatal closure.The integrated model of the circadian clock circuit and ABA-regulated environmental sensing allowed us to explain multiple experimental observations on the timing and stomatal responses to genetic and environmental perturbations. These results crystallise a new role of TOC1 as an environmental sensor, which both affects the pace of the central oscillator and modulates the kinetics of downstream processes. link: http://identifiers.org/pubmed/23506153
Parameters:
Name | Description |
---|---|
p17 = 17.0 |
Reaction: cE3 + cG => cEG; cE3, cG, Rate Law: def*p17*cE3*cG/def |
n13 = 2.0; parameter_7 = 2.0; parameter_3 = 0.4; g2 = 0.01; e = 2.0; g6 = 0.3 |
Reaction: => cLUX_m; cT, cEC, cL, cEC, cL, cT, Rate Law: def*parameter_3^parameter_7/(parameter_3^parameter_7+cT^parameter_7)*n13*g2/(cEC+g2)*g6^e/(cL^e+g6^e)/def |
p16 = 0.62 |
Reaction: => cE3; cE3_m, cE3_m, Rate Law: def*p16*cE3_m/def |
m16 = 0.5 |
Reaction: cNI_m => ; cNI_m, Rate Law: def*m16*cNI_m/def |
parameter_14 = 0.5; n2 = 0.35; g5 = 0.2; parameter_11 = 2.0; g4 = 0.006; e = 2.0 |
Reaction: => cT_m; cL, species_3, cEC, cEC, cL, species_3, Rate Law: def*n2/(1+(cL/(g5*(1+(species_3/parameter_14)^parameter_11)))^e)*g4/(cEC+g4)/def |
m29 = 0.3 |
Reaction: species_4 => ; species_4, Rate Law: default*m29*species_4/def |
m7 = 0.1; p5 = 1.0; m6 = 0.2; m8 = 0.5; L = 0.5 |
Reaction: cT => ; cZTL, cZG, cT, cZG, cZTL, Rate Law: def*((m6+m7*(1-L))*cT*(p5*cZTL+cZG)+m8*cT) |
parameter_29 = 1.0; parameter_28 = 0.2; parameter_9 = 2.0; parameter_18 = 1.0; parameter_16 = 0.2 |
Reaction: => species_2; species_1, species_1, Rate Law: default*parameter_28*parameter_16^parameter_9/((0.5*((parameter_29+species_1+parameter_18)-((parameter_29+species_1+parameter_18)^2-4*parameter_29*species_1)^(1/2)))^parameter_9+parameter_16^parameter_9)/def |
m11 = 1.0; L = 0.5 |
Reaction: cP => ; cP, Rate Law: def*m11*cP*L |
m33 = 13.0; m31 = 0.1; L = 0.5 |
Reaction: cCOP1d => ; cCOP1d, Rate Law: def*m31*(1+m33*(1-L))*cCOP1d |
p8 = 0.6 |
Reaction: => cP9; cP9_m, cP9_m, Rate Law: def*p8*cP9_m/def |
p17 = 17.0; p29 = 0.1; m19 = 0.9; p28 = 2.0 |
Reaction: cG => ; cE3n, cE3n, cG, Rate Law: def*((m19*cG+p28*cG)-p29*p28*cG/(p29+m19+p17*cE3n))/def |
m30 = 1.0 |
Reaction: species_3 => ; species_2, species_2, species_3, Rate Law: default*m30*species_3*species_2/def |
parameter_7 = 2.0; g12 = 0.1; n10 = 0.3; e = 2.0; n11 = 0.6; b = 2.0; parameter_12 = 0.6; g13 = 1.0 |
Reaction: => cNI_m; cT, cLm, cP7, cLm, cP7, cT, Rate Law: def*parameter_12^parameter_7/(parameter_12^parameter_7+cT^parameter_7)*(n10*cLm^e/(cLm^e+g12^e)+n11*cP7^b/(cP7^b+g13^b))/def |
m32 = 0.2; parameter_26 = 0.1; m19 = 0.9; m10 = 0.1; p29 = 0.1; L = 0.5; d = 2.0; p17 = 17.0; p24 = 11.0; p18 = 4.0; p28 = 2.0; m9 = 0.2; g7 = 1.0 |
Reaction: cEC => ; cCOP1n, cCOP1d, cG, cE3n, cEG, cCOP1d, cCOP1n, cE3n, cEC, cEG, cG, Rate Law: def*(m10*cCOP1n*cEC+m9*cCOP1d*cEC+m32*cEC*(1+p24*L*(p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m10*cCOP1n+m9*cCOP1d+parameter_26))^d/((p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m10*cCOP1n+m9*cCOP1d+parameter_26))^d+g7^d))) |
g16 = 0.3; e = 2.0; n3 = 0.29 |
Reaction: => cE3_m; cL, cL, Rate Law: def*n3*g16^e/(cL^e+g16^e)/def |
p3 = 0.1; c = 2.0; g3 = 0.6 |
Reaction: => cLm; cL, cL, Rate Law: def*p3*cL^c/(cL^c+g3^c)/def |
p23 = 0.37 |
Reaction: => cE4; cE4_m, cE4_m, Rate Law: def*p23*cE4_m/def |
g14 = 0.02; parameter_7 = 2.0; q2 = 1.56; g15 = 0.4; e = 2.0; n12 = 9.0; parameter_1 = 0.6; L = 0.5 |
Reaction: => cG_m; cT, cP, cEC, cL, cEC, cL, cP, cT, Rate Law: def*parameter_1^parameter_7/(parameter_1^parameter_7+cT^parameter_7)*(L*q2*cP+n12*g14/(cEC+g14)*g15^e/(cL^e+g15^e)) |
p27 = 0.8 |
Reaction: => cLUX; cLUX_m, cLUX_m, Rate Law: def*p27*cLUX_m/def |
m20 = 0.6 |
Reaction: cZTL => ; cZTL, Rate Law: def*m20*cZTL/def |
p4 = 0.5 |
Reaction: => cT; cT_m, cT_m, Rate Law: def*p4*cT_m/def |
p17 = 17.0; p26 = 0.3; m19 = 0.9; p28 = 2.0; m10 = 0.1; p29 = 0.1; m9 = 0.2; p21 = 1.0; p25 = 2.0 |
Reaction: cE3n => ; cCOP1n, cCOP1d, cE4, cLUX, cG, cE3n, cCOP1d, cCOP1n, cE3n, cE4, cG, cLUX, Rate Law: def*(((m10*cE3n*cCOP1n+m9*cE3n*cCOP1d+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n))+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/def |
m14 = 0.4 |
Reaction: cP7_m => ; cP7_m, Rate Law: def*m14*cP7_m/def |
p9 = 0.8 |
Reaction: => cP7; cP7_m, cP7_m, Rate Law: def*p9*cP7_m/def |
m12 = 1.0 |
Reaction: cP9_m => ; cP9_m, Rate Law: def*m12*cP9_m/def |
n4 = 0.04; n7 = 0.1; g9 = 0.3; parameter_7 = 2.0; e = 2.0; g8 = 0.04; parameter_2 = 0.4; q3 = 3.0; L = 0.5 |
Reaction: => cP9_m; cP, cL, cEC, cT, cEC, cL, cP, cT, Rate Law: def*parameter_2^parameter_7/(parameter_2^parameter_7+cT^parameter_7)*(L*q3*cP+(n4+n7*cL^e/(cL^e+g9^e))*g8/(cEC+g8)) |
m5 = 0.3 |
Reaction: cT_m => ; cT_m, Rate Law: def*m5*cT_m/def |
p11 = 0.5 |
Reaction: => cG; cG_m, cG_m, Rate Law: def*p11*cG_m/def |
m37 = 0.4 |
Reaction: species_1 => ; species_1, Rate Law: default*m37*species_1/def |
m21 = 0.08 |
Reaction: cZG => ; cZG, Rate Law: def*m21*cZG/def |
m24 = 0.5; m17 = 0.5; L = 0.5 |
Reaction: cNI => ; cNI, Rate Law: def*(m17+m24*(1-L))*cNI |
p1 = 0.13; p2 = 0.27; L = 0.5 |
Reaction: => cL; cL_m, cL_m, Rate Law: def*cL_m*(p1*L+p2) |
m10 = 0.1; m9 = 0.2; p26 = 0.3; p21 = 1.0; m35 = 0.3; p25 = 2.0 |
Reaction: cE4 => ; cE3n, cLUX, cCOP1d, cCOP1n, cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*((m35*cE4+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n))/def |
m26 = 0.5 |
Reaction: cE3_m => ; cE3_m, Rate Law: def*m26*cE3_m/def |
m3 = 0.2; p3 = 0.1; c = 2.0; g3 = 0.6 |
Reaction: cL => ; cL, Rate Law: def*(m3*cL+p3*cL^c/(cL^c+g3^c))/def |
parameter_27 = 0.1 |
Reaction: => species_3, Rate Law: default*parameter_27/def |
m23 = 0.5; m15 = 0.7; L = 0.5 |
Reaction: cP7 => ; cP7, Rate Law: def*(m15+m23*(1-L))*cP7 |
p12 = 10.0; L = 0.5; p13 = 0.1 |
Reaction: cG + cZTL => cZG; cG, cZG, cZTL, Rate Law: def*(p12*L*cZTL*cG-p13*(1-L)*cZG) |
p6 = 0.2 |
Reaction: cCOP1c => cCOP1n; cCOP1c, Rate Law: def*p6*cCOP1c/def |
m4 = 0.2 |
Reaction: cLm => ; cLm, Rate Law: def*m4*cLm/def |
p14 = 0.14 |
Reaction: => cZTL, Rate Law: def*p14/def |
m34 = 0.6 |
Reaction: cLUX_m => ; cLUX_m, Rate Law: def*m34*cLUX_m/def |
m10 = 0.1; m9 = 0.2; p26 = 0.3; p21 = 1.0; p25 = 2.0 |
Reaction: => cEC; cLUX, cE4, cE3n, cCOP1d, cCOP1n, cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n)/def |
m10 = 0.1; m9 = 0.2; m36 = 0.3; p26 = 0.3; p21 = 1.0; p25 = 2.0 |
Reaction: cLUX => ; cE4, cE3n, cCOP1d, cCOP1n, cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*(m36*cLUX+p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n))/def |
g11 = 0.7; parameter_7 = 2.0; g10 = 0.5; n9 = 0.6; e = 2.0; f = 2.0; n8 = 0.5; parameter_6 = 0.1 |
Reaction: => cP7_m; cLm, cL, cP9, cT, cL, cLm, cP9, cT, Rate Law: def*parameter_6^parameter_7/(parameter_6^parameter_7+cT^parameter_7)*(n8*(cLm+cL)^e/((cLm+cL)^e+g10^e)+n9*cP9^f/(cP9^f+g11^f))/def |
parameter_13 = 0.3; parameter_7 = 2.0; parameter_24 = 0.5; e = 2.0; parameter_17 = 0.1 |
Reaction: => species_1; cT, cL, cL, cT, Rate Law: default*parameter_13^parameter_7/(parameter_13^parameter_7+cT^parameter_7)*parameter_24*cL^e/(cL^e+parameter_17^e)/def |
m18 = 3.4 |
Reaction: cG_m => ; cG_m, Rate Law: def*m18*cG_m/def |
m9 = 0.2 |
Reaction: cE3 => ; cCOP1c, cCOP1c, cE3, Rate Law: def*m9*cE3*cCOP1c/def |
m27 = 0.1; p15 = 2.0; L = 0.5 |
Reaction: cCOP1c => ; cCOP1c, Rate Law: def*m27*cCOP1c*(1+p15*L) |
m13 = 0.32; m22 = 0.1; L = 0.5 |
Reaction: cP9 => ; cP9, Rate Law: def*(m13+m22*(1-L))*cP9 |
m2 = 0.24; m1 = 0.54; L = 0.5 |
Reaction: cL_m => ; cL_m, Rate Law: def*(m2+(m1-m2)*L)*cL_m |
parameter_20 = 0.2 |
Reaction: species_2 => ; species_2, Rate Law: default*parameter_20*species_2/def |
a = 2.0; n1 = 2.6; g1 = 0.1; q1 = 1.0; L = 0.5 |
Reaction: => cL_m; cP, cP9, cP7, cNI, cT, cNI, cP, cP7, cP9, cT, Rate Law: def*(L*q1*cP+n1*g1^a/((cP9+cP7+cNI+cT)^a+g1^a)) |
n14 = 0.1; n6 = 20.0; L = 0.5 |
Reaction: cCOP1n => cCOP1d; cP, cCOP1n, cP, Rate Law: def*(n6*L*cP*cCOP1n+n14*cCOP1n) |
n5 = 0.4 |
Reaction: => cCOP1c, Rate Law: def*n5/def |
parameter_10 = 2.0; parameter_21 = 0.5; parameter_15 = 0.3; parameter_25 = 0.2; L = 0.5 |
Reaction: => species_4; species_4, species_3, species_3, species_4, Rate Law: default*(parameter_25+parameter_21*L)*(1-species_4)*parameter_15^parameter_10/(parameter_15^parameter_10+species_3^parameter_10)/def |
parameter_7 = 2.0; parameter_8 = 2.0; e = 2.0; g6 = 0.3; parameter_4 = 0.03; parameter_5 = 0.4 |
Reaction: => cE4_m; cT, cEC, cL, cEC, cL, cT, Rate Law: def*parameter_5^parameter_7/(parameter_5^parameter_7+cT^parameter_7)*parameter_8*parameter_4/(cEC+parameter_4)*g6^e/(cL^e+g6^e)/def |
p17 = 17.0; parameter_26 = 0.1; p18 = 4.0; m19 = 0.9; p28 = 2.0; m10 = 0.1; p29 = 0.1; m9 = 0.2 |
Reaction: cEG => ; cCOP1c, cE3n, cG, cCOP1n, cCOP1d, cCOP1c, cCOP1d, cCOP1n, cE3n, cEG, cG, Rate Law: def*((m10*cEG*cCOP1c+p18*cEG)-parameter_26*(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m10*cCOP1n+m9*cCOP1d+parameter_26))/def |
p10 = 0.54 |
Reaction: => cNI; cNI_m, cNI_m, Rate Law: def*p10*cNI_m/def |
p20 = 0.1; p19 = 1.0 |
Reaction: cE3 => cE3n; cE3, cE3n, Rate Law: def*(p19*cE3-p20*cE3n)/def |
p7 = 0.3; L = 0.5 |
Reaction: => cP; cP, Rate Law: def*p7*(1-L)*(1-cP) |
States:
Name | Description |
---|---|
cE4 |
[Protein EARLY FLOWERING 4] |
cNI |
[Two-component response regulator-like APRR5] |
cLUX |
[Homeodomain-like superfamily protein] |
cP9 |
[Two-component response regulator-like APRR9] |
cP9 m |
[Two-component response regulator-like APRR9; messenger RNA] |
cZTL |
[Adagio protein 1] |
species 1 |
[Magnesium-chelatase subunit ChlH, chloroplastic; messenger RNA] |
species 4 |
cs |
cCOP1n |
[E3 ubiquitin-protein ligase COP1; nucleus] |
cNI m |
[Two-component response regulator-like APRR3; messenger RNA] |
cEG |
[Protein GIGANTEA; Protein EARLY FLOWERING 3] |
cG m |
[Protein GIGANTEA; messenger RNA] |
cE4 m |
[Protein EARLY FLOWERING 4; messenger RNA] |
cCOP1d |
[E3 ubiquitin-protein ligase COP1; nucleus] |
cP |
[GO:0003575] |
cE3n |
[Protein EARLY FLOWERING 3; nucleus] |
cP7 |
[Two-component response regulator-like APRR7] |
cZG |
[Protein GIGANTEA; Adagio protein 1] |
cE3 m |
[Protein EARLY FLOWERING 3; messenger RNA] |
species 2 |
[Protein phosphatase 2C 16] |
cL m |
[Protein CCA1; Protein LHY; messenger RNA] |
cG |
[Protein GIGANTEA] |
cE3 |
[Protein EARLY FLOWERING 3] |
cEC |
[Protein EARLY FLOWERING 3; Protein EARLY FLOWERING 4; Homeodomain-like superfamily protein] |
cP7 m |
[Two-component response regulator-like APRR7; messenger RNA] |
cLUX m |
[Homeodomain-like superfamily protein; messenger RNA] |
cT m |
[Two-component response regulator-like APRR1; messenger RNA] |
cCOP1c |
[E3 ubiquitin-protein ligase COP1; cytoplasm] |
species 3 |
[Serine/threonine-protein kinase SRK2E] |
cLm |
[Protein LHY; Protein CCA1; CCO:U0000010] |
cT |
[Two-component response regulator-like APRR1] |
cL |
[Protein LHY; Protein CCA1] |
Observables: none
MODEL1410060000
@ v0.0.1
Poliquin2013 - Energy Deregulations in Parkinson's DiseaseEncoded non-curated model. Issues: - Fluxes, reactions, param…
DetailsParkinson's disease (PD) is a multifactorial disease known to result from a variety of factors. Although age is the principal risk factor, other etiological mechanisms have been identified, including gene mutations and exposure to toxins. Deregulation of energy metabolism, mostly through the loss of complex I efficiency, is involved in disease progression in both the genetic and sporadic forms of the disease. In this study, we investigated energy deregulation in the cerebral tissue of animal models (genetic and toxin induced) of PD using an approach that combines metabolomics and mathematical modelling. In a first step, quantitative measurements of energy-related metabolites in mouse brain slices revealed most affected pathways. A genetic model of PD, the Park2 knockout, was compared to the effect of CCCP, a mitochondrial uncoupler [corrected]. Model simulated and experimental results revealed a significant and sustained decrease in ATP after CCCP exposure, but not in the genetic mice model. In support to data analysis, a mathematical model of the relevant metabolic pathways was developed and calibrated onto experimental data. In this work, we show that a short-term stress response in nucleotide scavenging is most probably induced by the toxin exposure. In turn, the robustness of energy-related pathways in the model explains how genetic perturbations, at least in young animals, are not sufficient to induce significant changes at the metabolite level. link: http://identifiers.org/pubmed/23935941
Parameters: none
States: none
Observables: none
MODEL2005150001
@ v0.0.1
The cell-cycle oscillator includes an essential negative-feedback loop: Cdc2 activates the anaphase-promoting complex (A…
DetailsThe cell-cycle oscillator includes an essential negative-feedback loop: Cdc2 activates the anaphase-promoting complex (APC), which leads to cyclin destruction and Cdc2 inactivation. Under some circumstances, a negative-feedback loop is sufficient to generate sustained oscillations. However, the Cdc2/APC system also includes positive-feedback loops, whose functional importance we now assess. We show that short-circuiting positive feedback makes the oscillations in Cdc2 activity faster, less temporally abrupt, and damped. This compromises the activation of cyclin destruction and interferes with mitotic exit and DNA replication. This work demonstrates a systems-level role for positive-feedback loops in the embryonic cell cycle and provides an example of how oscillations can emerge out of combinations of subcircuits whose individual behaviors are not oscillatory. This work also underscores the fundamental similarity of cell-cycle oscillations in embryos to repetitive action potentials in pacemaker neurons, with both systems relying on a combination of negative and positive-feedback loops. link: http://identifiers.org/pubmed/16122424
Parameters: none
States: none
Observables: none
BIOMD0000000013
@ v0.0.1
This a model from the article: Applications of metabolic modelling to plant metabolism. Poolman MG ,Assmus HE, F…
DetailsIn this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes. link: http://identifiers.org/pubmed/15073223
Parameters:
Name | Description |
---|---|
PGI_v=5.0E8; q14=2.3 |
Reaction: F6P_ch => G6P_ch, Rate Law: PGI_v*chloroplast*(F6P_ch-G6P_ch/q14) |
q15=0.058; PGM_v=5.0E8 |
Reaction: G6P_ch => G1P_ch, Rate Law: PGM_v*chloroplast*(G6P_ch-G1P_ch/q15) |
F_TKL_v=5.0E8; q7=0.084 |
Reaction: GAP_ch + F6P_ch => X5P_ch + E4P_ch, Rate Law: chloroplast*F_TKL_v*(F6P_ch*GAP_ch-E4P_ch*X5P_ch/q7) |
Light_on = 1.0; FBPase_ch_KiF6P=0.7; FBPase_ch_km=0.03; FBPase_ch_KiPi=12.0; FBPase_ch_vm=200.0 |
Reaction: FBP_ch => F6P_ch + Pi_ch, Rate Law: Light_on*FBPase_ch_vm*FBP_ch*chloroplast/(FBP_ch+FBPase_ch_km*(1+F6P_ch/FBPase_ch_KiF6P+Pi_ch/FBPase_ch_KiPi)) |
q3=1.6E7; Light_on = 1.0; G3Pdh_v=5.0E8 |
Reaction: x_NADPH_ch + BPGA_ch + x_Proton_ch => x_NADP_ch + GAP_ch + Pi_ch, Rate Law: Light_on*G3Pdh_v*chloroplast*(BPGA_ch*x_NADPH_ch*x_Proton_ch-x_NADP_ch*GAP_ch*Pi_ch/q3) |
q10=0.85; G_TKL_v=5.0E8 |
Reaction: S7P_ch + GAP_ch => R5P_ch + X5P_ch, Rate Law: chloroplast*G_TKL_v*(GAP_ch*S7P_ch-X5P_ch*R5P_ch/q10) |
StPase_Vm=40.0; StPase_kiG1P=0.05; StPase_km=0.1 |
Reaction: x_Starch_ch + Pi_ch => G1P_ch, Rate Law: StPase_Vm*Pi_ch*chloroplast/(Pi_ch+StPase_km*(1+G1P_ch/StPase_kiG1P)) |
Ru5Pk_ch_KiPi=4.0; Ru5Pk_ch_KiADP1=2.5; Light_on = 1.0; Ru5Pk_ch_KiADP2=0.4; Ru5Pk_ch_vm=10000.0; Ru5Pk_ch_KiPGA=2.0; Ru5Pk_ch_km1=0.05; Ru5Pk_ch_KiRuBP=0.7; Ru5Pk_ch_km2=0.05 |
Reaction: Ru5P_ch + ATP_ch => RuBP_ch + ADP_ch; PGA_ch, Pi_ch, Rate Law: Light_on*Ru5Pk_ch_vm*Ru5P_ch*chloroplast*ATP_ch/((Ru5P_ch+Ru5Pk_ch_km1*(1+PGA_ch/Ru5Pk_ch_KiPGA+RuBP_ch/Ru5Pk_ch_KiRuBP+Pi_ch/Ru5Pk_ch_KiPi))*(ATP_ch*(1+ADP_ch/Ru5Pk_ch_KiADP1)+Ru5Pk_ch_km2*(1+ADP_ch/Ru5Pk_ch_KiADP2))) |
q4=22.0; TPI_v=5.0E8 |
Reaction: GAP_ch => DHAP_ch, Rate Law: chloroplast*TPI_v*(GAP_ch-DHAP_ch/q4) |
R5Piso_v=5.0E8; q11=0.4 |
Reaction: R5P_ch => Ru5P_ch, Rate Law: R5Piso_v*chloroplast*(R5P_ch-Ru5P_ch/q11) |
Light_on = 1.0; Rbco_KiFBP=0.04; Rbco_KiNADPH=0.07; Rbco_KiPGA=0.84; Rbco_vm=340.0; Rbco_KiSBP=0.075; Rbco_km=0.02; Rbco_KiPi=0.9 |
Reaction: RuBP_ch + x_CO2 => PGA_ch; FBP_ch, SBP_ch, Pi_ch, x_NADPH_ch, Rate Law: Light_on*Rbco_vm*RuBP_ch*chloroplast/(RuBP_ch+Rbco_km*(1+PGA_ch/Rbco_KiPGA+FBP_ch/Rbco_KiFBP+SBP_ch/Rbco_KiSBP+Pi_ch/Rbco_KiPi+x_NADPH_ch/Rbco_KiNADPH)) |
TP_Piap_vm=250.0; TP_Piap_kPGA_ch=0.25; TP_Piap_kDHAP_ch=0.077; TP_Piap_kPi_ch=0.63; TP_Piap_kGAP_ch=0.075; TP_Piap_kPi_cyt=0.74 |
Reaction: x_Pi_cyt + GAP_ch => x_GAP_cyt + Pi_ch; PGA_ch, DHAP_ch, Rate Law: TP_Piap_vm*GAP_ch*chloroplast/(TP_Piap_kGAP_ch*(1+(1+TP_Piap_kPi_cyt/x_Pi_cyt)*(Pi_ch/TP_Piap_kPi_ch+PGA_ch/TP_Piap_kPGA_ch+DHAP_ch/TP_Piap_kDHAP_ch+GAP_ch/TP_Piap_kGAP_ch))) |
E_Aldo_v=5.0E8; q8=13.0 |
Reaction: DHAP_ch + E4P_ch => SBP_ch, Rate Law: chloroplast*E_Aldo_v*(E4P_ch*DHAP_ch-SBP_ch/q8) |
q12=0.67; X5Pepi_v=5.0E8 |
Reaction: X5P_ch => Ru5P_ch, Rate Law: chloroplast*X5Pepi_v*(X5P_ch-Ru5P_ch/q12) |
Light_on = 1.0; SBPase_ch_km=0.013; SBPase_ch_vm=40.0; SBPase_ch_KiPi=12.0 |
Reaction: SBP_ch => Pi_ch + S7P_ch, Rate Law: Light_on*SBPase_ch_vm*SBP_ch*chloroplast/(SBP_ch+SBPase_ch_km*(1+Pi_ch/SBPase_ch_KiPi)) |
q2=3.1E-4; PGK_v=5.0E8; Light_on = 1.0 |
Reaction: PGA_ch + ATP_ch => BPGA_ch + ADP_ch, Rate Law: Light_on*PGK_v*chloroplast*(PGA_ch*ATP_ch-BPGA_ch*ADP_ch/q2) |
stsyn_ch_km1=0.08; stsyn_ch_Ki=10.0; stsyn_ch_ka2=0.02; stsyn_ch_ka1=0.1; stsyn_ch_ka3=0.02; StSyn_vm=40.0; stsyn_ch_km2=0.08 |
Reaction: ATP_ch + G1P_ch => x_Starch_ch + ADP_ch + Pi_ch; PGA_ch, F6P_ch, FBP_ch, Rate Law: StSyn_vm*G1P_ch*ATP_ch*chloroplast/((G1P_ch+stsyn_ch_km1)*(1+ADP_ch/stsyn_ch_Ki)*(ATP_ch+stsyn_ch_km2)+stsyn_ch_km2*Pi_ch/(stsyn_ch_ka1*PGA_ch)+stsyn_ch_ka2*F6P_ch+stsyn_ch_ka3*FBP_ch) |
q5=7.1; F_Aldo_v=5.0E8 |
Reaction: GAP_ch + DHAP_ch => FBP_ch, Rate Law: F_Aldo_v*chloroplast*(DHAP_ch*GAP_ch-FBP_ch/q5) |
TP_Piap_vm=250.0; PGA_xpMult=0.75; TP_Piap_kPGA_ch=0.25; TP_Piap_kDHAP_ch=0.077; TP_Piap_kPi_ch=0.63; TP_Piap_kGAP_ch=0.075; TP_Piap_kPi_cyt=0.74 |
Reaction: x_Pi_cyt + PGA_ch => x_PGA_cyt + Pi_ch; DHAP_ch, GAP_ch, Rate Law: PGA_xpMult*TP_Piap_vm*PGA_ch*chloroplast/(TP_Piap_kPGA_ch*(1+(1+TP_Piap_kPi_cyt/x_Pi_cyt)*(Pi_ch/TP_Piap_kPi_ch+PGA_ch/TP_Piap_kPGA_ch+DHAP_ch/TP_Piap_kDHAP_ch+GAP_ch/TP_Piap_kGAP_ch))) |
LR_kmPi=0.3; Light_on = 1.0; LR_kmADP=0.014; LR_vm=3500.0 |
Reaction: Pi_ch + ADP_ch => ATP_ch, Rate Law: Light_on*LR_vm*ADP_ch*Pi_ch*chloroplast/((ADP_ch+LR_kmADP)*(Pi_ch+LR_kmPi)) |
States:
Name | Description |
---|---|
E4P ch |
[D-erythrose 4-phosphate; D-Erythrose 4-phosphate] |
DHAP ch |
[dihydroxyacetone phosphate; Glycerone phosphate] |
PGA ch |
[3-Phospho-D-glycerate] |
x NADPH ch |
[NADPH; NADPH] |
x PGA cyt |
[3-Phospho-D-glycerate] |
x DHAP cyt |
[dihydroxyacetone phosphate; Glycerone phosphate] |
R5P ch |
[aldehydo-D-ribose 5-phosphate; D-Ribose 5-phosphate] |
ADP ch |
[ADP; ADP] |
FBP ch |
[beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate] |
Pi ch |
[phosphate(3-); Orthophosphate] |
S7P ch |
[sedoheptulose 7-phosphate; Sedoheptulose 7-phosphate] |
Ru5P ch |
[D-ribulose 5-phosphate; D-Ribulose 5-phosphate] |
x Pi cyt |
[phosphate(3-); Orthophosphate] |
GAP ch |
[glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate] |
RuBP ch |
[D-Ribulose 1,5-bisphosphate] |
ATP ch |
[ATP; ATP] |
x Starch ch |
[Starch] |
BPGA ch |
[3-Phospho-D-glyceroyl phosphate] |
x Proton ch |
[proton] |
x GAP cyt |
[glyceraldehyde 3-phosphate; Glyceraldehyde 3-phosphate] |
x NADP ch |
[NADP(+); NADP+] |
G6P ch |
[D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate] |
F6P ch |
[beta-D-fructofuranose 6-phosphate(2-)] |
X5P ch |
[D-xylulose 5-phosphate; D-Xylulose 5-phosphate] |
x CO2 |
[carbon dioxide; CO2] |
G1P ch |
[alpha-D-glucose 1-phosphate(2-); D-Glucose 1-phosphate] |
SBP ch |
[sedoheptulose 1,7-bisphosphate; Sedoheptulose 1,7-bisphosphate] |
Observables: none
MODEL3618435756
@ v0.0.1
This is the full scale model of the Arabidopsis metabolic network described in the article: A Genome-scale Metabolic M…
DetailsWe describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast. link: http://identifiers.org/pubmed/19755544
Parameters: none
States: none
Observables: none
MODEL3618487388
@ v0.0.1
This is the reduced model of the Arabidopsis metabolic network described in the article: A Genome-scale Metabolic Mode…
DetailsWe describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast. link: http://identifiers.org/pubmed/19755544
Parameters: none
States: none
Observables: none
MODEL8684444027
@ v0.0.1
This a model from the article: Mathematical model for the androgenic regulation of the prostate in intact and castrate…
DetailsThe testicular-hypothalamic-pituitary axis regulates male reproductive system functions. Understanding these regulatory mechanisms is important for assessing the reproductive effects of environmental and pharmaceutical androgenic and antiandrogenic compounds. A mathematical model for the dynamics of androgenic synthesis, transport, metabolism, and regulation of the adult rodent ventral prostate was developed on the basis of a model by Barton and Anderson (1997). The model describes the systemic and local kinetics of testosterone (T), 5alpha-dihydrotestosterone (DHT), and luteinizing hormone (LH), with metabolism of T to DHT by 5alpha-reductase in liver and prostate. Also included are feedback loops for the positive regulation of T synthesis by LH and negative regulation of LH by T and DHT. The model simulates maintenance of the prostate as a function of hormone concentrations and androgen receptor (AR)-mediated signal transduction. The regulatory processes involved in prostate size and function include cell proliferation, apoptosis, fluid production, and 5alpha-reductase activity. Each process is controlled through the occupancy of a representative gene by androgen-AR dimers. The model simulates prostate dynamics for intact, castrated, and intravenous T-injected rats. After calibration, the model accurately captures the castration-induced regression of the prostate compared with experimental data that show that the prostate regresses to approximately 17 and 5% of its intact weight at 14 and 30 days postcastration, respectively. The model also accurately predicts serum T and AR levels following castration compared with data. This model provides a framework for quantifying the kinetics and effects of environmental and pharmaceutical endocrine active compounds on the prostate. link: http://identifiers.org/pubmed/16757547
Parameters: none
States: none
Observables: none
BIOMD0000000800
@ v0.0.1
This is a basic mathematical model describing the dynamics of three cell lines (normal host cells, leukemic host cells a…
DetailsIn this paper a basic mathematical model is introduced to describe the dynamics of three cell lines after allogeneic stem cell transplantation: normal host cells, leukemic host cells and donor cells. Their evolution is one of competitive type and depends upon kinetic and cellcell interaction parameters. Numerical simulations prove that the evolution can ultimately lead either to the normal hematopoietic state achieved by the expansion of the donor cells and the elimination of the host cells, or to the leukemic hematopoietic state characterized by the proliferation of the cancer line and the suppression of the other cell lines. One state or the other is reached depending on cellcell interactions (anti-host, anti-leukemia and anti-graft effects) and initial cell concentrations at transplantation. The model also provides a theoretical basis for the control of post-transplant evolution aimed at the achievement of normal hematopoiesis. link: http://identifiers.org/doi/10.1142/S1793524511001684
Parameters:
Name | Description |
---|---|
C = 0.01 |
Reaction: y =>, Rate Law: compartment*C*y |
epsilon = 1.0; A = 0.45; B = 2.2E-8; G = 2.0 |
Reaction: => y; x, z, Rate Law: compartment*A/(1+B*(x+y+z))*(x+y+epsilon)/(x+y+epsilon+G*z)*y |
epsilon = 1.0; b = 2.2E-8; h = 2.0; a = 0.23 |
Reaction: => z; x, y, Rate Law: compartment*a/(1+b*(x+y+z))*(1-h*(x+y)/(z+epsilon+h*(x+y)))*z |
epsilon = 1.0; b = 2.2E-8; a = 0.23; g = 2.0 |
Reaction: => x; y, z, Rate Law: compartment*a/(1+b*(x+y+z))*(x+y+epsilon)/(x+y+epsilon+g*z)*x |
c = 0.01 |
Reaction: z =>, Rate Law: compartment*c*z |
States:
Name | Description |
---|---|
x |
[hematopoietic stem cell; bone marrow] |
z |
[hematopoietic stem cell; bone marrow] |
y |
[leukemic stem cell; bone marrow] |
Observables: none
MODEL8683876463
@ v0.0.1
This a model from the article: Simulation study of cellular electric properties in heart failure Priebe L, Beuckelma…
DetailsPatients with severe heart failure are at high risk of sudden cardiac death. In the majority of these patients, sudden cardiac death is thought to be due to ventricular tachyarrhythmias. Alterations of the electric properties of single myocytes in heart failure may favor the occurrence of ventricular arrhythmias in these patients by inducing early or delayed afterdepolarizations. Mathematical models of the cellular action potential and its underlying ionic currents could help to elucidate possible arrhythmogenic mechanisms on a cellular level. In the present study, selected ionic currents based on human data are incorporated into a model of the ventricular action potential for the purpose of studying the cellular electrophysiological consequences of heart failure. Ionic currents that are not yet sufficiently characterized in human ventricular myocytes are adopted from the action potential model developed by Luo and Rudy (LR model). The main results obtained from this model are as follows: The action potential in ventricular myocytes from failing hearts is longer than in nonfailing control hearts. The major underlying mechanisms for this prolongation are the enhanced activity of the Na+-Ca2+ exchanger, the slowed diastolic decay of the [Ca2+]i transient, and the reduction of the inwardly rectifying K+ current and the Na+-K+ pump current in myocytes of failing hearts. Furthermore, the fast and slow components of the delayed rectifier K+ current (I(Kr) and I(Ks), respectively) are of utmost importance in determining repolarization of the human ventricular action potential. In contrast, the influence of the transient outward K+ current on APD is only small in both cell groups. Inhibition of I(Kr) promotes the development of early afterdepolarizations in failing, but not nonfailing, myocytes. Furthermore, spontaneous Ca2+ release from the sarcoplasmic reticulum triggers a premature action potential only in failing myocytes. This model of the ventricular action potential and its alterations in heart failure is intended to serve as a tool for investigating the effects of therapeutic interventions on the electric excitability of the human ventricular myocardium. link: http://identifiers.org/pubmed/9633920
Parameters: none
States: none
Observables: none
BIOMD0000000172
@ v0.0.1
from: **Schemes of fluc control in a model of Saccharomyces cerevisiae glycolysis ** **Pritchard, L and Kell, DB**Eu…
DetailsWe used parameter scanning to emulate changes to the limiting rate for steps in a fitted model of glucose-derepressed yeast glycolysis. Three flux-control regimes were observed, two of which were under the dominant control of hexose transport, in accordance with various experimental studies and other model predictions. A third control regime in which phosphofructokinase exerted dominant glycolytic flux control was also found, but it appeared to be physiologically unreachable by this model, and all realistically obtainable flux control regimes featured hexose transport as a step involving high flux control. link: http://identifiers.org/pubmed/12180966
Parameters:
Name | Description |
---|---|
k_19=21.4 |
Reaction: NAD + AcAld => NADH + Succinate, Rate Law: cell*k_19*AcAld |
k1_15=45.0; k2_15=100.0 |
Reaction: ADP => ATP + AMP, Rate Law: cell*(k1_15*ADP*ADP-k2_15*ATP*AMP) |
Kp2g_9=0.08; Kp3g_9=1.2; Keq_9=0.19; Vmax_9=2585.0 |
Reaction: P3G => P2G, Rate Law: cell*Vmax_9*(P3G/Kp3g_9-P2G/(Kp3g_9*Keq_9))/(1+P3G/Kp3g_9+P2G/Kp2g_9) |
KGLYCOGEN_17=6.0 |
Reaction: ATP + G6P => ADP + Glycogen, Rate Law: cell*KGLYCOGEN_17 |
Ktrehalose_18=2.4 |
Reaction: ATP + G6P => ADP + Trehalose, Rate Law: cell*Ktrehalose_18 |
Keq_3=0.29; Kf6p_3=0.3; Vmax_3=1056.0; Kg6p_3=1.4 |
Reaction: G6P => F6P, Rate Law: cell*Vmax_3*(G6P/Kg6p_3-F6P/(Kg6p_3*Keq_3))/(1+G6P/Kg6p_3+F6P/Kf6p_3) |
L0_4=0.66; Kf16_4=0.111; Kamp_4=0.0995; Camp_4=0.0845; Vmax_4=110.0; Cf16_4=0.397; Katp_4=0.71; Kiatp_4=0.65; Kf6p_4=0.1; Ciatp_4=100.0; Catp_4=3.0; Kf26_4=6.82E-4; Cf26_4=0.0174; gR_4=5.12 |
Reaction: ATP + F6P => ADP + F16bP; AMP, F26bP, Rate Law: cell*Vmax_4*gR_4*F6P/Kf6p_4*ATP/Katp_4*(1+F6P/Kf6p_4+ATP/Katp_4+gR_4*F6P/Kf6p_4*ATP/Katp_4)/((1+F6P/Kf6p_4+ATP/Katp_4+gR_4*F6P/Kf6p_4*ATP/Katp_4)^2+L0_4*((1+Ciatp_4*ATP/Kiatp_4)/(1+ATP/Kiatp_4))^2*((1+Camp_4*AMP/Kamp_4)/(1+AMP/Kamp_4))^2*((1+Cf26_4*F26bP/Kf26_4+Cf16_4*F16bP/Kf16_4)/(1+F26bP/Kf26_4+F16bP/Kf16_4))^2*(1+Catp_4*ATP/Katp_4)^2) |
Kgap_5=2.4; Kf16bp_5=0.3; Kdhap_5=2.0; Kigap_5=10.0; Vmax_5=94.69; Keq_5=0.069 |
Reaction: F16bP => DHAP + GAP, Rate Law: cell*Vmax_5*(F16bP/Kf16bp_5-DHAP*GAP/(Kf16bp_5*Keq_5))/(1+F16bP/Kf16bp_5+DHAP/Kdhap_5+GAP/Kgap_5+F16bP*GAP/(Kf16bp_5*Kigap_5)+DHAP*GAP/(Kdhap_5*Kgap_5)) |
Katp_8=0.3; Kp3g_8=0.53; Keq_8=3200.0; Kadp_8=0.2; Vmax_8=1288.0; Kbpg_8=0.003 |
Reaction: ADP + BPG => ATP + P3G, Rate Law: cell*Vmax_8*(Keq_8*BPG*ADP-P3G*ATP)/(Kp3g_8*Katp_8)/((1+BPG/Kbpg_8+P3G/Kp3g_8)*(1+ADP/Kadp_8+ATP/Katp_8)) |
Keq_11=6500.0; Kpyr_11=21.0; Kadp_11=0.53; Vmax_11=1000.0; Kpep_11=0.14; Katp_11=1.5 |
Reaction: ADP + PEP => ATP + PYR, Rate Law: cell*Vmax_11*(PEP*ADP/(Kpep_11*Kadp_11)-PYR*ATP/(Kpep_11*Kadp_11*Keq_11))/((1+PEP/Kpep_11+PYR/Kpyr_11)*(1+ADP/Kadp_11+ATP/Katp_11)) |
Kiacald_13=1.1; Kinad_13=0.92; Keq_13=6.9E-5; Kinadh_13=0.031; Kacald_13=1.11; Kietoh_13=90.0; Knadh_13=0.11; Ketoh_13=17.0; Vmax_13=209.5; Knad_13=0.17 |
Reaction: NAD + EtOH => NADH + AcAld, Rate Law: cell*Vmax_13*(EtOH*NAD/(Ketoh_13*Kinad_13)-AcAld*NADH/(Ketoh_13*Kinad_13*Keq_13))/(1+NAD/Kinad_13+EtOH*Knad_13/(Kinad_13*Ketoh_13)+AcAld*Knadh_13/(Kinadh_13*Kacald_13)+NADH/Kinadh_13+EtOH*NAD/(Kinad_13*Ketoh_13)+NAD*AcAld*Knadh_13/(Kinad_13*Kinadh_13*Kacald_13)+EtOH*NADH*Knad_13/(Kinad_13*Kinadh_13*Ketoh_13)+AcAld*NADH/(Kacald_13*Kinadh_13)+EtOH*NAD*AcAld/(Kinad_13*Kiacald_13*Ketoh_13)+EtOH*AcAld*NADH/(Kietoh_13*Kinadh_13*Kacald_13)) |
Vmax_10=201.6; Kpep_10=0.5; Kp2g_10=0.04; Keq_10=6.7 |
Reaction: P2G => PEP, Rate Law: cell*Vmax_10*(P2G/Kp2g_10-PEP/(Kp2g_10*Keq_10))/(1+P2G/Kp2g_10+PEP/Kpep_10) |
Katpase_14=39.5 |
Reaction: ATP => ADP, Rate Law: cell*Katpase_14*ATP |
Kglc_1=1.1918; Ki_1=0.91; Vmax_1=97.24 |
Reaction: GLCo => GLCi, Rate Law: Vmax_1*(GLCo-GLCi)/Kglc_1/(1+(GLCo+GLCi)/Kglc_1+Ki_1*GLCo*GLCi/Kglc_1^2) |
k2_6=1.0E7; k1_6=450000.0 |
Reaction: DHAP => GAP, Rate Law: cell*(k1_6*DHAP-k2_6*GAP) |
Kadp_2=0.23; Katp_2=0.15; Kg6p_2=30.0; Kglc_2=0.08; Keq_2=2000.0; Vmax_2=236.7 |
Reaction: GLCi + ATP => G6P + ADP, Rate Law: cell*Vmax_2*(GLCi*ATP/(Kglc_2*Katp_2)-G6P*ADP/(Kglc_2*Katp_2*Keq_2))/((1+GLCi/Kglc_2+G6P/Kg6p_2)*(1+ATP/Katp_2+ADP/Kadp_2)) |
C_7=1.0; Vmaxf_7=1152.0; Knadh_7=0.06; Vmaxr_7=6719.0; Knad_7=0.09; Kgap_7=0.21; Kbpg_7=0.0098 |
Reaction: GAP + NAD => BPG + NADH, Rate Law: cell*C_7*(Vmaxf_7*GAP*NAD/(Kgap_7*Knad_7)-Vmaxr_7*BPG*NADH/(Kbpg_7*Knadh_7))/((1+GAP/Kgap_7+BPG/Kbpg_7)*(1+NAD/Knad_7+NADH/Knadh_7)) |
Keq_16=4300.0; Kdhap_16=0.4; Kglycerol_16=1.0; Knadh_16=0.023; Vmax_16=47.11; Knad_16=0.93 |
Reaction: DHAP + NADH => NAD + Glycerol, Rate Law: cell*Vmax_16*(DHAP/Kdhap_16*NADH/Knadh_16-Glycerol/Kdhap_16*NAD/Knadh_16*1/Keq_16)/((1+DHAP/Kdhap_16+Glycerol/Kglycerol_16)*(1+NADH/Knadh_16+NAD/Knad_16)) |
Vmax_12=857.8; nH_12=1.9; Kpyr_12=4.33 |
Reaction: PYR => AcAld + CO2, Rate Law: cell*Vmax_12*(PYR/Kpyr_12)^nH_12/(1+(PYR/Kpyr_12)^nH_12) |
States:
Name | Description |
---|---|
ATP |
[ATP; ATP] |
Trehalose |
[alpha,alpha-trehalose; alpha,alpha-Trehalose] |
F16bP |
[beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate] |
AMP |
[AMP; AMP] |
DHAP |
[dihydroxyacetone phosphate; Glycerone phosphate] |
GLCi |
[D-glucopyranose; D-Glucose] |
P2G |
[2-phospho-D-glyceric acid; 2-Phospho-D-glycerate] |
P3G |
[3-phospho-D-glyceric acid; 3-Phospho-D-glycerate] |
Succinate |
[succinate(2-); Succinate] |
GLCo |
[D-glucopyranose; D-Glucose] |
AcAld |
[acetaldehyde; Acetaldehyde] |
PYR |
[pyruvate; Pyruvate] |
NADH |
[NADH; NADH] |
EtOH |
[ethanol; Ethanol] |
BPG |
[3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate] |
F6P |
[beta-D-fructofuranose 6-phosphate; beta-D-Fructose 6-phosphate] |
CO2 |
[carbon dioxide; CO2] |
Glycerol |
[glycerol; Glycerol] |
GAP |
[D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate] |
G6P |
[alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate] |
Glycogen |
[glycogen; Glycogen] |
NAD |
[NAD(+); NAD+] |
ADP |
[ADP; ADP] |
PEP |
[phosphoenolpyruvate; Phosphoenolpyruvate] |
Observables: none
BIOMD0000000563
@ v0.0.1
Pritchard2014 - plant-microbe interaction[](http://www.researchgate.net/publication/269416257_Phosphoproteomic_analyses_…
Detailslink: http://identifiers.org/pubmed/25382065
Parameters:
Name | Description |
---|---|
V=0.1; Ki=0.1; Km=0.1 |
Reaction: E => E_int; Callose, E, Callose, Rate Law: V*E/(Km+E+Km*Callose/Ki) |
k1=0.1 |
Reaction: Path => PAMP + Path; Path, Rate Law: k1*Path |
k1=0.1; k2=0.1 |
Reaction: R + E_int => R_0; R, E_int, R_0, Rate Law: Cell*(k1*R*E_int-k2*R_0) |
States:
Name | Description |
---|---|
PAMP |
PAMP |
PRR |
PRR* |
Path bulk |
Path_bulk |
Path |
Path |
R 0 |
R* |
Callose |
Callose |
R |
R |
E |
E |
E int |
E_int |
PRR 0 |
PRR |
Observables: none
BIOMD0000000091
@ v0.0.1
Proctor2005 - Actions of chaperones and their role in ageingThis model is described in the article: [Modelling the acti…
DetailsMany molecular chaperones are also known as heat shock proteins because they are synthesised in increased amounts after brief exposure of cells to elevated temperatures. They have many cellular functions and are involved in the folding of nascent proteins, the re-folding of denatured proteins, the prevention of protein aggregation, and assisting the targeting of proteins for degradation by the proteasome and lysosomes. They also have a role in apoptosis and are involved in modulating signals for immune and inflammatory responses. Stress-induced transcription of heat shock proteins requires the activation of heat shock factor (HSF). Under normal conditions, HSF is bound to heat shock proteins resulting in feedback repression. During stress, cellular proteins undergo denaturation and sequester heat shock proteins bound to HSF, which is then able to become transcriptionally active. The induction of heat shock proteins is impaired with age and there is also a decline in chaperone function. Aberrant/damaged proteins accumulate with age and are implicated in several important age-related conditions (e.g. Alzheimer's disease, Parkinson's disease, and cataract). Therefore, the balance between damaged proteins and available free chaperones may be greatly disturbed during ageing. We have developed a mathematical model to describe the heat shock system. The aim of the model is two-fold: to explore the heat shock system and its implications in ageing; and to demonstrate how to build a model of a biological system using our simulation system (biology of ageing e-science integration and simulation (BASIS)). link: http://identifiers.org/pubmed/15610770
Parameters:
Name | Description |
---|---|
k14 = 0.05 |
Reaction: TriH + HSE => HSETriH, Rate Law: k14*HSE*TriH |
k3 = 50.0 |
Reaction: MisP + Hsp90 => MCom, Rate Law: k3*MisP*Hsp90 |
k18 = 12.0 |
Reaction: ADP => ATP, Rate Law: k18*ADP |
k15 = 0.08 |
Reaction: HSETriH => HSE + TriH, Rate Law: k15*HSETriH |
k6 = 6.0E-7 |
Reaction: MisP + ATP => ADP, Rate Law: k6*MisP*ATP |
k4 = 1.0E-5 |
Reaction: MCom => MisP + Hsp90, Rate Law: k4*MCom |
k5 = 4.0E-6 |
Reaction: MCom + ATP => Hsp90 + NatP + ADP, Rate Law: k5*MCom*ATP |
k2 = 2.0E-5 |
Reaction: NatP + ROS => MisP + ROS, Rate Law: k2*NatP*ROS |
k13 = 0.5 |
Reaction: DiH => HSF1, Rate Law: k13*DiH |
k17 = 8.02E-9 |
Reaction: Hsp90 + ATP => ADP, Rate Law: k17*Hsp90*ATP |
k1 = 10.0 |
Reaction: source => NatP, Rate Law: k1 |
k8 = 500.0 |
Reaction: Hsp90 + HSF1 => HCom, Rate Law: k8*Hsp90*HSF1 |
k19 = 0.02 |
Reaction: ATP => ADP, Rate Law: k19*ATP |
k7 = 1.0E-7 |
Reaction: MisP + AggP => AggP, Rate Law: k7*MisP*AggP |
k20 = 0.1 |
Reaction: source => ROS, Rate Law: k20 |
k10 = 0.01 |
Reaction: HSF1 => DiH, Rate Law: (HSF1-1)*k10*HSF1/2 |
k11 = 100.0 |
Reaction: HSF1 + DiH => TriH, Rate Law: k11*HSF1*DiH |
k12 = 0.5 |
Reaction: TriH => HSF1 + DiH, Rate Law: k12*TriH |
k9 = 1.0 |
Reaction: HCom => Hsp90 + HSF1, Rate Law: k9*HCom |
k21 = 0.001 |
Reaction: ROS =>, Rate Law: k21*ROS |
k16 = 1000.0 |
Reaction: HSETriH => HSETriH + Hsp90, Rate Law: k16*HSETriH |
States:
Name | Description |
---|---|
DiH |
[protein complex; IPR000232] |
ROS |
[reactive oxygen species] |
ATP |
[ATP; ATP] |
X |
X |
HSETriH |
HSETriH |
Hsp90 |
[IPR001404] |
MisP |
MisP |
HSF1 |
[IPR000232] |
MCom |
[protein complex] |
HCom |
[protein complex] |
source |
source |
NatP |
NatP |
HSE |
HSE |
ADP |
[ADP; ADP] |
AggP |
AggP |
TriH |
[protein complex; IPR000232] |
Observables: none
BIOMD0000000087
@ v0.0.1
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedic…
DetailsOne of the DNA damage-response mechanisms in budding yeast is temporary cell-cycle arrest while DNA repair takes place. The DNA damage response requires the coordinated interaction between DNA repair and checkpoint pathways. Telomeres of budding yeast are capped by the Cdc13 complex. In the temperature-sensitive cdc13-1 strain, telomeres are unprotected over a specific temperature range leading to activation of the DNA damage response and subsequently cell-cycle arrest. Inactivation of cdc13-1 results in the generation of long regions of single-stranded DNA (ssDNA) and is affected by the activity of various checkpoint proteins and nucleases. This paper describes a mathematical model of how uncapped telomeres in budding yeast initiate the checkpoint pathway leading to cell-cycle arrest. The model was encoded in the Systems Biology Markup Language (SBML) and simulated using the stochastic simulation system Biology of Ageing e-Science Integration and Simulation (BASIS). Each simulation follows the time course of one mother cell keeping track of the number of cell divisions, the level of activity of each of the checkpoint proteins, the activity of nucleases and the amount of ssDNA generated. The model can be used to carry out a variety of in silico experiments in which different genes are knocked out and the results of simulation are compared to experimental data. Possible extensions to the model are also discussed. link: http://identifiers.org/pubmed/17015293
Parameters:
Name | Description |
---|---|
k6a=5.0E-5; kalive = 1.0 |
Reaction: Exo1I => Exo1A, Rate Law: k6a*Exo1I*kalive |
kalive = 1.0; k18a=0.001 |
Reaction: S + ssDNA => S, Rate Law: k18a*S*ssDNA*kalive |
k8a=0.001; kalive = 1.0 |
Reaction: ssDNA + RPA => RPAssDNA1, Rate Law: k8a*RPA*ssDNA*kalive |
k8c=100.0; kalive = 1.0 |
Reaction: ssDNA + RPAssDNA2 => RPAssDNA, Rate Law: k8c*RPAssDNA2*ssDNA*kalive |
kc4=0.01; kalive = 1.0 |
Reaction: M + MCdkA + MG1on => budscar + G1 + MCdkI + MG1off, Rate Law: kc4*M*MCdkA*MG1on*kalive |
kalive = 1.0; k18b=1.0E-5 |
Reaction: G2 + G2Moff + ssDNA => G2 + G2Moff, Rate Law: G2*G2Moff*k18b*ssDNA*kalive |
k19=0.001; kalive = 1.0 |
Reaction: Cdc13 + Rad17Utelo + recovery => Ctelo + Rad17 + recovery, Rate Law: Cdc13*k19*Rad17Utelo*recovery*kalive |
k17a=0.05; kalive = 1.0 |
Reaction: Mec1RPAssDNA + S => Mec1 + RPA + S + ssDNA, Rate Law: k17a*Mec1RPAssDNA*S*kalive |
kalive = 1.0; k16=0.1 |
Reaction: Dun1A + G2Mon => Dun1A + G2Moff, Rate Law: Dun1A*G2Mon*k16*kalive |
k9=100.0; kalive = 1.0 |
Reaction: Rad9Kin + Rad9I => Rad9Kin + Rad9A, Rate Law: k9*Rad9Kin*Rad9I*kalive |
kalive = 1.0; k8d=0.004 |
Reaction: RPAssDNA + Mec1 => Mec1RPAssDNA, Rate Law: k8d*RPAssDNA*Mec1*kalive |
kalive = 1.0; k1=5.0E-4 |
Reaction: Cdc13 + Utelo => Ctelo, Rate Law: k1*Cdc13*Utelo*kalive |
k5=3.0E-4; kalive = 1.0 |
Reaction: ExoXA + Rad17Utelo => ExoXA + Rad17Utelo + ssDNA, Rate Law: k5*ExoXA*Rad17Utelo*kalive |
kalive = 1.0; k7a=3.0E-5 |
Reaction: Utelo + Exo1A => Utelo + Exo1A + ssDNA, Rate Law: k7a*Utelo*Exo1A*kalive |
k15=0.2; kalive = 1.0 |
Reaction: Chk1A + G2Mon => Chk1A + G2Moff, Rate Law: Chk1A*G2Mon*k15*kalive |
kalive = 1.0; k6b=5.0E-4 |
Reaction: Exo1I + Rad24 => Exo1A + Rad24, Rate Law: k6b*Exo1I*Rad24*kalive |
k14=3.3E-6; kalive = 1.0 |
Reaction: Dun1I + Rad53A => Dun1A + Rad53A, Rate Law: Dun1I*k14*Rad53A*kalive |
kalive = 1.0; k4=0.01 |
Reaction: ExoXI + Rad17Utelo => ExoXA + Rad17Utelo, Rate Law: k4*ExoXI*Rad17Utelo*kalive |
kalive = 1.0; k10a=0.05 |
Reaction: ExoXA + Rad9A => ExoXI + Rad9A, Rate Law: ExoXA*k10a*Rad9A*kalive |
kc3=0.0012; kalive = 1.0 |
Reaction: Scyclin => sink, Rate Law: kc3*Scyclin*kalive |
k7b=3.0E-5; kalive = 1.0 |
Reaction: Rad17Utelo + Exo1A => Rad17Utelo + Exo1A + ssDNA, Rate Law: k7b*Rad17Utelo*Exo1A*kalive |
k8b=100.0; kalive = 1.0 |
Reaction: ssDNA + RPAssDNA1 => RPAssDNA2, Rate Law: k8b*RPAssDNA1*ssDNA*kalive |
kc1=0.16; kalive = 1.0 |
Reaction: S => Scyclin + S, Rate Law: kc1*S*kalive |
kalive = 1.0; k3=1.5E-8 |
Reaction: Utelo + Rad17 + Rad24 + ATP => Rad17Utelo + Rad24 + ADP, Rate Law: k3*Utelo*Rad17*Rad24*ATP*kalive/(5000+ATP) |
k10b=0.05; kalive = 1.0 |
Reaction: ExoXA + Rad9I => ExoXI + Rad9I, Rate Law: ExoXA*k10b*Rad9I*kalive |
k17b=0.05; kalive = 1.0 |
Reaction: G2 + G2Moff + Mec1RPAssDNA => G2 + G2Moff + Mec1 + RPA + ssDNA, Rate Law: G2*G2Moff*k17b*Mec1RPAssDNA*kalive |
kalive = 1.0; k2=3.85E-4 |
Reaction: Ctelo => Cdc13 + Utelo, Rate Law: k2*Ctelo*kalive |
k13=1.0; kalive = 1.0 |
Reaction: Exo1A + Rad53A => Exo1I + Rad53A, Rate Law: Exo1A*k13*Rad53A*kalive |
kc2=0.01; kalive = 1.0 |
Reaction: G1Soff + G1 + G1CdkA => G1Son + G1 + G1CdkA, Rate Law: G1*G1CdkA*G1Soff*kc2*kalive |
States:
Name | Description |
---|---|
G2Mon |
[G2/M transition of mitotic cell cycle] |
SG2off |
[obsolete regulation of transcription involved in S phase of mitotic cell cycle] |
G2CdkA |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
Rad9I |
[DNA repair protein RAD9] |
RPAssDNA |
[C00271; PIRSF002091; single-stranded DNA] |
Rad17 |
[DNA damage checkpoint control protein RAD17] |
Dun1I |
[DNA damage response protein kinase DUN1] |
MCdkI |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
Cdc13 |
[Cell division control protein 13] |
Rad17Utelo |
[DNA damage checkpoint control protein RAD17; chromosome, telomeric region] |
RPAssDNA2 |
[C00271; PIRSF002091; single-stranded DNA] |
Exo1I |
[Exodeoxyribonuclease 1] |
M |
[M phase] |
G2CdkI |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
G2 |
[G2 phase] |
G2cyclin |
[G2/mitotic-specific cyclin-1] |
G1CdkA |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
RPA |
[PIRSF002091] |
ExoXA |
[Exodeoxyribonuclease 10] |
RPAssDNA1 |
[C00271; PIRSF002091; single-stranded DNA] |
G1Son |
[G1/S transition of mitotic cell cycle] |
ExoXI |
[Exodeoxyribonuclease 10] |
G1 |
[G1 phase] |
G1CdkI |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
MG1off |
MG1off |
G1Soff |
[mitotic cell cycle checkpoint] |
Ctelo |
[telomere cap complex; chromosome, telomeric region] |
Mec1RPAssDNA |
[Serine/threonine-protein kinase MEC1; C00271; PIRSF002091; single-stranded DNA] |
MCdkA |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
SCdkI |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
Utelo |
[chromosome, telomeric region] |
Mcyclin |
[Meiosis-specific cyclin rem1] |
Mec1 |
[Serine/threonine-protein kinase MEC1] |
SCdkA |
[nuclear cyclin-dependent protein kinase holoenzyme complex] |
S |
[mitotic S phase] |
ssDNA |
[CHEBI:09160; C00271] |
G2Moff |
[G2 DNA damage checkpoint] |
SG2on |
[obsolete regulation of transcription involved in S phase of mitotic cell cycle] |
ADP |
[ADP] |
Exo1A |
[Exodeoxyribonuclease 1] |
Scyclin |
[S-phase entry cyclin-5] |
Rad9A |
[DNA repair protein RAD9] |
Observables: none
BIOMD0000000105
@ v0.0.1
Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome systemThis is a stochastic model of the ubiquitin-…
DetailsThe ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation.The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics.We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process. link: http://identifiers.org/pubmed/17408507
Parameters:
Name | Description |
---|---|
k3 = 4.0E-6 |
Reaction: MisP => NatP + refNatP, Rate Law: k3*MisP |
k61 = 1.7E-5 |
Reaction: MisP + E3 => E3_MisP, Rate Law: k61*MisP*E3 |
k1 = 0.01 |
Reaction: Source => NatP, Rate Law: k1*Source |
k72 = 1.0E-8 |
Reaction: MisP_Ub3 + MisP_Ub4 => AggP, Rate Law: k72*MisP_Ub3*MisP_Ub4 |
k61r = 2.0E-4 |
Reaction: E3_MisP => MisP + E3, Rate Law: k61r*E3_MisP |
k71 = 1.0E-8 |
Reaction: MisP => AggP, Rate Law: k71*MisP*(MisP-1)*0.5 |
k63 = 0.001 |
Reaction: E2 + E1_Ub => E2_Ub + E1, Rate Law: k63*E2*E1_Ub |
k69 = 0.0 |
Reaction: MisP_Ub4_Proteasome + ATP => Ub + Proteasome + ADP + degUb4, Rate Law: k69*MisP_Ub4_Proteasome*ATP/(5000+ATP) |
k65 = 0.01 |
Reaction: MisP_Ub + E2_Ub => MisP_Ub2 + E2, Rate Law: k65*MisP_Ub*E2_Ub |
k2 = 2.0E-6 |
Reaction: NatP + ROS => MisP + ROS + totMisP, Rate Law: k2*NatP*ROS |
k66 = 1.0E-5 |
Reaction: MisP_Ub5 + DUB => MisP_Ub4 + DUB + Ub, Rate Law: k66*MisP_Ub5*DUB |
k64 = 0.001 |
Reaction: E2_Ub + E3_MisP => MisP_Ub + E2 + E3, Rate Law: k64*E2_Ub*E3_MisP |
k67 = 1.0E-5 |
Reaction: MisP_Ub6 + Proteasome => MisP_Ub6_Proteasome, Rate Law: k67*MisP_Ub6*Proteasome |
k62 = 2.0E-4 |
Reaction: E1 + Ub + ATP => E1_Ub + AMP, Rate Law: k62*E1*Ub*ATP/(5000+ATP) |
k68 = 1.0E-5 |
Reaction: MisP_Ub4_Proteasome + DUB => MisP_Ub3 + Proteasome + Ub + DUB, Rate Law: k68*MisP_Ub4_Proteasome*DUB |
States:
Name | Description |
---|---|
MisP Ub4 |
MisP_Ub4 |
MisP Ub5 |
MisP_Ub5 |
MisP Ub7 |
MisP_Ub7 |
E3 MisP |
E3_MisP |
MisP |
MisP |
E1 Ub |
[IPR000011; IPR000626] |
MisP Ub |
MisP_Ub |
MisP Ub8 |
MisP_Ub8 |
MisP Ub6 |
MisP_Ub6 |
MisP Ub5 Proteasome |
MisP_Ub5_Proteasome |
MisP Ub2 |
MisP_Ub2 |
MisP Ub4 Proteasome |
MisP_Ub4_Proteasome |
MisP Ub3 |
MisP_Ub3 |
E2 Ub |
[IPR000626; IPR000608] |
NatP |
NatP |
Observables: none
BIOMD0000000188
@ v0.0.1
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATMThis model is described in the article: [Explaining oscillatio…
DetailsIn individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer. link: http://identifiers.org/pubmed/18706112
Parameters:
Name | Description |
---|---|
ksynp53 = 0.006 psec |
Reaction: p53_mRNA => p53 + p53_mRNA + p53syn, Rate Law: ksynp53*p53_mRNA |
ksynMdm2 = 4.95E-4 psec |
Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2 + mdm2syn, Rate Law: ksynMdm2*Mdm2_mRNA |
IR = 0.0 dGy; kdam = 0.08 molepsecpdGy |
Reaction: => damDNA, Rate Law: kdam*IR |
krepair = 2.0E-5 psec |
Reaction: damDNA => Sink, Rate Law: krepair*damDNA |
kdegMdm2mRNA = 1.0E-4 psec |
Reaction: Mdm2_mRNA => Sink + Mdm2mRNAdeg, Rate Law: kdegMdm2mRNA*Mdm2_mRNA |
kproteff = 1.0 dimensionless; kdegp53 = 8.25E-4 psec |
Reaction: Mdm2_p53 => Mdm2 + p53deg, Rate Law: kdegp53*Mdm2_p53*kproteff |
kdegATMMdm2 = 4.0E-4 psec |
Reaction: Mdm2_P => Sink + mdm2deg, Rate Law: kdegATMMdm2*Mdm2_P |
kdephosp53 = 0.5 psec |
Reaction: p53_P => p53, Rate Law: kdephosp53*p53_P |
kbinMdm2p53 = 0.001155 pmolpsec |
Reaction: p53 + Mdm2 => Mdm2_p53, Rate Law: kbinMdm2p53*p53*Mdm2 |
krelMdm2p53 = 1.155E-5 psec |
Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53 |
kdephosMdm2 = 0.5 psec |
Reaction: Mdm2_P => Mdm2, Rate Law: kdephosMdm2*Mdm2_P |
kphosMdm2 = 2.0 pmolpsec |
Reaction: Mdm2 + ATMA => Mdm2_P + ATMA, Rate Law: kphosMdm2*Mdm2*ATMA |
kproteff = 1.0 dimensionless; kdegMdm2 = 4.33E-4 psec |
Reaction: Mdm2 => Sink + mdm2deg, Rate Law: kdegMdm2*Mdm2*kproteff |
kinactATM = 5.0E-4 psec |
Reaction: ATMA => ATMI, Rate Law: kinactATM*ATMA |
kphosp53 = 5.0E-4 pmolpsec |
Reaction: p53 + ATMA => p53_P + ATMA, Rate Law: kphosp53*p53*ATMA |
kactATM = 1.0E-4 pmolpsec |
Reaction: damDNA + ATMI => damDNA + ATMA, Rate Law: kactATM*damDNA*ATMI |
ksynp53mRNA = 0.001 psec |
Reaction: Source => p53_mRNA, Rate Law: ksynp53mRNA*Source |
kdegp53mRNA = 1.0E-4 psec |
Reaction: p53_mRNA => Sink, Rate Law: kdegp53mRNA*p53_mRNA |
ksynMdm2mRNA = 1.0E-4 psec |
Reaction: p53_P => p53_P + Mdm2_mRNA + Mdm2mRNAsyn, Rate Law: ksynMdm2mRNA*p53_P |
States:
Name | Description |
---|---|
Mdm2 P |
[MDM2; E3 ubiquitin-protein ligase Mdm2] |
mdm2deg |
[proteasome-mediated ubiquitin-dependent protein catabolic process] |
damDNA |
[deoxyribonucleic acid; cellular response to DNA damage stimulus] |
p53 mRNA |
[messenger RNA; RNA] |
Mdm2mRNAdeg |
[mRNA catabolic process] |
mdm2syn |
[translation] |
ATMA |
[Serine-protein kinase ATM] |
p53 |
[Cellular tumor antigen p53; TP53] |
p53deg |
[proteasome-mediated ubiquitin-dependent protein catabolic process] |
totp53 |
totp53 |
Mdm2 p53 |
[Cellular tumor antigen p53; E3 ubiquitin-protein ligase Mdm2] |
Source |
Source |
p53 P |
[Cellular tumor antigen p53; TP53] |
ATMI |
[Serine-protein kinase ATM] |
p53syn |
[translation] |
totMdm2 |
totMdm2 |
Sink |
Sink |
Mdm2 |
[E3 ubiquitin-protein ligase Mdm2; MDM2] |
Mdm2 mRNA |
[messenger RNA; RNA] |
Mdm2mRNAsyn |
[transcription factor activity, sequence-specific DNA binding] |
Observables: none
BIOMD0000000189
@ v0.0.1
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARFThis model is described in the article: [Explaining oscilla…
DetailsIn individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer. link: http://identifiers.org/pubmed/18706112
Parameters:
Name | Description |
---|---|
kbinMdm2p53 = 0.001155 pmolepsec |
Reaction: p53 + Mdm2 => Mdm2_p53, Rate Law: kbinMdm2p53*p53*Mdm2 |
ksynMdm2 = 4.95E-4 psec |
Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2 + mdm2syn, Rate Law: ksynMdm2*Mdm2_mRNA |
IR = 0.0 dGy; kdam = 0.08 molepsecpdGy |
Reaction: => damDNA + totdamDNA, Rate Law: kdam*IR |
kdegMdm2mRNA = 1.0E-4 psec |
Reaction: Mdm2_mRNA => Sink + Mdm2mRNAdeg, Rate Law: kdegMdm2mRNA*Mdm2_mRNA |
krepair = 2.0E-5 psec |
Reaction: damDNA => Sink, Rate Law: krepair*damDNA |
kproteff = 1.0 dimensionless; kdegp53 = 8.25E-4 psec |
Reaction: Mdm2_p53 => Mdm2 + p53deg, Rate Law: kdegp53*Mdm2_p53*kproteff |
ksynp53 = 0.078 psec |
Reaction: Source => p53 + p53syn, Rate Law: ksynp53*Source |
kproteff = 1.0 dimensionless; kdegARF = 1.0E-4 psec |
Reaction: ARF => Sink, Rate Law: kdegARF*ARF*kproteff |
kactARF = 3.3E-5 psec |
Reaction: damDNA => damDNA + ARF, Rate Law: kactARF*damDNA |
krelMdm2p53 = 1.155E-5 psec |
Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53 |
kproteff = 1.0 dimensionless; kdegARFMdm2 = 0.001 psec |
Reaction: ARF_Mdm2 => ARF + mdm2deg, Rate Law: kdegARFMdm2*ARF_Mdm2*kproteff |
kbinARFMdm2 = 0.01 pmolepsec |
Reaction: ARF + Mdm2 => ARF_Mdm2, Rate Law: kbinARFMdm2*ARF*Mdm2 |
kproteff = 1.0 dimensionless; kdegMdm2 = 4.33E-4 psec |
Reaction: Mdm2 => Sink + mdm2deg, Rate Law: kdegMdm2*Mdm2*kproteff |
ksynMdm2mRNA = 1.0E-4 psec |
Reaction: p53 => p53 + Mdm2_mRNA + Mdm2mRNAsyn, Rate Law: ksynMdm2mRNA*p53 |
States:
Name | Description |
---|---|
Mdm2mRNAsyn |
[transcription factor activity, sequence-specific DNA binding] |
ARF Mdm2 |
[E3 ubiquitin-protein ligase Mdm2; Tumor suppressor ARF] |
damDNA |
[deoxyribonucleic acid; cellular response to DNA damage stimulus] |
Mdm2mRNAdeg |
[mRNA catabolic process] |
mdm2syn |
[translation] |
ARF |
[CDKN2A; Tumor suppressor ARF] |
totp53 |
totp53 |
p53 |
[Cellular tumor antigen p53; TP53] |
totdamDNA |
totdamDNA |
p53deg |
[proteasome-mediated ubiquitin-dependent protein catabolic process] |
Source |
Source |
Mdm2 p53 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53] |
p53syn |
[translation] |
totMdm2 |
totMdm2 |
Sink |
Sink |
Mdm2 |
[MDM2; E3 ubiquitin-protein ligase Mdm2] |
Mdm2 mRNA |
[messenger RNA; RNA] |
mdm2deg |
[proteasome-mediated ubiquitin-dependent protein catabolic process] |
Observables: none
BIOMD0000000286
@ v0.0.1
This is the model described the article: GSK3 and p53 - is there a link in Alzheimer's disease? Carole J Proctor and…
DetailsBACKGROUND: Recent evidence suggests that glycogen synthase kinase-3beta (GSK3beta) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3beta and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3beta. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model. RESULTS: The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3beta and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3beta and a vicious cycle ensues. CONCLUSIONS: Since p53 and GSK3beta are both involved in the apoptotic pathway, and GSK3beta overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration. link: http://identifiers.org/pubmed/20181016
Parameters:
Name | Description |
---|---|
kactDUBp53 = 1.0E-7 |
Reaction: Mdm2_p53_Ub4 + p53DUB => Mdm2_p53_Ub3 + p53DUB + Ub, Rate Law: kactDUBp53*Mdm2_p53_Ub4*p53DUB |
krelMTTau = 1.0E-4 |
Reaction: MT_Tau => Tau, Rate Law: krelMTTau*MT_Tau |
krepair = 2.0E-5 |
Reaction: damDNA => Sink, Rate Law: krepair*damDNA |
kphosMdm2GSK3bp53 = 0.5 |
Reaction: Mdm2_p53_Ub4 + GSK3b_p53 => Mdm2_P1_p53_Ub4 + GSK3b_p53, Rate Law: kphosMdm2GSK3bp53*Mdm2_p53_Ub4*GSK3b_p53 |
kaggTauP1 = 1.0E-8 |
Reaction: Tau_P1 => AggTau, Rate Law: kaggTauP1*Tau_P1*(Tau_P1-1)*0.5 |
kaggTauP2 = 1.0E-7 |
Reaction: Tau_P2 => AggTau, Rate Law: kaggTauP2*Tau_P2*(Tau_P2-1)*0.5 |
kdephosMdm2 = 0.5 |
Reaction: Mdm2_P => Mdm2, Rate Law: kdephosMdm2*Mdm2_P |
kdephosp53 = 0.5 |
Reaction: p53_P => p53, Rate Law: kdephosp53*p53_P |
ksynp53mRNAAbeta = 1.0E-5 |
Reaction: Abeta => p53_mRNA + Abeta, Rate Law: ksynp53mRNAAbeta*Abeta |
kbinTauProt = 1.925E-7 |
Reaction: Tau + Proteasome => Proteasome_Tau, Rate Law: kbinTauProt*Tau*Proteasome |
krelGSK3bp53 = 0.002 |
Reaction: GSK3b_p53 => GSK3b + p53, Rate Law: krelGSK3bp53*GSK3b_p53 |
kdegTau20SProt = 0.01 |
Reaction: Proteasome_Tau => Proteasome, Rate Law: kdegTau20SProt*Proteasome_Tau |
krelMdm2p53 = 1.155E-5 |
Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53 |
kaggTau = 1.0E-8 |
Reaction: Tau + AggTau => AggTau, Rate Law: kaggTau*Tau*AggTau |
kphosp53 = 2.0E-4 |
Reaction: p53 + ATMA => p53_P + ATMA, Rate Law: kphosp53*p53*ATMA |
kinactATM = 5.0E-4 |
Reaction: ATMA => ATMI, Rate Law: kinactATM*ATMA |
kgenROSAbeta = 1.0E-5 |
Reaction: AggAbeta => AggAbeta + ROS, Rate Law: kgenROSAbeta*AggAbeta |
kinhibprot = 1.0E-5 |
Reaction: AggTau + Proteasome => AggTau_Proteasome, Rate Law: kinhibprot*AggTau*Proteasome |
kprodAbeta = 5.0E-5 |
Reaction: GSK3b_p53 => Abeta + GSK3b_p53, Rate Law: kprodAbeta*GSK3b_p53 |
kbinGSK3bp53 = 2.0E-6 |
Reaction: GSK3b + p53_P => GSK3b_p53_P, Rate Law: kbinGSK3bp53*GSK3b*p53_P |
kMdm2PolyUb = 0.00456 |
Reaction: Mdm2_Ub2 + E2_Ub => Mdm2_Ub3 + E2, Rate Law: kMdm2PolyUb*Mdm2_Ub2*E2_Ub |
ksynMdm2mRNAGSK3bp53 = 7.0E-4 |
Reaction: GSK3b_p53_P => GSK3b_p53_P + Mdm2_mRNA, Rate Law: ksynMdm2mRNAGSK3bp53*GSK3b_p53_P |
kbinProt = 2.0E-6 |
Reaction: Mdm2_P1_p53_Ub4 + Proteasome => p53_Ub4_Proteasome + Mdm2, Rate Law: kbinProt*Mdm2_P1_p53_Ub4*Proteasome |
kphosMdm2GSK3b = 0.005 |
Reaction: Mdm2_p53_Ub4 + GSK3b => Mdm2_P1_p53_Ub4 + GSK3b, Rate Law: kphosMdm2GSK3b*Mdm2_p53_Ub4*GSK3b |
kbinE1Ub = 2.0E-4 |
Reaction: E1 + Ub + ATP => E1_Ub + AMP, Rate Law: kbinE1Ub*E1*Ub*ATP/(5000+ATP) |
kdegAbeta = 1.0E-4 |
Reaction: Abeta => Sink, Rate Law: kdegAbeta*Abeta |
ksynMdm2 = 4.95E-4 |
Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2, Rate Law: ksynMdm2*Mdm2_mRNA |
kp53Ub = 5.0E-5 |
Reaction: E2_Ub + Mdm2_p53 => Mdm2_p53_Ub + E2, Rate Law: kp53Ub*E2_Ub*Mdm2_p53 |
kphospTauGSK3bp53 = 0.1 |
Reaction: GSK3b_p53_P + Tau => GSK3b_p53_P + Tau_P1, Rate Law: kphospTauGSK3bp53*GSK3b_p53_P*Tau |
kp53PolyUb = 0.01 |
Reaction: Mdm2_p53_Ub + E2_Ub => Mdm2_p53_Ub2 + E2, Rate Law: kp53PolyUb*Mdm2_p53_Ub*E2_Ub |
kproteff = 1.0; kdegp53 = 0.005 |
Reaction: p53_Ub4_Proteasome + ATP => Ub + Proteasome + ADP, Rate Law: kdegp53*kproteff*p53_Ub4_Proteasome*ATP/(5000+ATP) |
kdephospTau = 0.01 |
Reaction: Tau_P1 + PP1 => Tau + PP1, Rate Law: kdephospTau*Tau_P1*PP1 |
ksynTau = 8.0E-5 |
Reaction: Source => Tau, Rate Law: ksynTau*Source |
kdam = 0.08 |
Reaction: => damDNA; IR, Rate Law: kdam*IR |
ksynMdm2mRNA = 5.0E-4 |
Reaction: p53_P => p53_P + Mdm2_mRNA, Rate Law: ksynMdm2mRNA*p53_P |
kMdm2Ub = 4.56E-6 |
Reaction: Mdm2 + E2_Ub => Mdm2_Ub + E2, Rate Law: kMdm2Ub*Mdm2*E2_Ub |
kactATM = 1.0E-4 |
Reaction: damDNA + ATMI => damDNA + ATMA, Rate Law: kactATM*damDNA*ATMI |
kphospTauGSK3b = 2.0E-4 |
Reaction: GSK3b + Tau => GSK3b + Tau_P1, Rate Law: kphospTauGSK3b*GSK3b*Tau |
kdegMdm2 = 0.01; kproteff = 1.0 |
Reaction: Mdm2_Ub4_Proteasome => Proteasome + Ub, Rate Law: kdegMdm2*Mdm2_Ub4_Proteasome*kproteff |
kactDUBMdm2 = 1.0E-7 |
Reaction: Mdm2_Ub2 + Mdm2DUB => Mdm2_Ub + Mdm2DUB + Ub, Rate Law: kactDUBMdm2*Mdm2_Ub2*Mdm2DUB |
kpf = 0.001 |
Reaction: AggAbeta + AbetaPlaque => AbetaPlaque, Rate Law: kpf*AggAbeta*AbetaPlaque |
kaggAbeta = 1.0E-8 |
Reaction: Abeta => AggAbeta, Rate Law: kaggAbeta*Abeta*(Abeta-1)*0.5 |
kphosMdm2 = 2.0 |
Reaction: Mdm2 + ATMA => Mdm2_P + ATMA, Rate Law: kphosMdm2*Mdm2*ATMA |
ksynp53mRNA = 0.001 |
Reaction: Source => p53_mRNA, Rate Law: ksynp53mRNA*Source |
kMdm2PUb = 6.84E-6 |
Reaction: Mdm2_P + E2_Ub => Mdm2_P_Ub + E2, Rate Law: kMdm2PUb*Mdm2_P*E2_Ub |
kdegp53mRNA = 1.0E-4 |
Reaction: p53_mRNA => Sink, Rate Law: kdegp53mRNA*p53_mRNA |
ktangfor = 0.001 |
Reaction: AggTau => NFT, Rate Law: ktangfor*AggTau*(AggTau-1)*0.5 |
kbinMTTau = 0.1 |
Reaction: Tau => MT_Tau, Rate Law: kbinMTTau*Tau |
ksynp53 = 0.007 |
Reaction: p53_mRNA => p53 + p53_mRNA, Rate Law: ksynp53*p53_mRNA |
kdegMdm2mRNA = 5.0E-4 |
Reaction: Mdm2_mRNA => Sink, Rate Law: kdegMdm2mRNA*Mdm2_mRNA |
States:
Name | Description |
---|---|
Mdm2 P |
[E3 ubiquitin-protein ligase Mdm2] |
AggAbeta |
[Amyloid beta A4 protein] |
AggTau Proteasome |
[IPR002955; proteasome complex] |
ATP |
[ATP] |
AbetaPlaque |
[Amyloid beta A4 protein] |
MT Tau |
[IPR015562] |
Ub |
[Ubiquitin-60S ribosomal protein L40] |
Proteasome Tau |
[IPR002955; proteasome complex] |
Mdm2 P Ub3 |
[E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40] |
AMP |
[AMP; AMP] |
Mdm2 Ub2 |
[E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40] |
p53 |
[Cellular tumor antigen p53] |
Source |
Source |
p53 P |
[Cellular tumor antigen p53] |
AggAbeta Proteasome |
[Amyloid beta A4 protein; proteasome complex] |
IR |
IR |
E2 Ub |
[Ubiquitin-60S ribosomal protein L40; IPR000608] |
GSK3b p53 P |
[Glycogen synthase kinase-3 beta; Cellular tumor antigen p53] |
Abeta |
[Amyloid beta A4 protein] |
Mdm2 |
[E3 ubiquitin-protein ligase Mdm2] |
Mdm2DUB |
[IPR001394] |
Tau P1 |
[IPR002955] |
ROS |
[reactive oxygen species] |
GSK3b p53 |
[Glycogen synthase kinase-3 beta; Cellular tumor antigen p53] |
AggTau |
[IPR002955] |
Tau |
[IPR002955] |
PP1 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit] |
damDNA |
[deoxyribonucleic acid] |
Mdm2 P Ub2 |
[E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40] |
Mdm2 p53 Ub |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40] |
Mdm2 p53 Ub3 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40] |
Mdm2 p53 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53] |
NFT |
[IPR002955] |
Mdm2 Ub |
[E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40] |
ATMI |
[Serine-protein kinase ATM] |
Mdm2 p53 Ub4 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40] |
ADP |
[ADP] |
Tau P2 |
[IPR002955] |
Sink |
Sink |
Mdm2 p53 Ub2 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40] |
Observables: none
BIOMD0000000293
@ v0.0.1
This a model from the article: Modelling the Role of UCH-L1 on Protein Aggregation in Age-Related Neurodegeneration.…
DetailsOverexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions. link: http://identifiers.org/pubmed/20949132
Parameters:
Name | Description |
---|---|
kpolyUb = 0.01 |
Reaction: E3_MisP_Ub6 + E2_Ub => E3_MisP_Ub7 + E2, Rate Law: kpolyUb*E3_MisP_Ub6*E2_Ub |
kbinAggProt = 5.0E-9 |
Reaction: AggA1 + Proteasome => AggP_Proteasome, Rate Law: kbinAggProt*AggA1*Proteasome |
kdisaggasyn3 = 6.0E-9 |
Reaction: AggA3 => AggA2 + asyn, Rate Law: kdisaggasyn3*AggA3 |
kgenROSAggP = 2.0E-5 |
Reaction: AggP5 => AggP5 + ROS, Rate Law: kgenROSAggP*AggP5 |
kdisaggasyn5 = 2.0E-9 |
Reaction: AggA5 => AggA4 + asyn, Rate Law: kdisaggasyn5*AggA5 |
kbinProt = 5.0E-6 |
Reaction: Parkin_asyn_dam_Ub7 + Proteasome => asyn_dam_Ub7_Proteasome + Parkin, Rate Law: kbinProt*Parkin_asyn_dam_Ub7*Proteasome |
kdisagg2 = 8.0E-9 |
Reaction: AggP2 => AggP1 + MisP, Rate Law: kdisagg2*AggP2 |
kactDUB = 1.0E-4 |
Reaction: Parkin_asyn_dam_Ub2_DUB => Parkin_asyn_dam_Ub_DUB + Ub, Rate Law: kactDUB*Parkin_asyn_dam_Ub2_DUB |
kdisaggasyn2 = 8.0E-9 |
Reaction: AggA2 => AggA1 + asyn, Rate Law: kdisaggasyn2*AggA2 |
kbinasynDUB = 2.0E-7 |
Reaction: Parkin_asyn_dam_Ub7 + DUB => Parkin_asyn_dam_Ub7_DUB, Rate Law: kbinasynDUB*Parkin_asyn_dam_Ub7*DUB |
krelMisPE3 = 2.0E-4 |
Reaction: E3_MisP => MisP + E3, Rate Law: krelMisPE3*E3_MisP |
kbinSUBUCHL1 = 4.0E-8 |
Reaction: E3SUB_SUB_misfolded_Ub2 + UCHL1 => E3SUB_SUB_misfolded_Ub2_UCHL1, Rate Law: kbinSUBUCHL1*E3SUB_SUB_misfolded_Ub2*UCHL1 |
kaggasyn2 = 5.0E-10 |
Reaction: asyn + AggA2 => AggA3, Rate Law: kaggasyn2*asyn*AggA2 |
kactDUBProt = 1.0E-6 |
Reaction: SUB_misfolded_Ub4_Proteasome + DUB => SUB_misfolded + Proteasome + Ub + DUB, Rate Law: kactDUBProt*SUB_misfolded_Ub4_Proteasome*DUB |
kactProt = 0.01; kproteff = 1.0 |
Reaction: SUB_misfolded_Ub7_Proteasome + ATP => Ub + Proteasome + ADP, Rate Law: kactProt*SUB_misfolded_Ub7_Proteasome*kproteff*ATP/(5000+ATP) |
kbinE2Ub = 0.001 |
Reaction: E2 + E1_Ub => E2_Ub + E1, Rate Law: kbinE2Ub*E2*E1_Ub |
kigrowth2 = 5.0E-9 |
Reaction: E3_MisP_Ub6 + SeqAggP => SeqAggP + aggMisP + aggUb + aggE3, Rate Law: kigrowth2*E3_MisP_Ub6*SeqAggP |
kremROS = 0.001 |
Reaction: ROS => Sink, Rate Law: kremROS*ROS |
kactUchl1 = 1.0E-4 |
Reaction: E3SUB_SUB_misfolded_Ub3_UCHL1 => E3SUB_SUB_misfolded_Ub2_UCHL1 + Ub, Rate Law: kactUchl1*E3SUB_SUB_misfolded_Ub3_UCHL1 |
kubss = 0.1 |
Reaction: MisP => MisP + Ub + upregUb, Rate Law: kubss*MisP^6/(1500^6+MisP^6) |
kbinMisPE3 = 1.0E-4 |
Reaction: MisP + E3 => E3_MisP, Rate Law: kbinMisPE3*MisP*E3 |
States:
Name | Description |
---|---|
aggMisP |
aggMisP |
Ub |
[Ubiquitin-60S ribosomal protein L40] |
SeqAggP |
SeqAggP |
aggUb |
[Ubiquitin-60S ribosomal protein L40] |
MisP |
[protein] |
AggP5 |
AggP5 |
AggA3 |
[Alpha-synuclein] |
E3 MisP Ub7 |
[protein; Ubiquitin-60S ribosomal protein L40; IPR000569] |
Parkin asyn dam Ub6 |
[E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40] |
aggE3 |
[IPR000569] |
E3SUB SUB misfolded Ub3 UCHL1 |
[Ubiquitin carboxyl-terminal hydrolase isozyme L1; Ubiquitin-60S ribosomal protein L40; IPR000569] |
AggA4 |
[Alpha-synuclein] |
aggDUB |
[IPR001394] |
E2 Ub |
[Ubiquitin-60S ribosomal protein L40; IPR000608] |
E3 MisP Ub8 |
[protein; Ubiquitin-60S ribosomal protein L40; IPR000569] |
ROS |
[reactive oxygen species] |
Parkin asyn dam Ub7 |
[E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40] |
E3SUB SUB misfolded Ub2 UCHL1 |
[Ubiquitin carboxyl-terminal hydrolase isozyme L1; Ubiquitin-60S ribosomal protein L40; IPR000569] |
Parkin asyn dam Ub DUB |
[E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40] |
E3 |
[IPR000569] |
E2 |
[IPR000608] |
AggA1 |
[Alpha-synuclein] |
AggA5 |
[Alpha-synuclein] |
E3 MisP Ub6 |
[protein; Ubiquitin-60S ribosomal protein L40; IPR000569] |
Parkin asyn dam Ub8 |
[E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40] |
Parkin asyn dam Ub2 DUB |
[E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40] |
AggA2 |
[Alpha-synuclein] |
Observables: none
BIOMD0000000344
@ v0.0.1
This model is from the article: Modelling the Role of the Hsp70/Hsp90 System in the Maintenance of Protein Homeostas…
DetailsNeurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis. link: http://identifiers.org/pubmed/21779370
Parameters:
Name | Description |
---|---|
kdegHsp90 = 0.01; kalive = 1.0 |
Reaction: Hsp90_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegHsp90*Hsp90_Proteasome*kalive*ATP/(5000+ATP) |
kalive = 1.0; kdephosp38Mkp1 = 0.05 |
Reaction: p38_P + Mkp1_P => p38 + Mkp1_P, Rate Law: kdephosp38Mkp1*p38_P*Mkp1_P*kalive |
kdegMkp1 = 0.01; kalive = 1.0 |
Reaction: Mkp1_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegMkp1*Mkp1_Proteasome*kalive*ATP/(5000+ATP) |
kalive = 1.0; kbinHspMisp = 8.0E-6 |
Reaction: MisP + Hsp70 => Hsp70_MisP, Rate Law: kbinHspMisp*MisP*Hsp70*kalive |
kdephosJnkMkp1 = 0.05; kalive = 1.0 |
Reaction: Jnk_P + Mkp1_P => Jnk + Mkp1_P, Rate Law: kdephosJnkMkp1*Jnk_P*Mkp1_P*kalive |
kalive = 1.0; kbinAggPProt = 1.0E-5 |
Reaction: AggP + Proteasome => AggP_Proteasome, Rate Law: kbinAggPProt*AggP*Proteasome*kalive |
kgenROS = 0.01; kalive = 1.0 |
Reaction: Source => ROS, Rate Law: kgenROS*Source*kalive |
kbinMisPProt = 1.0E-7; kalive = 1.0 |
Reaction: Hsp70_MisP + Proteasome => MisP_Proteasome + Hsp70, Rate Law: kbinMisPProt*Hsp70_MisP*Proteasome*kalive |
kalive = 1.0; kbinHsp90client = 2.0E-4 |
Reaction: Hsp90 + Hsp90Client => Hsp90_Hsp90Client, Rate Law: kbinHsp90client*Hsp90*Hsp90Client*kalive |
kalive = 1.0; kdegAkt = 0.01 |
Reaction: Akt_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegAkt*Akt_Proteasome*kalive*ATP/(5000+ATP) |
kPIdeath = 2.0E-8; kalive = 1.0 |
Reaction: AggP_Proteasome => AggP_Proteasome + PIDeath + CellDeath, Rate Law: kPIdeath*AggP_Proteasome*kalive |
kalive = 1.0; krelHsp70Ppx = 5.0 |
Reaction: Hsp70_Ppx => Hsp70 + Ppx, Rate Law: krelHsp70Ppx*Hsp70_Ppx*kalive |
kalive = 1.0; krelAktProt = 1.0E-8 |
Reaction: Akt_Proteasome => Akt + Proteasome, Rate Law: krelAktProt*Akt_Proteasome*kalive |
kagg = 1.0E-8; kalive = 1.0 |
Reaction: MisP => AggP, Rate Law: kagg*MisP*(MisP-1)*0.5*kalive |
kphosMkp1 = 0.02; kalive = 1.0 |
Reaction: Mkp1 + Hsp70 => Mkp1_P + Hsp70, Rate Law: kphosMkp1*Mkp1*Hsp70*kalive |
kbinHsp90Prot = 1.0E-8; kalive = 1.0 |
Reaction: Hsp90 + Proteasome => Hsp90_Proteasome, Rate Law: kbinHsp90Prot*Hsp90*Proteasome*kalive |
kalive = 1.0; kdegMisP = 0.01 |
Reaction: MisP_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegMisP*MisP_Proteasome*kalive*ATP/(5000+ATP) |
kgenROSp38 = 1.0E-4; kalive = 1.0; kp38act = 1.0 |
Reaction: p38_P => p38_P + ROS, Rate Law: kgenROSp38*p38_P*kalive*kp38act |
kmisfold = 2.0E-6; kalive = 1.0 |
Reaction: NatP + ROS => MisP + ROS, Rate Law: kmisfold*NatP*ROS*kalive |
kbinHsp70client = 2.0E-4; kalive = 1.0 |
Reaction: Hsp70 + Hsp70Client => Hsp70_Hsp70Client, Rate Law: kbinHsp70client*Hsp70*Hsp70Client*kalive |
kbinHsp70Ppx = 0.2; kalive = 1.0 |
Reaction: Hsp70 + Ppx => Hsp70_Ppx, Rate Law: kbinHsp70Ppx*Hsp70*Ppx*kalive |
ksynMkp1 = 1.0E-5; kalive = 1.0 |
Reaction: Source => Mkp1, Rate Law: ksynMkp1*Source*kalive |
kbinHsp70Prot = 1.2E-8; kalive = 1.0 |
Reaction: Hsp70 + Proteasome => Hsp70_Proteasome, Rate Law: kbinHsp70Prot*Hsp70*Proteasome*kalive |
krefold = 5.5E-4; kalive = 1.0 |
Reaction: Hsp90_MisP + ATP => Hsp90 + NatP + ADP, Rate Law: krefold*Hsp90_MisP*kalive*ATP/(5000+ATP) |
kbasalsynHsp70 = 0.008; kalive = 1.0 |
Reaction: Source => Hsp70, Rate Law: kbasalsynHsp70*kalive |
kalive = 1.0; kphosp38 = 0.02 |
Reaction: ROS + p38 => ROS + p38_P, Rate Law: kphosp38*ROS*p38*kalive |
kphosHsf1 = 0.03; kalive = 1.0 |
Reaction: Hsf1_Hsf1_Hsf1 + Pkc => Hsf1_Hsf1_Hsf1_P + Pkc, Rate Law: kphosHsf1*Hsf1_Hsf1_Hsf1*Pkc*kalive |
kbinMkp1Prot = 9.6E-9; kalive = 1.0 |
Reaction: Mkp1 + Proteasome => Mkp1_Proteasome, Rate Law: kbinMkp1Prot*Mkp1*Proteasome*kalive |
kalive = 1.0; kgenROSAggP = 1.0E-6 |
Reaction: AggP => AggP + ROS, Rate Law: kgenROSAggP*AggP*kalive |
kphosJnk = 0.02; kalive = 1.0 |
Reaction: ROS + Jnk => ROS + Jnk_P, Rate Law: kphosJnk*Jnk*ROS*kalive |
kbinAktProt = 6.0E-8; kalive = 1.0 |
Reaction: Akt_CHIP_Hsp90 + Proteasome => Akt_Proteasome + CHIP + Hsp90, Rate Law: kbinAktProt*Akt_CHIP_Hsp90*Proteasome*kalive |
kalive = 1.0; kp38death = 1.5E-7; kp38act = 1.0 |
Reaction: p38_P => p38_P + p38Death + CellDeath, Rate Law: kp38death*p38_P*kalive*kp38act |
kalive = 1.0; kdamHsp = 1.0E-8 |
Reaction: Hsp70 + ROS => Hsp70_dam + ROS, Rate Law: kdamHsp*Hsp70*ROS*kalive |
kbinHsf1Hsp90 = 0.02; kalive = 1.0 |
Reaction: Hsp90 + Hsf1 => Hsf1_Hsp90, Rate Law: kbinHsf1Hsp90*Hsp90*Hsf1*kalive |
krelHsp70client = 5.0; kalive = 1.0 |
Reaction: Hsp70_Hsp70Client => Hsp70 + Hsp70Client, Rate Law: krelHsp70client*Hsp70_Hsp70Client*kalive |
kremROS = 0.001; kalive = 1.0 |
Reaction: ROS => Sink, Rate Law: kremROS*ROS*kalive |
kalive = 1.0; kJnkdeath = 1.5E-7 |
Reaction: Jnk_P => Jnk_P + JNKDeath + CellDeath, Rate Law: kJnkdeath*Jnk_P*kalive |
krelHsf1Hsp90 = 0.5; kalive = 1.0 |
Reaction: Hsf1_Hsp90 => Hsp90 + Hsf1, Rate Law: krelHsf1Hsp90*Hsf1_Hsp90*kalive |
kdegHsp70 = 0.01; kalive = 1.0 |
Reaction: Hsp70_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegHsp70*Hsp70_Proteasome*kalive*ATP/(5000+ATP) |
kdephosHsf1 = 0.01; kalive = 1.0 |
Reaction: Hsf1_Hsf1_Hsf1_P + Hsp70_Ppx => Hsf1_Hsf1_Hsf1 + Hsp70_Ppx, Rate Law: kdephosHsf1*Hsf1_Hsf1_Hsf1_P*Hsp70_Ppx*kalive |
kupregHsp = 0.2; kalive = 1.0 |
Reaction: HSEHsp70_Hsf1_Hsf1_Hsf1_P => HSEHsp70_Hsf1_Hsf1_Hsf1_P + Hsp70, Rate Law: kupregHsp*HSEHsp70_Hsf1_Hsf1_Hsf1_P*kalive |
kalive = 1.0; kdephosMkp1 = 0.001 |
Reaction: Mkp1_P + ROS => Mkp1 + ROS, Rate Law: kdephosMkp1*Mkp1_P*ROS*kalive |
kseqagg = 7.0E-7; kalive = 1.0 |
Reaction: SeqAggP + MisP => SeqAggP, Rate Law: kseqagg*SeqAggP*MisP*kalive |
krelHsp90client = 5.0; kalive = 1.0 |
Reaction: Hsp90_Hsp90Client => Hsp90 + Hsp90Client, Rate Law: krelHsp90client*Hsp90_Hsp90Client*kalive |
kalive = 1.0; krelHspMisp = 8.0E-5 |
Reaction: Hsp90_MisP => MisP + Hsp90, Rate Law: krelHspMisp*Hsp90_MisP*kalive |
States:
Name | Description |
---|---|
Proteasome |
[proteasome complex] |
Mkp1 |
[Dual specificity protein phosphatase 1] |
ATP |
[ATP] |
Jnk |
[Mitogen-activated protein kinase 8] |
Jnk P |
[Mitogen-activated protein kinase 8; Phosphoprotein] |
ROS |
[reactive oxygen species] |
AggP Proteasome |
[protein; proteasome complex] |
p38 P |
[Mitogen-activated protein kinase 14; Phosphoprotein] |
Hsp90 |
[IPR001404] |
p38 |
[Mitogen-activated protein kinase 14] |
SeqAggP |
[protein] |
MisP |
[protein] |
Mkp1 P |
[Dual specificity protein phosphatase 1; Phosphoprotein] |
Hsp70 |
[IPR001023] |
Pkc |
[IPR012233] |
MisP Proteasome |
[protein; proteasome complex] |
Ppx |
[protein serine/threonine phosphatase complex] |
NatP |
[protein] |
Hsp70 Ppx |
[IPR001023; protein serine/threonine phosphatase complex] |
AggP |
[protein] |
Mkp1 Proteasome |
[Dual specificity protein phosphatase 1; proteasome complex] |
Observables: none
BIOMD0000000462
@ v0.0.1
Proctor2012 - Amyloid-beta aggregationThis model supports the current thinking that levels of dimers are important in in…
DetailsAlzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (Aß), particularly Aß₄₂, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting Aß₄₂ immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine Aß₄₂ turnover and its aggregation in detail and to test the effect of immunization against Aß dimers.Our model demonstrates for the first time that even a slight decrease in the clearance rate of Aß₄₂ monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of Aβ are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies. link: http://identifiers.org/pubmed/22748062
Parameters:
Name | Description |
---|---|
kdimer = 1.1783E-7 |
Reaction: Abeta => AbDim; Abeta, Rate Law: kdimer*Abeta*(Abeta-1)*0.5 |
kpf = 2.785E-6 |
Reaction: AbDim => AbP; AbDim, Rate Law: kpf*AbDim*(AbDim-1)*0.5 |
kdegNep = 1.8E-10 |
Reaction: Nep => Sink; Nep, Rate Law: kdegNep*Nep |
kdedimer = 8.4655E-6 |
Reaction: AbDim => Abeta; AbDim, Rate Law: kdedimer*AbDim |
kprod = 1.86E-5 |
Reaction: Source => Abeta; Source, Rate Law: kprod*Source |
kdisagg = 5.4357E-5 |
Reaction: AbP => Abeta; AbP, Rate Law: kdisagg*AbP |
kdeg = 2.1E-5 |
Reaction: Abeta + Nep => Sink + Nep; Abeta, Nep, Rate Law: kdeg*Abeta*Nep*0.001 |
kpg = 0.00574; kpghalf = 4.0 |
Reaction: Abeta + AbP => AbP; Abeta, AbP, Rate Law: kpg*Abeta*AbP^2/(kpghalf^2+AbP^2) |
States:
Name | Description |
---|---|
AbP |
[amyloid-beta; amyloid plaque] |
Source |
AbetaPlaque |
Nep |
[Neprilysin] |
Abeta |
[amyloid-beta] |
Sink |
AbetaPlaque |
AbDim |
[amyloid-beta; protein complex] |
Observables: none
BIOMD0000000504
@ v0.0.1
Proctor2013 - Cartilage breakdown, interventions to reduce collagen releaseThe molecular pathways involved in cartilage…
DetailsObjective. To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions to reduce collagen release. Methods. We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language (SBML), a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein and activity and initiate cartilage collagen breakdown. Simulations were performed in the COPASI software package. Results. The model predicted that simulated inhibition of c-Jun N-terminal kinase (JNK) or p38 mitogen-activated protein kinase, and over-expression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Over-expression of TIMP-1 was much less effective than TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of Janus kinase 1 (JAK1), the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets which are downstream, such as the JNK pathway, rather than close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion. Our study shows the value of computer modelling as a tool for examining possible interventions to reduce cartilage collagen breakdown. The model predicts interventions that either prevent transcription or inhibit activity of collagenases are promising strategies and should be investigated further in an experimental setting. © 2013 American College of Rheumatology. link: http://identifiers.org/pubmed/24285357
Parameters:
Name | Description |
---|---|
kdegMKP1 = 1.0E-4 |
Reaction: MKP1 => Sink; MKP1, Rate Law: kdegMKP1*MKP1 |
kdegADAMTS4 = 5.0E-5 |
Reaction: ADAMTS4 => Sink; ADAMTS4, Rate Law: kdegADAMTS4*ADAMTS4 |
ksynDUSP16 = 0.005; kAP1activity = 1.0 |
Reaction: cFos_cJun => cFos_cJun + DUSP16; cFos_cJun, Rate Law: ksynDUSP16*cFos_cJun*kAP1activity |
ksynMMP1 = 1.5E-4 |
Reaction: MMP1_mRNA => MMP1_mRNA + proMMP1; MMP1_mRNA, Rate Law: ksynMMP1*MMP1_mRNA |
ksyncFosmRNAStat3 = 0.05 |
Reaction: STAT3_P_nuc => STAT3_P_nuc + cFos_mRNA; STAT3_P_nuc, Rate Law: ksyncFosmRNAStat3*STAT3_P_nuc |
kdephosJNKDUSP16 = 0.001 |
Reaction: JNK_P + DUSP16 => JNK + DUSP16; JNK_P, DUSP16, Rate Law: kdephosJNKDUSP16*JNK_P*DUSP16 |
ksynTIMP3mRNAStat3 = 4.0E-5; kAP1activity = 1.0 |
Reaction: STAT3_P_nuc => STAT3_P_nuc + TIMP3_mRNA; STAT3_P_nuc, Rate Law: ksynTIMP3mRNAStat3*STAT3_P_nuc*kAP1activity |
kdephoscFosDUSP16 = 1.0E-4 |
Reaction: cFos_P + DUSP16 => cFos + DUSP16; cFos_P, DUSP16, Rate Law: kdephoscFosDUSP16*cFos_P*DUSP16 |
krelTRAF6PP4 = 1.0E-6 |
Reaction: TRAF6_PP4 => TRAF6 + PP4; TRAF6_PP4, Rate Law: krelTRAF6PP4*TRAF6_PP4 |
ksynPTPRT = 1.0E-4 |
Reaction: STAT3_P_nuc => STAT3_P_nuc + PTPRT; STAT3_P_nuc, Rate Law: ksynPTPRT*STAT3_P_nuc |
kcyt2nucSTAT3 = 0.001 |
Reaction: STAT3_P_cyt => STAT3_P_nuc; STAT3_P_cyt, Rate Law: kcyt2nucSTAT3*STAT3_P_cyt |
ksynSOCS3 = 0.001 |
Reaction: SOCS3_mRNA => SOCS3_mRNA + SOCS3; SOCS3_mRNA, Rate Law: ksynSOCS3*SOCS3_mRNA |
kphosSTAT3 = 0.005 |
Reaction: STAT3_cyt + JAK1_P => STAT3_P_cyt + JAK1_P; STAT3_cyt, JAK1_P, Rate Law: kphosSTAT3*STAT3_cyt*JAK1_P |
kbinTRAF6 = 1.0E-5 |
Reaction: IL1_IL1R_IRAK2 + TRAF6 => IL1_IL1R + IRAK2_TRAF6; IL1_IL1R_IRAK2, TRAF6, Rate Law: kbinTRAF6*IL1_IL1R_IRAK2*TRAF6 |
kdegDUSP16 = 1.3E-4 |
Reaction: DUSP16 => Sink; DUSP16, Rate Law: kdegDUSP16*DUSP16 |
ksynbasalTIMP3mRNA = 2.8E-4 |
Reaction: Source => TIMP3_mRNA; Source, Rate Law: ksynbasalTIMP3mRNA*Source |
kdegcJun = 1.3E-4 |
Reaction: cJun => Sink; cJun, Rate Law: kdegcJun*cJun |
krelMMP1 = 0.001 |
Reaction: MMP1_TIMP1 => MMP1 + TIMP1; MMP1_TIMP1, Rate Law: krelMMP1*MMP1_TIMP1 |
kdephosSTAT3nucPTPRT = 5.0E-4 |
Reaction: STAT3_P_nuc + PTPRT => STAT3_nuc + PTPRT; STAT3_P_nuc, PTPRT, Rate Law: kdephosSTAT3nucPTPRT*STAT3_P_nuc*PTPRT |
krelADAMTS4TIMP1 = 0.001 |
Reaction: ADAMTS4_TIMP1 => ADAMTS4 + TIMP1; ADAMTS4_TIMP1, Rate Law: krelADAMTS4TIMP1*ADAMTS4_TIMP1 |
ksynTIMP1mRNAStat3 = 4.0E-5 |
Reaction: STAT3_P_nuc + TIMP1_DNA => STAT3_P_nuc + TIMP1_DNA + TIMP1_mRNA; STAT3_P_nuc, TIMP1_DNA, Rate Law: ksynTIMP1mRNAStat3*STAT3_P_nuc*TIMP1_DNA |
kdephosSTAT3nuc = 1.0E-7 |
Reaction: STAT3_P_nuc => STAT3_nuc; STAT3_P_nuc, Rate Law: kdephosSTAT3nuc*STAT3_P_nuc |
ksynSOCS3mRNA = 0.006 |
Reaction: STAT3_P_nuc => STAT3_P_nuc + SOCS3_mRNA; STAT3_P_nuc, Rate Law: ksynSOCS3mRNA*STAT3_P_nuc |
ksynDUSP16cJun = 2.0E-4 |
Reaction: cJun_dimer => cJun_dimer + DUSP16; cJun_dimer, Rate Law: ksynDUSP16cJun*cJun_dimer |
ksynADAMTS4 = 5.0E-4 |
Reaction: ADAMTS4_mRNA => ADAMTS4_mRNA + ADAMTS4; ADAMTS4_mRNA, Rate Law: ksynADAMTS4*ADAMTS4_mRNA |
kphoscJun = 1.0E-4 |
Reaction: cJun + JNK_P => cJun_P + JNK_P; cJun, JNK_P, Rate Law: kphoscJun*cJun*JNK_P |
kdegAggrecan = 3.0E-8 |
Reaction: Aggrecan_Collagen2 + ADAMTS4 => ADAMTS4 + Collagen2 + AggFrag; Aggrecan_Collagen2, ADAMTS4, Rate Law: kdegAggrecan*Aggrecan_Collagen2*ADAMTS4 |
kdephosp38 = 0.001 |
Reaction: p38_P => p38; p38_P, Rate Law: kdephosp38*p38_P |
ksynTIMP1 = 2.0E-4 |
Reaction: TIMP1_mRNA => TIMP1_mRNA + TIMP1; TIMP1_mRNA, Rate Law: ksynTIMP1*TIMP1_mRNA |
kdegPTPRT = 5.0E-5 |
Reaction: PTPRT => Sink; PTPRT, Rate Law: kdegPTPRT*PTPRT |
kdegSOCS3mRNA = 4.0E-4 |
Reaction: SOCS3_mRNA => Sink; SOCS3_mRNA, Rate Law: kdegSOCS3mRNA*SOCS3_mRNA |
ksynbasalTIMP1mRNA = 1.4E-4 |
Reaction: TIMP1_DNA => TIMP1_mRNA + TIMP1_DNA; TIMP1_DNA, Rate Law: ksynbasalTIMP1mRNA*TIMP1_DNA |
kdegTIMP3 = 2.0E-5 |
Reaction: TIMP3 => Sink; TIMP3, Rate Law: kdegTIMP3*TIMP3 |
kinhibADAMTS4TIMP1 = 3.0E-6 |
Reaction: ADAMTS4 + TIMP1 => ADAMTS4_TIMP1; ADAMTS4, TIMP1, Rate Law: kinhibADAMTS4TIMP1*ADAMTS4*TIMP1 |
kdegMMP13mRNA = 6.4E-6 |
Reaction: MMP13_mRNA => Sink; MMP13_mRNA, Rate Law: kdegMMP13mRNA*MMP13_mRNA |
ksyncJunmRNAcJun = 0.005 |
Reaction: cJun_dimer => cJun_mRNA + cJun_dimer; cJun_dimer, Rate Law: ksyncJunmRNAcJun*cJun_dimer |
kphosJAK1 = 1.0E-5 |
Reaction: JAK1 + OSM_OSMR => JAK1_P + OSM_OSMR; JAK1, OSM_OSMR, Rate Law: kphosJAK1*JAK1*OSM_OSMR |
kdegTIMP1mRNA = 1.4E-5 |
Reaction: TIMP1_mRNA => Sink; TIMP1_mRNA, Rate Law: kdegTIMP1mRNA*TIMP1_mRNA |
kdegTIMP3mRNA = 1.4E-5 |
Reaction: TIMP3_mRNA => Sink; TIMP3_mRNA, Rate Law: kdegTIMP3mRNA*TIMP3_mRNA |
kinhibMMP1TIMP3 = 1.0E-8 |
Reaction: MMP1 + TIMP3 => MMP1_TIMP3; MMP1, TIMP3, Rate Law: kinhibMMP1TIMP3*MMP1*TIMP3 |
kdephosSTAT3 = 1.0E-5 |
Reaction: STAT3_P_cyt => STAT3_cyt; STAT3_P_cyt, Rate Law: kdephosSTAT3*STAT3_P_cyt |
ksyncJunmRNA = 0.0125; kAP1activity = 1.0 |
Reaction: cFos_cJun => cJun_mRNA + cFos_cJun; cFos_cJun, Rate Law: ksyncJunmRNA*cFos_cJun*kAP1activity |
kdephosSTAT3PTPRT = 8.0E-4 |
Reaction: STAT3_P_cyt + PTPRT => STAT3_cyt + PTPRT; STAT3_P_cyt, PTPRT, Rate Law: kdephosSTAT3PTPRT*STAT3_P_cyt*PTPRT |
kdegMMP13 = 1.0E-6 |
Reaction: MMP13 => Sink; MMP13, Rate Law: kdegMMP13*MMP13 |
ksynPP4cJun = 2.0E-4 |
Reaction: cJun_dimer => cJun_dimer + PP4; cJun_dimer, Rate Law: ksynPP4cJun*cJun_dimer |
ksynMMP1mRNAcJun = 2.0E-4 |
Reaction: cJun_dimer => cJun_dimer + MMP1_mRNA; cJun_dimer, Rate Law: ksynMMP1mRNAcJun*cJun_dimer |
kinhibTRAF6 = 0.5 |
Reaction: TRAF6 + PP4 => TRAF6_PP4; TRAF6, PP4, Rate Law: kinhibTRAF6*TRAF6*PP4 |
kinhibADAMTS4TIMP3 = 5.0E-4 |
Reaction: TIMP3 + ADAMTS4 => ADAMTS4_TIMP3; TIMP3, ADAMTS4, Rate Law: kinhibADAMTS4TIMP3*TIMP3*ADAMTS4 |
kphosJNK = 1.0E-4 |
Reaction: JNK + IRAK2_TRAF6 => JNK_P + IRAK2_TRAF6; JNK, IRAK2_TRAF6, Rate Law: kphosJNK*JNK*IRAK2_TRAF6 |
kdegcJunmRNA = 0.003 |
Reaction: cJun_mRNA => Sink; cJun_mRNA, Rate Law: kdegcJunmRNA*cJun_mRNA |
knuc2cytSTAT3 = 0.001 |
Reaction: STAT3_nuc => STAT3_cyt; STAT3_nuc, Rate Law: knuc2cytSTAT3*STAT3_nuc |
ksynMMP1mRNA = 0.005; kAP1activity = 1.0 |
Reaction: cFos_cJun => cFos_cJun + MMP1_mRNA; cFos_cJun, Rate Law: ksynMMP1mRNA*cFos_cJun*kAP1activity |
kdephosJAK1PTPRT = 0.004 |
Reaction: JAK1_P + PTPRT => JAK1 + PTPRT; JAK1_P, PTPRT, Rate Law: kdephosJAK1PTPRT*JAK1_P*PTPRT |
ksynbasalcJunmRNA = 0.015 |
Reaction: Source => cJun_mRNA; Source, Rate Law: ksynbasalcJunmRNA*Source |
kdephoscJun = 0.01 |
Reaction: cJun_P => cJun; cJun_P, Rate Law: kdephoscJun*cJun_P |
kdephosJAK1 = 4.0E-4 |
Reaction: JAK1_P => JAK1; JAK1_P, Rate Law: kdephosJAK1*JAK1_P |
kdegMMP1 = 1.0E-6 |
Reaction: MMP1 => Sink; MMP1, Rate Law: kdegMMP1*MMP1 |
kdegCollagen2mmp1 = 5.0E-12 |
Reaction: Collagen2 + MMP1 => MMP1 + ColFrag; Collagen2, MMP1, Rate Law: kdegCollagen2mmp1*Collagen2*MMP1 |
kbinSOCS3OSMR = 0.005 |
Reaction: SOCS3 + OSMR => OSMR_SOCS3; SOCS3, OSMR, Rate Law: kbinSOCS3OSMR*SOCS3*OSMR |
kdephosp38MKP1 = 1.0E-5 |
Reaction: p38_P + MKP1 => p38 + MKP1; p38_P, MKP1, Rate Law: kdephosp38MKP1*p38_P*MKP1 |
ksynMMP13mRNA = 5.0E-4; kAP1activity = 1.0 |
Reaction: cFos_cJun => cFos_cJun + MMP13_mRNA; cFos_cJun, Rate Law: ksynMMP13mRNA*cFos_cJun*kAP1activity |
ksynTIMP1mRNA = 5.0E-7; kAP1activity = 1.0 |
Reaction: cFos_cJun + TIMP1_DNA => cFos_cJun + TIMP1_mRNA + TIMP1_DNA; cFos_cJun, TIMP1_DNA, Rate Law: ksynTIMP1mRNA*cFos_cJun*TIMP1_DNA*kAP1activity |
kdegPP4 = 1.0E-4 |
Reaction: PP4 => Sink; PP4, Rate Law: kdegPP4*PP4 |
kdephosJNK = 0.001 |
Reaction: JNK_P => JNK; JNK_P, Rate Law: kdephosJNK*JNK_P |
kdegMMP1mRNA = 6.4E-6 |
Reaction: MMP1_mRNA => Sink; MMP1_mRNA, Rate Law: kdegMMP1mRNA*MMP1_mRNA |
ksynTIMP3mRNA = 5.0E-7; kAP1activity = 1.0 |
Reaction: cFos_cJun => cFos_cJun + TIMP3_mRNA; cFos_cJun, Rate Law: ksynTIMP3mRNA*cFos_cJun*kAP1activity |
kAP1activity = 1.0; ksyncFosmRNA = 5.0E-6 |
Reaction: cFos_cJun => cFos_cJun + cFos_mRNA; cFos_cJun, Rate Law: ksyncFosmRNA*cFos_cJun*kAP1activity |
krelADAMTS4TIMP3 = 0.001 |
Reaction: ADAMTS4_TIMP3 => ADAMTS4 + TIMP3; ADAMTS4_TIMP3, Rate Law: krelADAMTS4TIMP3*ADAMTS4_TIMP3 |
kdegSOCS3 = 8.0E-4 |
Reaction: SOCS3 => Sink; SOCS3, Rate Law: kdegSOCS3*SOCS3 |
ksynTIMP3 = 4.0E-4 |
Reaction: TIMP3_mRNA => TIMP3_mRNA + TIMP3; TIMP3_mRNA, Rate Law: ksynTIMP3*TIMP3_mRNA |
States:
Name | Description |
---|---|
Aggrecan Collagen2 |
[Collagen alpha-1(II) chain; AggrecanAggrecan core protein] |
cFos |
[Proto-oncogene c-Fos] |
cJun |
[Transcription factor AP-1] |
cJun mRNA |
[Transcription factor AP-1; JUN] |
cFos mRNA |
[Proto-oncogene c-Fos; FOS] |
p38 P |
[Mitogen-activated protein kinase 11; 3842] |
TRAF6 PP4 |
[Serine/threonine-protein phosphatase 4 catalytic subunit; TNF receptor-associated factor 6] |
ColFrag |
[Collagen alpha-1(II) chain] |
ADAMTS4 |
[A disintegrin and metalloproteinase with thrombospondin motifs 4] |
TIMP1 mRNA |
[TIMP1; Metalloproteinase inhibitor 1] |
MKP1 |
[Dual specificity protein phosphatase 1] |
STAT3 cyt |
[cytoplasm; Signal transducer and activator of transcription 3] |
JAK1 |
[Tyrosine-protein kinase JAK1] |
DUSP16 |
[Dual specificity protein phosphatase 16] |
MMP13 mRNA |
[MMP13; Collagenase 3] |
STAT3 nuc |
[Signal transducer and activator of transcription 3; nucleus] |
JNK P |
[Mitogen-activated protein kinase 8; 3842] |
STAT3 P nuc |
[Signal transducer and activator of transcription 3; 3842; nucleus] |
SOCS3 |
[Suppressor of cytokine signaling 3] |
ADAMTS4 TIMP1 |
[Metalloproteinase inhibitor 1; A disintegrin and metalloproteinase with thrombospondin motifs 4] |
TIMP3 mRNA |
[TIMP3; Metalloproteinase inhibitor 3] |
SOCS3 mRNA |
[SOCS3; Suppressor of cytokine signaling 3] |
STAT3 P cyt |
[cytoplasm; Signal transducer and activator of transcription 3; 3842] |
p38 |
[Mitogen-activated protein kinase 11] |
IRAK2 TRAF6 |
[TNF receptor-associated factor 6; Interleukin-1 receptor-associated kinase-like 2] |
IRAK2 TRAF6 PP4 |
[Serine/threonine-protein phosphatase 4 catalytic subunit; Interleukin-1 receptor-associated kinase-like 2; TNF receptor-associated factor 6] |
ADAMTS4 TIMP3 |
[Metalloproteinase inhibitor 1; A disintegrin and metalloproteinase with thrombospondin motifs 4] |
proMMP1 |
[Interstitial collagenase] |
TIMP1 DNA |
[deoxyribonucleic acid; Metalloproteinase inhibitor 1] |
JAK1 P |
[Tyrosine-protein kinase JAK1; 3842] |
MMP1 |
[Interstitial collagenase] |
Sink |
Sink |
MMP1 mRNA |
[Interstitial collagenase; MMP1] |
TRAF6 |
[TNF receptor-associated factor 6] |
PP4 |
[Serine/threonine-protein phosphatase 4 catalytic subunit] |
Observables: none
BIOMD0000000488
@ v0.0.1
Proctor2013 - Effect of Aβ immunisation in Alzheimer's disease (deterministic version)Extension of a previously publishe…
DetailsProgress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies. link: http://identifiers.org/pubmed/24098635
Parameters:
Name | Description |
---|---|
kdisaggAbeta2 = 1.0E-6 |
Reaction: AbetaPlaque + antiAb => AbetaDimer + antiAb + disaggPlaque2; antiAb, AbetaPlaque, Rate Law: kdisaggAbeta2*antiAb*AbetaPlaque |
kactDUBp53 = 1.0E-7 |
Reaction: Mdm2_p53_Ub + p53DUB => Mdm2_p53 + p53DUB + Ub; Mdm2_p53_Ub, p53DUB, Rate Law: kactDUBp53*Mdm2_p53_Ub*p53DUB |
kremROS = 7.0E-5 |
Reaction: ROS => Sink; ROS, Rate Law: kremROS*ROS |
kinactglia2 = 5.0E-6 |
Reaction: GliaM2 => GliaM1; GliaM2, Rate Law: kinactglia2*GliaM2 |
kprodAbeta = 1.86E-5 |
Reaction: Source => Abeta; Source, Rate Law: kprodAbeta*Source |
krelMTTau = 1.0E-4 |
Reaction: MT_Tau => Tau; MT_Tau, Rate Law: krelMTTau*MT_Tau |
krepair = 2.0E-5 |
Reaction: damDNA => Sink; damDNA, Rate Law: krepair*damDNA |
kinhibprot = 1.0E-7 |
Reaction: AbetaDimer + Proteasome => AggAbeta_Proteasome; AbetaDimer, Proteasome, Rate Law: kinhibprot*AbetaDimer*Proteasome |
kbinAbantiAb = 1.0E-6 |
Reaction: AbetaDimer + antiAb => AbetaDimer_antiAb; AbetaDimer, antiAb, Rate Law: kbinAbantiAb*AbetaDimer*antiAb |
kactglia1 = 6.0E-7 |
Reaction: GliaM1 + AbetaPlaque => GliaM2 + AbetaPlaque; GliaM1, AbetaPlaque, Rate Law: kactglia1*GliaM1*AbetaPlaque |
kaggTauP1 = 1.0E-8 |
Reaction: Tau_P1 => AggTau; Tau_P1, Rate Law: kaggTauP1*Tau_P1^2*0.5 |
kaggTauP2 = 1.0E-7 |
Reaction: Tau_P2 => AggTau; Tau_P2, Rate Law: kaggTauP2*Tau_P2^2*0.5 |
kdephosMdm2 = 0.5 |
Reaction: Mdm2_P => Mdm2; Mdm2_P, Rate Law: kdephosMdm2*Mdm2_P |
kdephosp53 = 0.5 |
Reaction: p53_P => p53; p53_P, Rate Law: kdephosp53*p53_P |
kbinMdm2p53 = 0.001155 |
Reaction: p53 + Mdm2 => Mdm2_p53; p53, Mdm2, Rate Law: kbinMdm2p53*p53*Mdm2 |
krelGSK3bp53 = 0.002 |
Reaction: GSK3b_p53 => GSK3b + p53; GSK3b_p53, Rate Law: krelGSK3bp53*GSK3b_p53 |
kdegTau20SProt = 0.01 |
Reaction: Proteasome_Tau => Proteasome; Proteasome_Tau, Rate Law: kdegTau20SProt*Proteasome_Tau |
krelMdm2p53 = 1.155E-5 |
Reaction: Mdm2_p53 => p53 + Mdm2; Mdm2_p53, Rate Law: krelMdm2p53*Mdm2_p53 |
kdisaggAbeta = 1.0E-6 |
Reaction: AbetaDimer => Abeta; AbetaDimer, Rate Law: kdisaggAbeta*AbetaDimer |
kaggTau = 1.0E-8 |
Reaction: Tau => AggTau; Tau, Rate Law: kaggTau*Tau^2*0.5 |
kinactATM = 5.0E-4 |
Reaction: ATMA => ATMI; ATMA, Rate Law: kinactATM*ATMA |
kdisaggAbeta1 = 2.0E-4 |
Reaction: AbetaPlaque => AbetaDimer + disaggPlaque1; AbetaPlaque, Rate Law: kdisaggAbeta1*AbetaPlaque |
kdegAbetaGlia = 0.005 |
Reaction: AbetaPlaque_GliaA => GliaA + degAbetaGlia; AbetaPlaque_GliaA, Rate Law: kdegAbetaGlia*AbetaPlaque_GliaA |
kbinGSK3bp53 = 2.0E-6 |
Reaction: GSK3b + p53 => GSK3b_p53; GSK3b, p53, Rate Law: kbinGSK3bp53*GSK3b*p53 |
kgenROSGlia = 1.0E-5 |
Reaction: AbetaPlaque_GliaA => AbetaPlaque_GliaA + ROS; AbetaPlaque_GliaA, Rate Law: kgenROSGlia*AbetaPlaque_GliaA |
kMdm2PolyUb = 0.00456 |
Reaction: Mdm2_Ub2 + E2_Ub => Mdm2_Ub3 + E2; Mdm2_Ub2, E2_Ub, Rate Law: kMdm2PolyUb*Mdm2_Ub2*E2_Ub |
ksynMdm2mRNAGSK3bp53 = 7.0E-4 |
Reaction: GSK3b_p53 => GSK3b_p53 + Mdm2_mRNA; GSK3b_p53, Rate Law: ksynMdm2mRNAGSK3bp53*GSK3b_p53 |
kbinProt = 2.0E-6 |
Reaction: Mdm2_Ub4 + Proteasome => Mdm2_Ub4_Proteasome; Mdm2_Ub4, Proteasome, Rate Law: kbinProt*Mdm2_Ub4*Proteasome |
kphosMdm2GSK3b = 0.005 |
Reaction: Mdm2_p53_Ub4 + GSK3b => Mdm2_P1_p53_Ub4 + GSK3b; Mdm2_p53_Ub4, GSK3b, Rate Law: kphosMdm2GSK3b*Mdm2_p53_Ub4*GSK3b |
kbinE1Ub = 2.0E-4 |
Reaction: E1 + Ub + ATP => E1_Ub + AMP; E1, Ub, ATP, Rate Law: kbinE1Ub*E1*Ub*ATP/(5000+ATP) |
kpghalf = 10.0; kpg = 0.15 |
Reaction: AbetaDimer + AbetaPlaque => AbetaPlaque; AbetaDimer, AbetaPlaque, Rate Law: kpg*AbetaDimer*AbetaPlaque^2/(kpghalf^2+AbetaPlaque^2) |
kaggAbeta = 3.0E-6 |
Reaction: Abeta => AbetaDimer; Abeta, Rate Law: kaggAbeta*Abeta^2*0.5 |
krelAbetaGlia = 5.0E-5 |
Reaction: AbetaPlaque_GliaA => AbetaPlaque + GliaA; AbetaPlaque_GliaA, Rate Law: krelAbetaGlia*AbetaPlaque_GliaA |
kp53Ub = 5.0E-5 |
Reaction: E2_Ub + Mdm2_p53 => Mdm2_p53_Ub + E2; E2_Ub, Mdm2_p53, Rate Law: kp53Ub*E2_Ub*Mdm2_p53 |
kbinAbetaGlia = 1.0E-5 |
Reaction: AbetaPlaque + GliaA => AbetaPlaque_GliaA; AbetaPlaque, GliaA, Rate Law: kbinAbetaGlia*AbetaPlaque*GliaA |
kp53PolyUb = 0.01 |
Reaction: Mdm2_p53_Ub2 + E2_Ub => Mdm2_p53_Ub3 + E2; Mdm2_p53_Ub2, E2_Ub, Rate Law: kp53PolyUb*Mdm2_p53_Ub2*E2_Ub |
kdamROS = 1.0E-5 |
Reaction: ROS => ROS + damDNA; ROS, Rate Law: kdamROS*ROS |
kdam = 0.08 |
Reaction: IR => IR + damDNA; IR, Rate Law: kdam*IR |
ksynMdm2mRNA = 5.0E-4 |
Reaction: p53_P => p53_P + Mdm2_mRNA; p53_P, Rate Law: ksynMdm2mRNA*p53_P |
kgenROSPlaque = 1.0E-5 |
Reaction: AbetaPlaque => AbetaPlaque + ROS; AbetaPlaque, Rate Law: kgenROSPlaque*AbetaPlaque |
kactATM = 1.0E-4 |
Reaction: damDNA + ATMI => damDNA + ATMA; damDNA, ATMI, Rate Law: kactATM*damDNA*ATMI |
kphospTauGSK3b = 2.0E-4 |
Reaction: GSK3b + Tau => GSK3b + Tau_P1; GSK3b, Tau, Rate Law: kphospTauGSK3b*GSK3b*Tau |
kdegMdm2 = 0.01; kproteff = 1.0 |
Reaction: Mdm2_Ub4_Proteasome => Proteasome + Ub; Mdm2_Ub4_Proteasome, Rate Law: kdegMdm2*Mdm2_Ub4_Proteasome*kproteff |
kactDUBMdm2 = 1.0E-7 |
Reaction: Mdm2_Ub + Mdm2DUB => Mdm2 + Mdm2DUB + Ub; Mdm2_Ub, Mdm2DUB, Rate Law: kactDUBMdm2*Mdm2_Ub*Mdm2DUB |
kdegAbeta = 1.5E-5 |
Reaction: Abeta_antiAb => antiAb; Abeta_antiAb, Rate Law: 10*kdegAbeta*Abeta_antiAb |
kgenROSAbeta = 2.0E-5 |
Reaction: AggAbeta_Proteasome => AggAbeta_Proteasome + ROS; AggAbeta_Proteasome, Rate Law: kgenROSAbeta*AggAbeta_Proteasome |
kphosMdm2 = 2.0 |
Reaction: Mdm2 + ATMA => Mdm2_P + ATMA; Mdm2, ATMA, Rate Law: kphosMdm2*Mdm2*ATMA |
kactglia2 = 6.0E-7 |
Reaction: GliaM2 + antiAb => GliaA + antiAb; GliaM2, antiAb, Rate Law: kactglia2*GliaM2*antiAb |
kMdm2PUb = 6.84E-6 |
Reaction: Mdm2_P + E2_Ub => Mdm2_P_Ub + E2; Mdm2_P, E2_Ub, Rate Law: kMdm2PUb*Mdm2_P*E2_Ub |
kinactglia1 = 5.0E-6 |
Reaction: GliaA => GliaM2; GliaA, Rate Law: kinactglia1*GliaA |
kdegp53mRNA = 1.0E-4 |
Reaction: p53_mRNA => Sink; p53_mRNA, Rate Law: kdegp53mRNA*p53_mRNA |
ktangfor = 0.001 |
Reaction: AggTau => NFT; AggTau, Rate Law: ktangfor*AggTau^2*0.5 |
ksynp53 = 0.007 |
Reaction: p53_mRNA => p53 + p53_mRNA; p53_mRNA, Rate Law: ksynp53*p53_mRNA |
kbinMTTau = 0.1 |
Reaction: Tau => MT_Tau; Tau, Rate Law: kbinMTTau*Tau |
States:
Name | Description |
---|---|
Mdm2 P |
[E3 ubiquitin-protein ligase Mdm2; phosphoprotein] |
antiAb |
[Immunoglobulin] |
Mdm2 p53 Ub2 |
[Cellular tumor antigen p53; E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B] |
AbetaPlaque |
[Amyloid beta A4 protein; urn:miriam:sbo:SBO%3A0000543] |
MT Tau |
[IPR015562] |
Ub |
[Polyubiquitin-B] |
AMP |
[AMP] |
p53 |
[Cellular tumor antigen p53] |
disaggPlaque2 |
disaggPlaque2 |
Mdm2 Ub2 |
[E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B] |
Source |
Source |
GliaA |
[microglial cell] |
p53 P |
[Cellular tumor antigen p53; phosphoprotein] |
IR |
IR |
Mdm2 P Ub |
[Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; phosphoprotein] |
Abeta |
[Amyloid beta A4 protein] |
Mdm2 |
[E3 ubiquitin-protein ligase Mdm2] |
GliaM2 |
[microglial cell] |
ROS |
[reactive oxygen species] |
Proteasome |
[proteasome complex] |
GSK3b p53 |
[Cellular tumor antigen p53; Glycogen synthase kinase-3 beta] |
Mdm2 Ub3 |
[Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2] |
AbetaDimer |
[Amyloid beta A4 protein] |
damDNA |
[deoxyribonucleic acid] |
AbetaDimer antiAb |
[Amyloid beta A4 protein; Immunoglobulin] |
Mdm2 P Ub2 |
[Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; phosphoprotein] |
p53 mRNA |
[Cellular tumor antigen p53] |
GliaM1 |
[microglial cell] |
AggTau |
[IPR002955; urn:miriam:sbo:SBO%3A0000543] |
ATMA |
[Serine-protein kinase ATM; urn:miriam:pato:PATO%3A000234] |
Mdm2 Ub4 |
[Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2] |
Mdm2 p53 |
[E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53] |
AbetaPlaque GliaA |
[Amyloid beta A4 protein; microglial cell; urn:miriam:sbo:SBO%3A0000543] |
Abeta antiAb |
[Amyloid beta A4 protein; Immunoglobulin] |
GSK3b |
[Glycogen synthase kinase-3 beta] |
degAbetaGlia |
degAbetaGlia |
Sink |
Sink |
Mdm2 P Ub4 Proteasome |
[Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; proteasome complex; phosphoprotein] |
Mdm2 Ub4 Proteasome |
[Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; proteasome complex] |
Mdm2 mRNA |
[E3 ubiquitin-protein ligase Mdm2] |
Observables: none
BIOMD0000000634
@ v0.0.1
Proctor2013 - Effect of Aβ immunisation in Alzheimer's disease (stochastic version)Extension of a previously published s…
DetailsProgress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies. link: http://identifiers.org/pubmed/24098635
Parameters:
Name | Description |
---|---|
kdisaggAbeta2 = 1.0E-6 |
Reaction: AbetaPlaque + antiAb => AbetaDimer + antiAb + disaggPlaque2; antiAb, AbetaPlaque, Rate Law: kdisaggAbeta2*antiAb*AbetaPlaque |
kremROS = 7.0E-5 |
Reaction: ROS => Sink; ROS, Rate Law: kremROS*ROS |
kinactglia2 = 5.0E-6 |
Reaction: GliaM1 => GliaI; GliaM1, Rate Law: kinactglia2*GliaM1 |
krelMTTau = 1.0E-4 |
Reaction: MT_Tau => Tau; MT_Tau, Rate Law: krelMTTau*MT_Tau |
krepair = 2.0E-5 |
Reaction: damDNA => Sink; damDNA, Rate Law: krepair*damDNA |
kinhibprot = 1.0E-7 |
Reaction: AggTau + Proteasome => AggTau_Proteasome; AggTau, Proteasome, Rate Law: kinhibprot*AggTau*Proteasome |
kbinAbantiAb = 1.0E-6 |
Reaction: AbetaDimer + antiAb => AbetaDimer_antiAb; AbetaDimer, antiAb, Rate Law: kbinAbantiAb*AbetaDimer*antiAb |
kactglia1 = 6.0E-7 |
Reaction: GliaI + AbetaPlaque => GliaM1 + AbetaPlaque; GliaI, AbetaPlaque, Rate Law: kactglia1*GliaI*AbetaPlaque |
kphosMdm2GSK3bp53 = 0.5 |
Reaction: Mdm2_p53_Ub4 + GSK3b_p53_P => Mdm2_P1_p53_Ub4 + GSK3b_p53_P; Mdm2_p53_Ub4, GSK3b_p53_P, Rate Law: kphosMdm2GSK3bp53*Mdm2_p53_Ub4*GSK3b_p53_P |
kaggTauP1 = 1.0E-8 |
Reaction: Tau_P1 => AggTau; Tau_P1, Rate Law: kaggTauP1*Tau_P1*(Tau_P1-1)*0.5 |
kaggTauP2 = 1.0E-7 |
Reaction: Tau_P2 => AggTau; Tau_P2, Rate Law: kaggTauP2*Tau_P2*(Tau_P2-1)*0.5 |
kdephosp53 = 0.5 |
Reaction: p53_P => p53; p53_P, Rate Law: kdephosp53*p53_P |
kbinMdm2p53 = 0.001155 |
Reaction: p53 + Mdm2 => Mdm2_p53; p53, Mdm2, Rate Law: kbinMdm2p53*p53*Mdm2 |
kbinTauProt = 1.925E-7 |
Reaction: Tau + Proteasome => Proteasome_Tau; Tau, Proteasome, Rate Law: kbinTauProt*Tau*Proteasome |
krelGSK3bp53 = 0.002 |
Reaction: GSK3b_p53_P => GSK3b + p53_P; GSK3b_p53_P, Rate Law: krelGSK3bp53*GSK3b_p53_P |
kdegTau20SProt = 0.01 |
Reaction: Proteasome_Tau => Proteasome; Proteasome_Tau, Rate Law: kdegTau20SProt*Proteasome_Tau |
krelMdm2p53 = 1.155E-5 |
Reaction: Mdm2_p53 => p53 + Mdm2; Mdm2_p53, Rate Law: krelMdm2p53*Mdm2_p53 |
kaggTau = 1.0E-8 |
Reaction: Tau => AggTau; Tau, Rate Law: kaggTau*Tau*(Tau-1)*0.5 |
kdisaggAbeta = 1.0E-6 |
Reaction: AbetaDimer => Abeta; AbetaDimer, Rate Law: kdisaggAbeta*AbetaDimer |
kphosp53 = 2.0E-4 |
Reaction: p53 + ATMA => p53_P + ATMA; p53, ATMA, Rate Law: kphosp53*p53*ATMA |
kinactATM = 5.0E-4 |
Reaction: ATMA => ATMI; ATMA, Rate Law: kinactATM*ATMA |
kdisaggAbeta1 = 2.0E-4 |
Reaction: AbetaPlaque => AbetaDimer + disaggPlaque1; AbetaPlaque, Rate Law: kdisaggAbeta1*AbetaPlaque |
kdegAbetaGlia = 0.005 |
Reaction: AbetaPlaque_GliaA => GliaA + degAbetaGlia; AbetaPlaque_GliaA, Rate Law: kdegAbetaGlia*AbetaPlaque_GliaA |
kprodAbeta2 = 1.86E-5 |
Reaction: GSK3b_p53_P => Abeta + GSK3b_p53_P; GSK3b_p53_P, Rate Law: kprodAbeta2*GSK3b_p53_P |
kbinGSK3bp53 = 2.0E-6 |
Reaction: GSK3b + p53_P => GSK3b_p53_P; GSK3b, p53_P, Rate Law: kbinGSK3bp53*GSK3b*p53_P |
kgenROSGlia = 1.0E-5 |
Reaction: AbetaPlaque_GliaA => AbetaPlaque_GliaA + ROS; AbetaPlaque_GliaA, Rate Law: kgenROSGlia*AbetaPlaque_GliaA |
kMdm2PolyUb = 0.00456 |
Reaction: Mdm2_Ub + E2_Ub => Mdm2_Ub2 + E2; Mdm2_Ub, E2_Ub, Rate Law: kMdm2PolyUb*Mdm2_Ub*E2_Ub |
ksynMdm2mRNAGSK3bp53 = 7.0E-4 |
Reaction: GSK3b_p53 => GSK3b_p53 + Mdm2_mRNA; GSK3b_p53, Rate Law: ksynMdm2mRNAGSK3bp53*GSK3b_p53 |
kbinProt = 2.0E-6 |
Reaction: Mdm2_P1_p53_Ub4 + Proteasome => p53_Ub4_Proteasome + Mdm2; Mdm2_P1_p53_Ub4, Proteasome, Rate Law: kbinProt*Mdm2_P1_p53_Ub4*Proteasome |
kbinE1Ub = 2.0E-4 |
Reaction: E1 + Ub + ATP => E1_Ub + AMP; E1, Ub, ATP, Rate Law: kbinE1Ub*E1*Ub*ATP/(5000+ATP) |
kpghalf = 10.0; kpg = 0.15 |
Reaction: AbetaDimer + AbetaPlaque => AbetaPlaque; AbetaDimer, AbetaPlaque, Rate Law: kpg*AbetaDimer*AbetaPlaque^2/(kpghalf^2+AbetaPlaque^2) |
ksynMdm2 = 4.95E-4 |
Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2; Mdm2_mRNA, Rate Law: ksynMdm2*Mdm2_mRNA |
krelAbetaGlia = 5.0E-5 |
Reaction: AbetaPlaque_GliaA => AbetaPlaque + GliaA; AbetaPlaque_GliaA, Rate Law: krelAbetaGlia*AbetaPlaque_GliaA |
kaggAbeta = 3.0E-6 |
Reaction: Abeta => AbetaDimer; Abeta, Rate Law: kaggAbeta*Abeta*(Abeta-1)*0.5 |
kbinAbetaGlia = 1.0E-5 |
Reaction: AbetaPlaque + GliaA => AbetaPlaque_GliaA; AbetaPlaque, GliaA, Rate Law: kbinAbetaGlia*AbetaPlaque*GliaA |
kphospTauGSK3bp53 = 0.1 |
Reaction: GSK3b_p53 + Tau_P1 => GSK3b_p53 + Tau_P2; GSK3b_p53, Tau_P1, Rate Law: kphospTauGSK3bp53*GSK3b_p53*Tau_P1 |
kpf = 0.2 |
Reaction: AbetaDimer => AbetaPlaque; AbetaDimer, Rate Law: kpf*AbetaDimer*(AbetaDimer-1)*0.5 |
kproteff = 1.0; kdegp53 = 0.005 |
Reaction: p53_Ub4_Proteasome + ATP => Ub + Proteasome + ADP; p53_Ub4_Proteasome, ATP, Rate Law: kdegp53*kproteff*p53_Ub4_Proteasome*ATP/(5000+ATP) |
kdephospTau = 0.01 |
Reaction: Tau_P1 + PP1 => Tau + PP1; Tau_P1, PP1, Rate Law: kdephospTau*Tau_P1*PP1 |
ksynTau = 8.0E-5 |
Reaction: Source => Tau; Source, Rate Law: ksynTau*Source |
kdamROS = 1.0E-5 |
Reaction: ROS => ROS + damDNA; ROS, Rate Law: kdamROS*ROS |
kdam = 0.08 |
Reaction: IR => IR + damDNA; IR, Rate Law: kdam*IR |
ksynMdm2mRNA = 5.0E-4 |
Reaction: p53_P => p53_P + Mdm2_mRNA; p53_P, Rate Law: ksynMdm2mRNA*p53_P |
kgenROSPlaque = 1.0E-5 |
Reaction: AbetaPlaque => AbetaPlaque + ROS; AbetaPlaque, Rate Law: kgenROSPlaque*AbetaPlaque |
kMdm2Ub = 4.56E-6 |
Reaction: Mdm2 + E2_Ub => Mdm2_Ub + E2; Mdm2, E2_Ub, Rate Law: kMdm2Ub*Mdm2*E2_Ub |
kphospTauGSK3b = 2.0E-4 |
Reaction: GSK3b + Tau_P1 => GSK3b + Tau_P2; GSK3b, Tau_P1, Rate Law: kphospTauGSK3b*GSK3b*Tau_P1 |
kdegAntiAb = 2.75E-6 |
Reaction: antiAb => Sink; antiAb, Rate Law: kdegAntiAb*antiAb |
kdegMdm2 = 0.01; kproteff = 1.0 |
Reaction: Mdm2_Ub4_Proteasome => Proteasome + Ub; Mdm2_Ub4_Proteasome, Rate Law: kdegMdm2*Mdm2_Ub4_Proteasome*kproteff |
kactATM = 1.0E-4 |
Reaction: damDNA + ATMI => damDNA + ATMA; damDNA, ATMI, Rate Law: kactATM*damDNA*ATMI |
kactDUBMdm2 = 1.0E-7 |
Reaction: Mdm2_Ub + Mdm2DUB => Mdm2 + Mdm2DUB + Ub; Mdm2_Ub, Mdm2DUB, Rate Law: kactDUBMdm2*Mdm2_Ub*Mdm2DUB |
kdegAbeta = 1.5E-5 |
Reaction: AbetaDimer_antiAb => antiAb; AbetaDimer_antiAb, Rate Law: 10*kdegAbeta*AbetaDimer_antiAb |
kgenROSAbeta = 2.0E-5 |
Reaction: Abeta => Abeta + ROS; Abeta, Rate Law: kgenROSAbeta*Abeta |
kphosMdm2 = 2.0 |
Reaction: Mdm2 + ATMA => Mdm2_P + ATMA; Mdm2, ATMA, Rate Law: kphosMdm2*Mdm2*ATMA |
ksynp53mRNA = 0.001 |
Reaction: Source => p53_mRNA; Source, Rate Law: ksynp53mRNA*Source |
kdegp53mRNA = 1.0E-4 |
Reaction: p53_mRNA => Sink; p53_mRNA, Rate Law: kdegp53mRNA*p53_mRNA |
ktangfor = 0.001 |
Reaction: AggTau => NFT; AggTau, Rate Law: ktangfor*AggTau*(AggTau-1)*0.5 |
ksynp53 = 0.007 |
Reaction: p53_mRNA => p53 + p53_mRNA; p53_mRNA, Rate Law: ksynp53*p53_mRNA |
kbinMTTau = 0.1 |
Reaction: Tau => MT_Tau; Tau, Rate Law: kbinMTTau*Tau |
kdegMdm2mRNA = 5.0E-4 |
Reaction: Mdm2_mRNA => Sink; Mdm2_mRNA, Rate Law: kdegMdm2mRNA*Mdm2_mRNA |
States:
Name | Description |
---|---|
Mdm2 P |
[E3 ubiquitin-protein ligase Mdm2; phosphoprotein] |
AggTau Proteasome |
[urn:miriam:sbo:SBO%3A0000543; IPR002955; proteasome complex] |
MT Tau |
[IPR002955] |
Proteasome Tau |
[IPR002955; proteasome complex] |
AMP |
[AMP] |
p53 |
[Cellular tumor antigen p53] |
Mdm2 Ub2 |
[E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B] |
Source |
Source |
p53 P |
[Cellular tumor antigen p53; phosphoprotein] |
IR |
IR |
E1 |
[IPR000011] |
GSK3b p53 P |
[Cellular tumor antigen p53; Glycogen synthase kinase-3 beta; phosphoprotein] |
Abeta |
[Amyloid beta A4 protein] |
Mdm2 |
[E3 ubiquitin-protein ligase Mdm2] |
ROS |
[reactive oxygen species] |
Tau P1 |
[IPR002955] |
Proteasome |
[proteasome complex] |
damDNA |
[deoxyribonucleic acid] |
AggTau |
[urn:miriam:sbo:SBO%3A0000543; IPR002955] |
AbetaDimer |
[Amyloid beta A4 protein] |
AbetaDimer antiAb |
[Amyloid beta A4 protein; Immunoglobulin] |
GliaM1 |
[microglial cell] |
p53 mRNA |
[Cellular tumor antigen p53] |
PP1 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit] |
ATMA |
[Serine-protein kinase ATM; active] |
AbetaPlaque GliaA |
[Amyloid beta A4 protein; microglial cell; urn:miriam:sbo:SBO%3A0000543] |
Mdm2 Ub |
[E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B] |
ATMI |
[Serine-protein kinase ATM; inactive] |
ADP |
[ADP] |
Tau P2 |
[IPR002955] |
GliaI |
[microglial cell] |
Sink |
Sink |
Mdm2 mRNA |
[E3 ubiquitin-protein ligase Mdm2] |
Observables: none
BIOMD0000000612
@ v0.0.1
Proctor2016 - Circadian rhythm of PTH and the dynamics of signaling molecules on bone remodelingThis model is described…
DetailsBone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-β, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing. link: http://identifiers.org/pubmed/27379013
Parameters:
Name | Description |
---|---|
kdegSost = 0.004 |
Reaction: Sost => Sink; Sost, Rate Law: kdegSost*Sost |
ksecRANKLbyOcyI = 1.0E-7 |
Reaction: Ocy_I => Ocy_I + RANKL; Ocy_I, Rate Law: ksecRANKLbyOcyI*Ocy_I |
kdiffHSC = 5.5E-5 |
Reaction: HSC + MCSF => HSC + MCSF + Ocl_p; HSC, MCSF, Rate Law: kdiffHSC*HSC*MCSF^2/(50^2+MCSF^2) |
kbinOclpRANKL = 0.001 |
Reaction: RANKL + Ocl_p => Ocl_p_RANKL; Ocl_p, RANKL, Rate Law: kbinOclpRANKL*Ocl_p*RANKL |
ksecMCSFbyObp = 1.0E-5 |
Reaction: Ob_p => Ob_p + MCSF; Ob_p, Rate Law: ksecMCSFbyObp*Ob_p |
ksecRANKLbyObpTgfb = 4.0E-6 |
Reaction: Ob_p_Tgfb_A => Ob_p_Tgfb_A + RANKL; Ob_p_Tgfb_A, Rate Law: ksecRANKLbyObpTgfb*Ob_p_Tgfb_A |
kdiffMSC = 6.5E-4 |
Reaction: MSC + Wnt_A => MSC + Wnt_A + Ob_pro; MSC, Wnt_A, Rate Law: kdiffMSC*MSC*Wnt_A^2/(50^2+Wnt_A^2) |
kbinBaxBcl2 = 0.01 |
Reaction: Bax + Bcl2 => Bax_Bcl2; Bax, Bcl2, Rate Law: kbinBaxBcl2*Bax*Bcl2 |
krelCrebRunx2 = 0.01 |
Reaction: CREB_Runx2 => CREB_P + Runx2; CREB_Runx2, Rate Law: krelCrebRunx2*CREB_Runx2 |
krelObmPTH = 0.005 |
Reaction: Ob_m_PTH => Ob_m + PTH; Ob_m_PTH, Rate Law: krelObmPTH*Ob_m_PTH |
ksecMCSFbyObpro = 1.0E-5 |
Reaction: Ob_pro => Ob_pro + MCSF; Ob_pro, Rate Law: ksecMCSFbyObpro*Ob_pro |
kactWnt = 0.03 |
Reaction: Wnt_I => Wnt_A; Wnt_I, Rate Law: kactWnt*Wnt_I |
ksecRANKLbyObp = 3.0E-6 |
Reaction: Ob_p => Ob_p + RANKL; Ob_p, Rate Law: ksecRANKLbyObp*Ob_p |
kdegBone = 6.5E-9 |
Reaction: Ocl_m + Bone => Ocl_m; Ocl_m, Bone, Rate Law: kdegBone*Ocl_m*Bone |
ksecRANKLbyObm = 1.0E-7 |
Reaction: Ob_m => Ob_m + RANKL; Ob_m, Rate Law: ksecRANKLbyObm*Ob_m |
kdiffObP = 1.0E-4 |
Reaction: Ob_p => Ob_m; Ob_p, Rate Law: kdiffObP*Ob_p |
kmatOb = 2.0E-9 |
Reaction: Ob_m => Ocy_I; Ob_m, Rate Law: kmatOb*Ob_m |
kdeathOb = 2.4E-4 |
Reaction: Ob_m_PTH + Bax => Bax + PTH; Ob_m_PTH, Bax, Rate Law: kdeathOb*Ob_m_PTH*Bax^2/(50^2+Bax^2) |
kformBone = 3.07E-6 |
Reaction: Ob_m_PTH => Ob_m_PTH + Bone + newbone; Ob_m_PTH, Rate Law: kformBone*Ob_m_PTH |
ksecRANKLbyObpro = 7.0E-6 |
Reaction: Ob_pro => Ob_pro + RANKL; Ob_pro, Rate Law: ksecRANKLbyObpro*Ob_pro |
kactCreb = 0.009 |
Reaction: Ob_m_PTH + CREB => Ob_m_PTH + CREB_P; CREB, Ob_m_PTH, Rate Law: kactCreb*CREB*Ob_m_PTH^2/(100^2+Ob_m_PTH^2) |
ksynPTH = 0.02 |
Reaction: Source => PTH; Source, Rate Law: ksynPTH*Source |
ksecRANKLbyOcy = 1.0E-6 |
Reaction: Ocy_A => Ocy_A + RANKL; Ocy_A, Rate Law: ksecRANKLbyOcy*Ocy_A |
kdeathOcy = 1.0E-8 |
Reaction: Ocy_I => Sink; Ocy_I, Rate Law: kdeathOcy*Ocy_I |
kdiffObproTgfb = 0.05 |
Reaction: Ob_pro + Tgfb_A => Ob_p + Tgfb_A; Ob_pro, Tgfb_A, Rate Law: kdiffObproTgfb*Ob_pro*Tgfb_A^2/(50^2+Tgfb_A^2) |
krelOclpRANKL = 0.001 |
Reaction: Ocl_p_RANKL => Ocl_p + RANKL; Ocl_p_RANKL, Rate Law: krelOclpRANKL*Ocl_p_RANKL |
kdeathOclp = 1.0E-5 |
Reaction: Ocl_p => Sink; Ocl_p, Rate Law: kdeathOclp*Ocl_p |
kdegRANKL = 3.0E-5 |
Reaction: RANKL => Sink; RANKL, Rate Law: kdegRANKL*RANKL |
kinactCreb = 1.0E-4 |
Reaction: CREB_P => CREB; CREB_P, Rate Law: kinactCreb*CREB_P |
krelOcyPTH = 0.005 |
Reaction: Ocy_I_PTH => Ocy_I + PTH; Ocy_I_PTH, Rate Law: krelOcyPTH*Ocy_I_PTH |
kdegRunx2PTH = 0.003 |
Reaction: Ob_m_PTH + Runx2 => Ob_m_PTH; Runx2, Ob_m_PTH, Rate Law: kdegRunx2PTH*Runx2*Ob_m_PTH |
ksecRANKLbyObmPTH = 1.0E-6 |
Reaction: Ob_m_PTH => Ob_m_PTH + RANKL; Ob_m_PTH, Rate Law: ksecRANKLbyObmPTH*Ob_m_PTH |
ksecTgfb = 5.0E-5 |
Reaction: Ob_m => Ob_m + Tgfb_I; Ob_m, Rate Law: ksecTgfb*Ob_m |
kinhibRANKL = 0.001 |
Reaction: OPG + RANKL => OPG_RANKL; OPG, RANKL, Rate Law: kinhibRANKL*OPG*RANKL |
ksynX = 0.01157 |
Reaction: Source => X; Source, Rate Law: ksynX*Source |
krelBaxBcl2 = 0.5 |
Reaction: Bax_Bcl2 => Bax + Bcl2; Bax_Bcl2, Rate Law: krelBaxBcl2*Bax_Bcl2 |
kdegPTH = 0.002 |
Reaction: PTH => Sink; PTH, Rate Law: kdegPTH*PTH |
kdegMCSF = 1.0E-4 |
Reaction: MCSF => Sink; MCSF, Rate Law: kdegMCSF*MCSF |
kbinObpTgfb = 2.0E-4 |
Reaction: Ob_p + Tgfb_A => Ob_p_Tgfb_A; Ob_p, Tgfb_A, Rate Law: kbinObpTgfb*Ob_p*Tgfb_A |
ksynRunx2 = 0.005 |
Reaction: Source => Runx2; Source, Rate Law: ksynRunx2*Source |
kdeathOcl = 6.5E-5 |
Reaction: Ocl_m => Sink; Ocl_m, Rate Law: kdeathOcl*Ocl_m |
kdegTgfb = 5.0E-5 |
Reaction: Tgfb_A => Sink; Tgfb_A, Rate Law: kdegTgfb*Tgfb_A |
kactWntPth = 0.001 |
Reaction: Wnt_I + Ob_m_PTH => Wnt_A + Ob_m_PTH; Wnt_I, Ob_m_PTH, Rate Law: kactWntPth*Wnt_I*Ob_m_PTH |
ksecMCSFbyMSC = 1.0E-5 |
Reaction: MSC => MSC + MCSF; MSC, Rate Law: ksecMCSFbyMSC*MSC |
kdegOPG = 4.0E-6 |
Reaction: OPG => Sink; OPG, Rate Law: kdegOPG*OPG |
krelRANKL = 0.001 |
Reaction: OPG_RANKL => OPG + RANKL; OPG_RANKL, Rate Law: krelRANKL*OPG_RANKL |
kbinCrebRunx2 = 0.01 |
Reaction: CREB_P + Runx2 => CREB_Runx2; CREB_P, Runx2, Rate Law: kbinCrebRunx2*CREB_P*Runx2 |
kdegOPGRANKL = 1.0E-5 |
Reaction: OPG_RANKL => Sink; OPG_RANKL, Rate Law: kdegOPGRANKL*OPG_RANKL |
kinactWnt = 0.8 |
Reaction: Wnt_A + Sost => Wnt_I + Sost; Wnt_A, Sost, Rate Law: kinactWnt*Wnt_A*Sost^2/(50^2+Sost^2) |
kactTgfb = 2.0E-7 |
Reaction: Tgfb_I + Ocl_m => Tgfb_A + Ocl_m; Tgfb_I, Ocl_m, Rate Law: kactTgfb*Tgfb_I*Ocl_m |
kdegRunx2 = 1.0E-4 |
Reaction: Runx2 => Sink; Runx2, Rate Law: kdegRunx2*Runx2 |
ksecOPGbyObp = 2.0E-6 |
Reaction: Ob_p => Ob_p + OPG; Ob_p, Rate Law: ksecOPGbyObp*Ob_p |
kmatObTgfb = 1.0E-8 |
Reaction: Ob_m + Tgfb_A => Ocy_I + Tgfb_A; Ob_m, Tgfb_A, Rate Law: kmatObTgfb*Ob_m*Tgfb_A^2/(50^2+Tgfb_A^2) |
kdegTgfbPTH = 1.7E-5 |
Reaction: Tgfb_A + Ob_m_PTH => Ob_m_PTH; Tgfb_A, Ob_m_PTH, Rate Law: kdegTgfbPTH*Tgfb_A*Ob_m_PTH |
ksecMCSFbyObm = 1.0E-5 |
Reaction: Ob_m_PTH => Ob_m_PTH + MCSF; Ob_m_PTH, Rate Law: ksecMCSFbyObm*Ob_m_PTH |
kdegBcl2 = 0.0025 |
Reaction: Bcl2 => Sink; Bcl2, Rate Law: kdegBcl2*Bcl2 |
kbinObpPTH = 3.0E-4 |
Reaction: Ob_p + PTH => Ob_p_PTH; Ob_p, PTH, Rate Law: kbinObpPTH*Ob_p*PTH^2/(100^2+PTH^2) |
kbinObmPTH = 0.02 |
Reaction: Ob_m + PTH => Ob_m_PTH; Ob_m, PTH, Rate Law: kbinObmPTH*Ob_m*PTH^2/(100^2+PTH^2) |
ksynBcl2 = 0.005 |
Reaction: CREB_Runx2 => CREB_Runx2 + Bcl2; CREB_Runx2, Rate Law: ksynBcl2*CREB_Runx2 |
krelObpPTH = 0.005 |
Reaction: Ob_p_PTH => Ob_p + PTH; Ob_p_PTH, Rate Law: krelObpPTH*Ob_p_PTH |
ksecRANKLbyMSC = 1.0E-6 |
Reaction: MSC => MSC + RANKL; MSC, Rate Law: ksecRANKLbyMSC*MSC |
kunload = 3.5E-4 |
Reaction: LOAD => Sink; LOAD, Rate Law: kunload*LOAD |
ksecSost = 7.5E-4 |
Reaction: Ocy_I => Ocy_I + Sost; Ocy_I, Rate Law: ksecSost*Ocy_I |
ksecRANKLbyObpPTH = 2.0E-5 |
Reaction: Ob_p_PTH => Ob_p_PTH + RANKL; Ob_p_PTH, Rate Law: ksecRANKLbyObpPTH*Ob_p_PTH |
States:
Name | Description |
---|---|
Sost |
[Sclerostin] |
CREB Runx2 |
[Cyclic AMP-responsive element-binding protein 1; Runt-related transcription factor 2] |
Bone |
Bone |
Ob p |
[preosteoblast] |
Ob m |
[terminally differentiated osteoblast] |
Wnt I |
[Proto-oncogene Wnt-1] |
PTH |
[Parathyroid hormone] |
MSC |
[mesenchymal stem cell] |
HSC |
[bone marrow hematopoietic cell] |
Bax |
[Apoptosis regulator BAX] |
newbone |
newbone |
Source |
Source |
Tgfb I |
[Transforming growth factor beta-1] |
CREB P |
[Cyclic AMP-responsive element-binding protein 1; phosphoprotein] |
Bcl2 |
[Apoptosis regulator Bcl-2] |
CREB |
[Cyclic AMP-responsive element-binding protein 1] |
X |
X |
Ob p PTH |
[Parathyroid hormone; preosteoblast] |
Ocy I PTH |
[Parathyroid hormone; osteocyte] |
Runx2 |
[Runt-related transcription factor 2] |
Ob m PTH |
[Parathyroid hormone; terminally differentiated osteoblast] |
Ob p Tgfb A |
[Transforming growth factor beta-1; preosteoblast] |
Wnt A |
[Proto-oncogene Wnt-1; TGF-beta 1 isoform 1 cleaved 1] |
LOAD |
LOAD |
Ob pro |
[non-terminally differentiated osteoblast] |
RANKL |
[Tumor necrosis factor ligand superfamily member 11] |
Sink |
Sink |
Bax Bcl2 |
[Apoptosis regulator Bcl-2; Apoptosis regulator BAX] |
MCSF |
[Macrophage colony-stimulating factor 1] |
Observables: none
MODEL1704110001
@ v0.0.1
Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir181_in_muscle)This model is described in th…
DetailsMicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6
Parameters: none
States: none
Observables: none
MODEL1704110004
@ v0.0.1
Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mirs_in_muscle)This model is described in the…
DetailsMicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6
Parameters: none
States: none
Observables: none
MODEL1704110000
@ v0.0.1
Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir1_in_muscle)This model is described in the…
DetailsMicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6
Parameters: none
States: none
Observables: none
MODEL1704110003
@ v0.0.1
Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir143_in_muscle)This model is described in th…
DetailsMicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6
Parameters: none
States: none
Observables: none
MODEL1704110002
@ v0.0.1
Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir378_in_muscle)This model is described in th…
DetailsMicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6
Parameters: none
States: none
Observables: none
MODEL1705170005
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (miR140 in osteoarthritis)This model is described in the article: [Com…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
MODEL1705170004
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (Mir140-IGFBP5 incoherent feed forward)This model is described in the a…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
MODEL1705170001
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (miR140-IL1 coherent feed forward)This model is described in the articl…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
MODEL1705170002
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (miR140-IL1 incoherent feed forward)This model is described in the arti…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
MODEL1705170000
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (miR140-SMAD3 double negative feedback)This model is described in the a…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
MODEL1705170003
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (miR140-SOX9 incoherent feed forward)This model is described in the art…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
MODEL1610100002
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA with Delay)This model is described in th…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
BIOMD0000000864
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA)This model is described in the article:…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters:
Name | Description |
---|---|
kbinTF1miRgene = 0.005 |
Reaction: miR_gene + TF1 => miR_gene_TF1, Rate Law: cell*kbinTF1miRgene*miR_gene*cell*TF1*cell/cell |
krelTF1miRgene = 5.0 |
Reaction: miR_gene_TF1 => miR_gene + TF1, Rate Law: cell*krelTF1miRgene*miR_gene_TF1*cell/cell |
ksynMiR = 5.0 |
Reaction: miR_gene_TF1 => miR_gene_TF1 + miR, Rate Law: cell*ksynMiR*miR_gene_TF1*cell/cell |
ksynTF1mRNA = 10.0 |
Reaction: Signal => Signal + TF1_mRNA, Rate Law: cell*ksynTF1mRNA*Signal*cell/cell |
kdegMiR = 0.008 |
Reaction: miR => Sink, Rate Law: cell*kdegMiR*miR*cell/cell |
ksynTF1 = 0.05 |
Reaction: TF1_mRNA => TF1_mRNA + TF1, Rate Law: cell*ksynTF1*TF1_mRNA*cell/cell |
kdegTF1 = 0.005 |
Reaction: TF1 => Sink, Rate Law: cell*kdegTF1*TF1*cell/cell |
kdegTF1mRNA = 1.0E-4 |
Reaction: TF1_mRNA => Sink, Rate Law: cell*kdegTF1mRNA*TF1_mRNA*cell/cell |
kdegTF1mRNAbyMiR = 0.001 |
Reaction: TF1_mRNA + miR => miR, Rate Law: cell*kdegTF1mRNAbyMiR*TF1_mRNA*cell*miR*cell/cell |
States:
Name | Description |
---|---|
miR gene TF1 |
miR_gene_TF1 |
TF1 mRNA |
TF1_mRNA |
miR |
miR |
Sink |
Sink |
miR gene |
miR_gene |
TF1 |
TF1 |
Signal |
Signal |
Observables: none
BIOMD0000000862
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedback By Micro RNA)This model is described in the article:…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters:
Name | Description |
---|---|
krelTF2miRgene = 0.001 |
Reaction: miR_gene_TF2 => miR_gene + TF2, Rate Law: cell*krelTF2miRgene*miR_gene_TF2*cell/cell |
kdegTF1 = 1.0E-5 |
Reaction: TF1 => Sink, Rate Law: cell*kdegTF1*TF1*cell/cell |
kbinTF1miRgene = 0.002 |
Reaction: miR_gene + TF1 => miR_gene_TF1, Rate Law: cell*kbinTF1miRgene*miR_gene*cell*TF1*cell/cell |
kdegTF1mRNA = 1.0E-4 |
Reaction: TF1_mRNA => Sink, Rate Law: cell*kdegTF1mRNA*TF1_mRNA*cell/cell |
ksynMiR = 0.2 |
Reaction: miR_gene_TF2 => miR_gene_TF2 + miR, Rate Law: cell*ksynMiR*miR_gene_TF2*cell/cell |
krelTF1miRgene = 0.001 |
Reaction: miR_gene_TF1 => miR_gene + TF1, Rate Law: cell*krelTF1miRgene*miR_gene_TF1*cell/cell |
kbinTF2miRgene = 1.0E-4 |
Reaction: miR_gene + TF2 => miR_gene_TF2, Rate Law: cell*kbinTF2miRgene*miR_gene*cell*TF2*cell/cell |
kdegMiR = 4.0E-4 |
Reaction: miR => Sink, Rate Law: cell*kdegMiR*miR*cell/cell |
kdegTF1mRNAbyMiR = 1.0E-6 |
Reaction: TF1_mRNA + miR => miR, Rate Law: cell*kdegTF1mRNAbyMiR*TF1_mRNA*cell*miR*cell/cell |
ksynTF1mRNA = 0.01 |
Reaction: Signal => Signal + TF1_mRNA, Rate Law: cell*ksynTF1mRNA*Signal*cell/cell |
ksynTF1 = 3.0E-4 |
Reaction: TF1_mRNA => TF1_mRNA + TF1, Rate Law: cell*ksynTF1*TF1_mRNA*cell/cell |
States:
Name | Description |
---|---|
miR gene TF1 |
miR_gene_TF1 |
miR gene TF2 |
miR_gene_TF2 |
TF2 |
TF2 |
TF1 mRNA |
TF1_mRNA |
miR |
miR |
miR gene |
miR_gene |
Sink |
Sink |
Signal |
Signal |
TF1 |
TF1 |
Observables: none
MODEL1610100003
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedforward Coherent By MicroRNA)This model is described in t…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters: none
States: none
Observables: none
BIOMD0000000860
@ v0.0.1
Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedforward Incoherent By MicroRNA)This model is described in…
DetailsThe aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952
Parameters:
Name | Description |
---|---|
kdegTF1targetmRNAbyMiR = 5.0E-5 1/ (mol *s) |
Reaction: TF1target_mRNA + miR => Sink + miR, Rate Law: cell*kdegTF1targetmRNAbyMiR*TF1target_mRNA*cell*miR*cell/cell |
kdegMiR = 4.0E-4 1/s |
Reaction: miR => Sink, Rate Law: cell*kdegMiR*miR*cell/cell |
ksynTF1targetmRNA = 0.004 1/s |
Reaction: TF1 => TF1 + TF1target_mRNA, Rate Law: cell*ksynTF1targetmRNA*TF1*cell/cell |
ksynMiR = 2.0E-4 1/s |
Reaction: TF1 => TF1 + miR, Rate Law: cell*ksynMiR*TF1*cell/cell |
kdegTF1targetmRNA = 0.001 1/s |
Reaction: TF1target_mRNA => Sink, Rate Law: cell*kdegTF1targetmRNA*TF1target_mRNA*cell/cell |
States:
Name | Description |
---|---|
miR |
[C25966] |
Sink |
Sink |
TF1 |
[1,4-beta-D-Mannooligosaccharide] |
TF1target mRNA |
TF1target_mRNA |
Observables: none
MODEL1507180044
@ v0.0.1
Puchalka2008 - Genome-scale metabolic network of Pseudomonas putida (iJP815)This model is described in the article: [Ge…
DetailsA cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential. link: http://identifiers.org/pubmed/18974823
Parameters: none
States: none
Observables: none
MODEL1409240001
@ v0.0.1
Puri2010 - Mathematical Modeling for the Pathogenesis of Alzheimer's DiseasePuri2010 - Mathematical Modeling for the Pat…
DetailsDespite extensive research, the pathogenesis of neurodegenerative Alzheimer's disease (AD) still eludes our comprehension. This is largely due to complex and dynamic cross-talks that occur among multiple cell types throughout the aging process. We present a mathematical model that helps define critical components of AD pathogenesis based on differential rate equations that represent the known cross-talks involving microglia, astroglia, neurons, and amyloid-β (Aβ). We demonstrate that the inflammatory activation of microglia serves as a key node for progressive neurodegeneration. Our analysis reveals that targeting microglia may hold potential promise in the prevention and treatment of AD. link: http://identifiers.org/pubmed/21179474
Parameters: none
States: none
Observables: none
MODEL7980735163
@ v0.0.1
This a model from the article: Ionic current model of a hypoglossal motoneuron. Purvis LK, Butera RJ. J Neurophysiol…
DetailsWe have developed a single-compartment, electrophysiological, hypoglossal motoneuron (HM) model based primarily on experimental data from neonatal rat HMs. The model is able to reproduce the fine features of the HM action potential: the fast afterhyperpolarization, the afterdepolarization, and the medium-duration afterhyperpolarization (mAHP). The model also reproduces the repetitive firing properties seen in neonatal HMs and replicates the neuron's response to pharmacological experiments. The model was used to study the role of specific ionic currents in HM firing and how variations in the densities of these currents may account for age-dependent changes in excitability seen in HMs. By varying the density of a fast inactivating calcium current, the model alternates between accelerating and adapting firing patterns. Modeling the age-dependent increase in H current density accounts for the decrease in mAHP duration observed experimentally, but does not fully account for the decrease in input resistance. An increase in the density of the voltage-dependent potassium currents and the H current is required to account for the decrease in input resistance. These changes also account for the age-dependent decrease in action potential duration. link: http://identifiers.org/pubmed/15653786
Parameters: none
States: none
Observables: none
MODEL1805150001
@ v0.0.1
Mathematical model
DetailsTo quantify how various molecular mechanisms are integrated to maintain platelet homeostasis and allow responsiveness to adenosine diphosphate (ADP), we developed a computational model of the human platelet. Existing kinetic information for 77 reactions, 132 fixed kinetic rate constants, and 70 species was combined with electrochemical calculations, measurements of platelet ultrastructure, novel experimental results, and published single-cell data. The model accurately predicted: (1) steady-state resting concentrations for intracellular calcium, inositol 1,4,5-trisphosphate, diacylglycerol, phosphatidic acid, phosphatidylinositol, phosphatidylinositol phosphate, and phosphatidylinositol 4,5-bisphosphate; (2) transient increases in intracellular calcium, inositol 1,4,5-trisphosphate, and G(q)-GTP in response to ADP; and (3) the volume of the platelet dense tubular system. A more stringent test of the model involved stochastic simulation of individual platelets, which display an asynchronous calcium spiking behavior in response to ADP. Simulations accurately reproduced the broad frequency distribution of measured spiking events and demonstrated that asynchronous spiking was a consequence of stochastic fluctuations resulting from the small volume of the platelet. The model also provided insights into possible mechanisms of negative-feedback signaling, the relative potency of platelet agonists, and cell-to-cell variation across platelet populations. This integrative approach to platelet biology offers a novel and complementary strategy to traditional reductionist methods. link: http://identifiers.org/pubmed/18596227
Parameters: none
States: none
Observables: none
BIOMD0000000544
@ v0.0.1
Qi2013 - IL-6 and IFN crosstalk modelThis model [[BIOMD0000000544]](http://www.ebi.ac.uk/biomodels-main/BIOMD0000000544…
DetailsBACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways. link: http://identifiers.org/pubmed/23384097
Parameters:
Name | Description |
---|---|
parameter_94 = 0.064; parameter_93 = 0.03 |
Reaction: species_35 + species_36 => species_46; species_35, species_36, species_46, Rate Law: compartment_1*(parameter_93*species_35*species_36-parameter_94*species_46) |
parameter_48 = 0.005 |
Reaction: species_25 => species_24 + species_29; species_25, Rate Law: c3*parameter_48*species_25 |
parameter_153 = 0.2; parameter_152 = 0.001 |
Reaction: species_95 + species_24 => species_94; species_24, species_94, species_95, Rate Law: c2*(parameter_152*species_24*species_95-parameter_153*species_94) |
parameter_221 = 0.001; parameter_222 = 7.99942179 |
Reaction: species_82 + species_11 => s118; species_82, species_11, s118, Rate Law: compartment_1*(parameter_221*species_82*species_11-parameter_222*s118) |
parameter_123 = 0.3 |
Reaction: species_66 => species_64 + species_59; species_66, Rate Law: compartment_1*parameter_123*species_66 |
parameter_145 = 0.003 |
Reaction: species_88 => species_81 + species_108; species_88, Rate Law: compartment_1*parameter_145*species_88 |
parameter_120 = 0.27 |
Reaction: species_65 => species_64 + species_61; species_65, Rate Law: compartment_1*parameter_120*species_65 |
parameter_109 = 2.5E-4; parameter_110 = 0.5 |
Reaction: species_53 + species_57 => species_58; species_53, species_57, species_58, Rate Law: compartment_1*(parameter_109*species_53*species_57-parameter_110*species_58) |
parameter_51 = 0.05 |
Reaction: species_28 => species_11; species_28, Rate Law: parameter_51*species_28 |
parameter_166 = 0.003 |
Reaction: species_101 => species_91 + species_20; species_101, Rate Law: compartment_1*parameter_166*species_101 |
parameter_155 = 0.005 |
Reaction: species_94 => species_96 + species_24; species_94, Rate Law: c2*parameter_155*species_94 |
parameter_150 = 0.2; parameter_149 = 2.0E-7 |
Reaction: species_84 + species_85 => species_91; species_84, species_85, species_91, Rate Law: compartment_1*(parameter_149*species_84*species_85-parameter_150*species_91) |
parameter_161 = 0.1; parameter_160 = 0.02 |
Reaction: species_99 + species_82 => species_100; species_99, species_82, species_100, Rate Law: compartment_1*(parameter_160*species_99*species_82-parameter_161*species_100) |
parameter_129 = 0.1; parameter_130 = 0.05 |
Reaction: species_5 + species_107 => species_78; species_5, species_107, species_78, Rate Law: compartment_1*(parameter_129*species_5*species_107-parameter_130*species_78) |
parameter_241 = 0.2; parameter_240 = 0.001 |
Reaction: s122 + species_24 => s126; s122, species_24, s126, Rate Law: parameter_240*s122*species_24-parameter_241*s126 |
parameter_238 = 0.001; parameter_239 = 0.2 |
Reaction: species_20 + s120 => s135; species_20, s120, s135, Rate Law: compartment_1*(parameter_238*species_20*s120-parameter_239*s135) |
parameter_61 = 6.0; parameter_62 = 0.06 |
Reaction: species_16 => species_33; species_16, species_33, Rate Law: compartment_1*(parameter_61*species_16-parameter_62*species_33) |
parameter_126 = 0.0388 |
Reaction: species_75 => species_74; species_75, Rate Law: compartment_1*parameter_126*species_75 |
parameter_175 = 0.8; parameter_174 = 0.008 |
Reaction: species_84 + species_100 => species_104; species_84, species_100, species_104, Rate Law: compartment_1*(parameter_174*species_84*species_100-parameter_175*species_104) |
parameter_100 = 0.011; parameter_101 = 0.001833 |
Reaction: species_44 + species_51 => species_52; species_44, species_51, species_52, Rate Law: compartment_1*(parameter_100*species_44*species_51-parameter_101*species_52) |
parameter_177 = 0.2; parameter_176 = 0.001 |
Reaction: species_108 + species_104 => species_105; species_108, species_104, species_105, Rate Law: compartment_1*(parameter_176*species_108*species_104-parameter_177*species_105) |
parameter_88 = 9.0E-4; parameter_87 = 0.3 |
Reaction: species_33 => species_9 + species_48; species_33, species_9, species_48, Rate Law: compartment_1*(parameter_87*species_33-parameter_88*species_9*species_48) |
parameter_40 = 0.005 |
Reaction: species_14 => species_23; species_14, Rate Law: parameter_40*species_14 |
parameter_131 = 0.02; parameter_132 = 0.02 |
Reaction: species_79 + species_78 => species_80; species_79, species_78, species_80, Rate Law: parameter_131*species_79*species_78-parameter_132*species_80 |
parameter_99 = 1.0 |
Reaction: species_50 => species_41 + species_49; species_50, Rate Law: compartment_1*parameter_99*species_50 |
parameter_243 = 0.0015 |
Reaction: s135 => species_85 + species_11 + species_20; s135, Rate Law: compartment_1*parameter_243*s135 |
parameter_244 = 0.0025 |
Reaction: s126 => species_26 + species_24 + species_96; s126, Rate Law: parameter_244*s126 |
parameter_96 = 0.0429; parameter_95 = 0.03 |
Reaction: species_33 + species_47 => species_37; species_33, species_47, species_37, Rate Law: compartment_1*(parameter_95*species_33*species_47-parameter_96*species_37) |
parameter_85 = 1.7; parameter_86 = 340.0 |
Reaction: species_48 => species_108; species_48, Rate Law: compartment_1*parameter_85*species_48/(parameter_86+species_48) |
parameter_53 = 400.0; parameter_52 = 0.01 |
Reaction: => species_30; species_23, species_23, Rate Law: c3*parameter_52*species_23/(parameter_53+species_23) |
parameter_224 = 5.09534E-4; parameter_225 = 4.982769238 |
Reaction: species_12 + species_82 => s119; species_12, species_82, s119, Rate Law: compartment_1*(parameter_224*species_12*species_82-parameter_225*s119) |
parameter_178 = 0.003 |
Reaction: species_105 => species_99 + species_81 + species_84 + species_108; species_105, Rate Law: compartment_1*parameter_178*species_105 |
parameter_97 = 0.0717; parameter_98 = 0.2 |
Reaction: species_49 + species_44 => species_50; species_49, species_44, species_50, Rate Law: compartment_1*(parameter_97*species_49*species_44-parameter_98*species_50) |
parameter_63 = 0.01; parameter_64 = 0.55 |
Reaction: species_33 + species_32 => species_34; species_33, species_32, species_34, Rate Law: compartment_1*(parameter_63*species_33*species_32-parameter_64*species_34) |
parameter_159 = 0.01 |
Reaction: => species_99; species_98, species_98, Rate Law: compartment_1*parameter_159*species_98 |
parameter_128 = 9.0E-4; parameter_127 = 0.9854 |
Reaction: species_75 => species_76; species_75, species_76, Rate Law: compartment_1*(parameter_127*species_75^2-parameter_128*species_76) |
parameter_83 = 0.0015; parameter_84 = 0.0045 |
Reaction: species_47 => species_32 + species_35; species_47, species_32, species_35, Rate Law: compartment_1*(parameter_83*species_47-parameter_84*species_32*species_35) |
parameter_236 = 0.1; parameter_235 = 0.02 |
Reaction: species_26 + species_95 => s122; species_26, species_95, s122, Rate Law: parameter_235*species_26*species_95-parameter_236*s122 |
parameter_82 = 0.021; parameter_81 = 0.3 |
Reaction: species_46 => species_47 + species_48; species_46, species_47, species_48, Rate Law: compartment_1*(parameter_81*species_46-parameter_82*species_47*species_48) |
parameter_231 = 0.001; parameter_232 = 400.0 |
Reaction: => species_30; species_92, species_92, Rate Law: c3*parameter_231*species_92/(parameter_232+species_92) |
parameter_139 = 0.005; parameter_140 = 0.5 |
Reaction: species_82 + species_85 => species_86; species_82, species_85, species_86, Rate Law: compartment_1*(parameter_139*species_82*species_85-parameter_140*species_86) |
parameter_89 = 0.01; parameter_90 = 0.55 |
Reaction: species_32 + species_48 => species_36; species_32, species_48, species_36, Rate Law: compartment_1*(parameter_89*species_32*species_48-parameter_90*species_36) |
parameter_148 = 0.003 |
Reaction: species_90 => species_84 + species_20; species_90, Rate Law: compartment_1*parameter_148*species_90 |
parameter_158 = 0.001 |
Reaction: species_97 => species_98; species_97, Rate Law: parameter_158*species_97 |
parameter_49 = 0.2; parameter_50 = 2.0E-7 |
Reaction: species_29 => species_26 + species_28; species_29, species_26, species_28, Rate Law: c3*(parameter_49*species_29-parameter_50*species_26*species_28) |
parameter_122 = 0.5; parameter_121 = 0.005 |
Reaction: species_61 + species_64 => species_66; species_61, species_64, species_66, Rate Law: compartment_1*(parameter_121*species_61*species_64-parameter_122*species_66) |
parameter_138 = 0.4 |
Reaction: species_83 => species_82 + species_85; species_83, Rate Law: compartment_1*parameter_138*species_83 |
parameter_44 = 0.2; parameter_43 = 0.001 |
Reaction: species_24 + species_26 => species_27; species_24, species_26, species_27, Rate Law: c3*(parameter_43*species_24*species_26-parameter_44*species_27) |
parameter_146 = 0.001; parameter_147 = 0.2 |
Reaction: species_85 + species_20 => species_90; species_85, species_20, species_90, Rate Law: compartment_1*(parameter_146*species_85*species_20-parameter_147*species_90) |
parameter_35 = 0.001; parameter_36 = 0.2 |
Reaction: species_14 + species_20 => species_22; species_14, species_20, species_22, Rate Law: compartment_1*(parameter_35*species_14*species_20-parameter_36*species_22) |
parameter_242 = 0.0015 |
Reaction: s135 => species_20 + species_12 + species_84; s135, Rate Law: compartment_1*parameter_242*s135 |
parameter_133 = 0.04; parameter_134 = 0.2 |
Reaction: species_80 => species_81; species_80, species_81, Rate Law: compartment_1*(parameter_133*species_80^2-parameter_134*species_81) |
parameter_143 = 0.001; parameter_144 = 0.2 |
Reaction: species_82 + species_108 => species_88; species_82, species_108, species_88, Rate Law: compartment_1*(parameter_143*species_82*species_108-parameter_144*species_88) |
parameter_58 = 5.0E-4 |
Reaction: species_31 => ; species_31, Rate Law: compartment_1*parameter_58*species_31 |
parameter_162 = 5.0E-4 |
Reaction: species_98 => ; species_98, Rate Law: compartment_1*parameter_162*species_98 |
parameter_169 = 0.001; parameter_170 = 0.2 |
Reaction: species_92 + species_24 => species_102; species_102, species_24, species_92, Rate Law: c2*(parameter_169*species_24*species_92-parameter_170*species_102) |
parameter_245 = 0.0025 |
Reaction: s126 => species_95 + species_28 + species_24; s126, Rate Law: parameter_245*s126 |
parameter_223 = 3.999994653 |
Reaction: s118 => species_12 + species_82; s118, Rate Law: compartment_1*parameter_223*s118 |
parameter_135 = 0.005 |
Reaction: species_81 => species_82; species_81, Rate Law: compartment_1*parameter_135*species_81 |
parameter_137 = 0.8; parameter_136 = 0.008 |
Reaction: species_82 + species_84 => species_83; species_82, species_84, species_83, Rate Law: compartment_1*(parameter_136*species_82*species_84-parameter_137*species_83) |
parameter_165 = 0.2; parameter_164 = 0.001 |
Reaction: species_87 + species_20 => species_101; species_87, species_20, species_101, Rate Law: compartment_1*(parameter_164*species_87*species_20-parameter_165*species_101) |
parameter_54 = 0.001 |
Reaction: species_30 => species_31; species_30, Rate Law: parameter_54*species_30 |
parameter_56 = 5.0; parameter_57 = 0.1 |
Reaction: species_9 + species_19 => species_15; species_9, species_19, species_15, Rate Law: compartment_1*(parameter_56*species_9*species_19-parameter_57*species_15) |
parameter_163 = 5.0E-4 |
Reaction: species_99 => ; species_99, Rate Law: compartment_1*parameter_163*species_99 |
parameter_32 = 0.001; parameter_33 = 0.2 |
Reaction: species_12 + species_20 => species_21; species_12, species_20, species_21, Rate Law: compartment_1*(parameter_32*species_12*species_20-parameter_33*species_21) |
parameter_125 = 20000.0; parameter_124 = 0.2335 |
Reaction: species_74 => species_75; species_63, species_63, species_74, Rate Law: compartment_1*parameter_124*species_63*species_74/(species_74+parameter_125) |
parameter_119 = 0.6; parameter_118 = 0.014 |
Reaction: species_63 + species_64 => species_65; species_63, species_64, species_65, Rate Law: compartment_1*(parameter_118*species_63*species_64-parameter_119*species_65) |
parameter_111 = 0.058 |
Reaction: species_58 => species_57 + species_51; species_58, Rate Law: compartment_1*parameter_111*species_58 |
parameter_179 = 5.0E-4 |
Reaction: species_105 => species_99 + species_106; species_105, Rate Law: compartment_1*parameter_179*species_105 |
States:
Name | Description |
---|---|
species 100 |
[Tyrosine-protein kinase JAK2; Interferon gamma; Interferon gamma receptor 1; Suppressor of cytokine signaling 1; SBO:0000286; phosphorylated] |
species 98 |
[Suppressor of cytokine signaling 1; SBO:0000278] |
species 20 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit] |
species 91 |
[Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 1-alpha/beta; phosphorylated] |
species 47 |
[Growth factor receptor-bound protein 2; Son of sevenless homolog 1] |
species 66 |
[Mitogen-activated protein kinase 1; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated] |
species 21 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 3; phosphorylated] |
species 57 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
species 15 |
[Interleukin-6 receptor subunit alpha; Interleukin-6; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; Suppressor of cytokine signaling 3; SBO:0000286] |
species 83 |
[Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000286; phosphorylated] |
species 33 |
[Interleukin-6; Interleukin-6 receptor subunit alpha; Interleukin-6 receptor subunit beta; Tyrosine-protein kinase JAK1; Tyrosine-protein phosphatase non-receptor type 11; SBO:0000286; phosphorylated] |
species 64 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
species 24 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
species 78 |
[Interferon gamma receptor 1; Tyrosine-protein kinase JAK2] |
species 58 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated] |
species 48 |
[Tyrosine-protein phosphatase non-receptor type 11; phosphorylated] |
species 76 |
[CCAAT/enhancer-binding protein beta; active] |
s126 |
[Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated] |
species 99 |
[Suppressor of cytokine signaling 1] |
species 101 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000286; phosphorylated] |
species 65 |
[Mitogen-activated protein kinase 1; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated] |
species 50 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; RAF proto-oncogene serine/threonine-protein kinase] |
species 27 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Signal transducer and activator of transcription 3; phosphorylated] |
species 63 |
[Mitogen-activated protein kinase 1] |
s135 |
[Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 1-alpha/beta; Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; phosphorylated] |
species 31 |
[Suppressor of cytokine signaling 3; SBO:0000278] |
species 51 |
[Dual specificity mitogen-activated protein kinase kinase 1] |
species 104 |
[Interferon gamma receptor 1; Interferon gamma; Tyrosine-protein kinase JAK2; Signal transducer and activator of transcription 1-alpha/beta; Suppressor of cytokine signaling 1; SBO:0000286; phosphorylated] |
species 28 |
[Signal transducer and activator of transcription 3] |
species 75 |
[CCAAT/enhancer-binding protein beta; active] |
species 84 |
[Signal transducer and activator of transcription 1-alpha/beta] |
species 29 |
[Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 3; phosphorylated] |
species 32 |
[Growth factor receptor-bound protein 2] |
species 30 |
[Suppressor of cytokine signaling 3; SBO:0000278] |
species 49 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit] |
species 74 |
[CCAAT/enhancer-binding protein beta] |
species 81 |
[Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; SBO:0000286] |
species 14 |
[SBO:0000608; phosphorylated; Signal transducer and activator of transcription 3] |
species 82 |
[Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; SBO:0000286; phosphorylated] |
species 80 |
[Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; Interferon gamma] |
species 46 |
[Tyrosine-protein phosphatase non-receptor type 11; Son of sevenless homolog 1; Growth factor receptor-bound protein 2] |
species 26 |
[Signal transducer and activator of transcription 3; phosphorylated] |
species 90 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 1-alpha/beta; phosphorylated] |
Observables: none
BIOMD0000000543
@ v0.0.1
Qi2013 - IL-6 and IFN crosstalk model (non-competitive)This model [[BIOMD0000000543]](http://www.ebi.ac.uk/biomodels-ma…
DetailsBACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways. link: http://identifiers.org/pubmed/23384097
Parameters:
Name | Description |
---|---|
parameter_68 = 1.3; parameter_67 = 0.015 |
Reaction: species_38 + species_37 => species_39; species_38, species_37, species_39, Rate Law: compartment_1*(parameter_67*species_38*species_37-parameter_68*species_39) |
parameter_48 = 0.005 |
Reaction: species_25 => species_24 + species_29; species_25, Rate Law: c3*parameter_48*species_25 |
parameter_153 = 0.2; parameter_152 = 0.001 |
Reaction: species_95 + species_24 => species_94; species_24, species_94, species_95, Rate Law: c2*(parameter_152*species_24*species_95-parameter_153*species_94) |
parameter_233 = 0.02; parameter_234 = 0.1 |
Reaction: species_12 + species_85 => s120; species_12, species_85, s120, Rate Law: compartment_1*(parameter_233*species_12*species_85-parameter_234*s120) |
parameter_123 = 0.3 |
Reaction: species_66 => species_64 + species_59; species_66, Rate Law: compartment_1*parameter_123*species_66 |
parameter_120 = 0.27 |
Reaction: species_65 => species_64 + species_61; species_65, Rate Law: compartment_1*parameter_120*species_65 |
parameter_166 = 0.003 |
Reaction: species_101 => species_91 + species_20; species_101, Rate Law: compartment_1*parameter_166*species_101 |
parameter_69 = 0.5; parameter_70 = 1.0E-4 |
Reaction: species_39 => species_40 + species_37; species_39, species_40, species_37, Rate Law: compartment_1*(parameter_69*species_39-parameter_70*species_40*species_37) |
parameter_51 = 0.05 |
Reaction: species_28 => species_11; species_28, Rate Law: parameter_51*species_28 |
parameter_155 = 0.005 |
Reaction: species_94 => species_96 + species_24; species_94, Rate Law: c2*parameter_155*species_94 |
parameter_150 = 0.2; parameter_149 = 2.0E-7 |
Reaction: species_84 + species_85 => species_91; species_84, species_85, species_91, Rate Law: compartment_1*(parameter_149*species_84*species_85-parameter_150*species_91) |
parameter_65 = 0.01; parameter_66 = 0.0214 |
Reaction: species_35 + species_34 => species_37; species_35, species_34, species_37, Rate Law: compartment_1*(parameter_65*species_35*species_34-parameter_66*species_37) |
parameter_241 = 0.2; parameter_240 = 0.001 |
Reaction: s122 + species_24 => s126; s122, species_24, s126, Rate Law: parameter_240*s122*species_24-parameter_241*s126 |
parameter_238 = 0.001; parameter_239 = 0.2 |
Reaction: species_20 + s120 => s135; species_20, s120, s135, Rate Law: compartment_1*(parameter_238*species_20*s120-parameter_239*s135) |
parameter_126 = 0.0388 |
Reaction: species_75 => species_74; species_75, Rate Law: compartment_1*parameter_126*species_75 |
parameter_22 = 2.0; parameter_21 = 0.002 |
Reaction: species_10 + species_84 => species_17; species_10, species_84, species_17, Rate Law: parameter_21*species_10*species_84-parameter_22*species_17 |
parameter_79 = 0.47; parameter_80 = 2.45E-4 |
Reaction: species_37 => species_46 + species_9; species_37, species_46, species_9, Rate Law: compartment_1*(parameter_79*species_37-parameter_80*species_46*species_9) |
parameter_1 = 0.1; parameter_2 = 0.05 |
Reaction: species_2 + species_1 => species_3; species_2, species_1, species_3, Rate Law: parameter_1*species_2*species_1-parameter_2*species_3 |
parameter_23 = 0.008; parameter_24 = 0.8 |
Reaction: species_67 + species_11 => species_17; species_67, species_11, species_17, Rate Law: parameter_23*species_67*species_11-parameter_24*species_17 |
parameter_131 = 0.02; parameter_132 = 0.02 |
Reaction: species_79 + species_78 => species_80; species_79, species_78, species_80, Rate Law: parameter_131*species_79*species_78-parameter_132*species_80 |
parameter_99 = 1.0 |
Reaction: species_50 => species_41 + species_49; species_50, Rate Law: compartment_1*parameter_99*species_50 |
parameter_243 = 0.0015 |
Reaction: s135 => species_85 + species_11 + species_20; s135, Rate Law: compartment_1*parameter_243*s135 |
parameter_72 = 0.0053; parameter_71 = 0.001 |
Reaction: species_40 + species_41 => species_42; species_40, species_41, species_42, Rate Law: compartment_1*(parameter_71*species_40*species_41-parameter_72*species_42) |
parameter_8 = 0.8; parameter_7 = 0.008 |
Reaction: s118 + species_84 => species_68; s118, species_84, species_68, Rate Law: compartment_1*(parameter_7*s118*species_84-parameter_8*species_68) |
parameter_244 = 0.0025 |
Reaction: s126 => species_26 + species_24 + species_96; s126, Rate Law: parameter_244*s126 |
parameter_14 = 0.008; parameter_15 = 0.8 |
Reaction: species_9 + species_11 => species_10; species_9, species_11, species_10, Rate Law: compartment_1*(parameter_14*species_9*species_11-parameter_15*species_10) |
parameter_96 = 0.0429; parameter_95 = 0.03 |
Reaction: species_33 + species_47 => species_37; species_33, species_47, species_37, Rate Law: compartment_1*(parameter_95*species_33*species_47-parameter_96*species_37) |
parameter_74 = 7.0E-4; parameter_73 = 1.0 |
Reaction: species_42 => species_43 + species_44; species_42, species_43, species_44, Rate Law: compartment_1*(parameter_73*species_42-parameter_74*species_43*species_44) |
parameter_229 = 0.005; parameter_230 = 0.5 |
Reaction: species_85 + species_9 => s139; species_85, species_9, s139, Rate Law: compartment_1*(parameter_229*species_85*species_9-parameter_230*s139) |
parameter_53 = 400.0; parameter_52 = 0.01 |
Reaction: => species_30; species_23, species_23, Rate Law: c3*parameter_52*species_23/(parameter_53+species_23) |
parameter_168 = 0.5; parameter_167 = 0.005 |
Reaction: species_95 => species_92; species_95, species_92, Rate Law: c2*(parameter_167*species_95^2-parameter_168*species_92) |
parameter_221 = 0.002; parameter_222 = 2.0 |
Reaction: species_82 + species_11 => s118; species_82, species_11, s118, Rate Law: compartment_1*(parameter_221*species_82*species_11-parameter_222*s118) |
parameter_159 = 0.01 |
Reaction: => species_99; species_98, species_98, Rate Law: compartment_1*parameter_159*species_98 |
parameter_37 = 0.003 |
Reaction: species_22 => species_18 + species_20; species_22, Rate Law: compartment_1*parameter_37*species_22 |
parameter_25 = 0.2 |
Reaction: species_17 => species_10 + species_85; species_17, Rate Law: parameter_25*species_17 |
parameter_223 = 0.2 |
Reaction: s118 => species_12 + species_82; s118, Rate Law: compartment_1*parameter_223*s118 |
parameter_83 = 0.0015; parameter_84 = 0.0045 |
Reaction: species_47 => species_32 + species_35; species_47, species_32, species_35, Rate Law: compartment_1*(parameter_83*species_47-parameter_84*species_32*species_35) |
parameter_236 = 0.1; parameter_235 = 0.02 |
Reaction: species_26 + species_95 => s122; species_26, species_95, s122, Rate Law: parameter_235*species_26*species_95-parameter_236*s122 |
parameter_10 = 2.0; parameter_9 = 0.002 |
Reaction: species_83 + species_11 => species_68; species_83, species_11, species_68, Rate Law: compartment_1*(parameter_9*species_83*species_11-parameter_10*species_68) |
parameter_38 = 2.0E-7; parameter_39 = 0.2 |
Reaction: species_11 + species_12 => species_18; species_11, species_12, species_18, Rate Law: compartment_1*(parameter_38*species_11*species_12-parameter_39*species_18) |
parameter_237 = 0.005 |
Reaction: s120 => s122; s120, Rate Law: compartment_1*parameter_237*s120 |
parameter_139 = 0.005; parameter_140 = 0.5 |
Reaction: species_82 + species_85 => species_86; species_82, species_85, species_86, Rate Law: compartment_1*(parameter_139*species_82*species_85-parameter_140*species_86) |
parameter_89 = 0.01; parameter_90 = 0.55 |
Reaction: species_32 + species_48 => species_36; species_32, species_48, species_36, Rate Law: compartment_1*(parameter_89*species_32*species_48-parameter_90*species_36) |
parameter_26 = 0.4 |
Reaction: species_17 => species_67 + species_12; species_17, Rate Law: parameter_26*species_17 |
parameter_158 = 0.001 |
Reaction: species_97 => species_98; species_97, Rate Law: parameter_158*species_97 |
parameter_49 = 0.2; parameter_50 = 2.0E-7 |
Reaction: species_29 => species_26 + species_28; species_29, species_26, species_28, Rate Law: c3*(parameter_49*species_29-parameter_50*species_26*species_28) |
parameter_19 = 0.4 |
Reaction: species_68 => s118 + species_85; species_68, Rate Law: compartment_1*parameter_19*species_68 |
parameter_122 = 0.5; parameter_121 = 0.005 |
Reaction: species_61 + species_64 => species_66; species_61, species_64, species_66, Rate Law: compartment_1*(parameter_121*species_61*species_64-parameter_122*species_66) |
parameter_138 = 0.4 |
Reaction: species_83 => species_82 + species_85; species_83, Rate Law: compartment_1*parameter_138*species_83 |
parameter_44 = 0.2; parameter_43 = 0.001 |
Reaction: species_24 + species_26 => species_27; species_24, species_26, species_27, Rate Law: c3*(parameter_43*species_24*species_26-parameter_44*species_27) |
parameter_151 = 0.005 |
Reaction: species_87 => species_92; species_87, Rate Law: parameter_151*species_87 |
parameter_35 = 0.001; parameter_36 = 0.2 |
Reaction: species_14 + species_20 => species_22; species_14, species_20, species_22, Rate Law: compartment_1*(parameter_35*species_14*species_20-parameter_36*species_22) |
parameter_20 = 0.2 |
Reaction: species_68 => species_83 + species_12; species_68, Rate Law: compartment_1*parameter_20*species_68 |
parameter_34 = 0.003 |
Reaction: species_21 => species_11 + species_20; species_21, Rate Law: compartment_1*parameter_34*species_21 |
parameter_78 = 2.2E-4; parameter_77 = 0.023 |
Reaction: species_45 => species_37 + species_38; species_45, species_37, species_38, Rate Law: compartment_1*(parameter_77*species_45-parameter_78*species_37*species_38) |
parameter_242 = 0.0015 |
Reaction: s135 => species_20 + species_12 + species_84; s135, Rate Law: compartment_1*parameter_242*s135 |
parameter_224 = 0.005; parameter_225 = 0.5 |
Reaction: species_12 + species_82 => s119; species_12, species_82, s119, Rate Law: compartment_1*(parameter_224*species_12*species_82-parameter_225*s119) |
parameter_162 = 5.0E-4 |
Reaction: species_98 => ; species_98, Rate Law: compartment_1*parameter_162*species_98 |
parameter_245 = 0.0025 |
Reaction: s126 => species_95 + species_28 + species_24; s126, Rate Law: parameter_245*s126 |
parameter_169 = 0.001; parameter_170 = 0.2 |
Reaction: species_92 + species_24 => species_102; species_102, species_24, species_92, Rate Law: c2*(parameter_169*species_24*species_92-parameter_170*species_102) |
parameter_76 = 0.4; parameter_75 = 0.0079 |
Reaction: species_37 + species_43 => species_45; species_37, species_43, species_45, Rate Law: compartment_1*(parameter_75*species_37*species_43-parameter_76*species_45) |
parameter_165 = 0.2; parameter_164 = 0.001 |
Reaction: species_87 + species_20 => species_101; species_87, species_20, species_101, Rate Law: compartment_1*(parameter_164*species_87*species_20-parameter_165*species_101) |
parameter_142 = 0.1; parameter_141 = 0.02 |
Reaction: species_85 => species_87; species_85, species_87, Rate Law: compartment_1*(parameter_141*species_85^2-parameter_142*species_87) |
parameter_56 = 5.0; parameter_57 = 0.1 |
Reaction: species_9 + species_19 => species_15; species_9, species_19, species_15, Rate Law: compartment_1*(parameter_56*species_9*species_19-parameter_57*species_15) |
parameter_45 = 0.005 |
Reaction: species_27 => species_28 + species_24; species_27, Rate Law: c3*parameter_45*species_27 |
parameter_228 = 0.2 |
Reaction: s138 => species_9 + species_85; s138, Rate Law: compartment_1*parameter_228*s138 |
parameter_125 = 20000.0; parameter_124 = 0.2335 |
Reaction: species_74 => species_75; species_63, species_63, species_74, Rate Law: compartment_1*parameter_124*species_63*species_74/(species_74+parameter_125) |
parameter_94 = 0.064; parameter_93 = 0.03 |
Reaction: species_35 + species_36 => species_46; species_35, species_36, species_46, Rate Law: compartment_1*(parameter_93*species_35*species_36-parameter_94*species_46) |
States:
Name | Description |
---|---|
species 67 |
[Interleukin-6; Interleukin-6 receptor subunit alpha; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; SBO:0000286; Signal transducer and activator of transcription 1-alpha/beta] |
species 27 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Signal transducer and activator of transcription 3; phosphorylated] |
species 36 |
[Growth factor receptor-bound protein 2; Tyrosine-protein phosphatase non-receptor type 11] |
species 98 |
[Suppressor of cytokine signaling 1; SBO:0000278] |
species 1 |
[Interleukin-6] |
species 20 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit] |
species 28 |
[Signal transducer and activator of transcription 3] |
s120 |
[Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000607; phosphorylated] |
s122 |
[Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000607; phosphorylated] |
species 75 |
[CCAAT/enhancer-binding protein beta; active] |
species 91 |
[Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 1-alpha/beta; phosphorylated] |
species 79 |
[Interferon gamma] |
species 92 |
[SBO:0000608; phosphorylated; Signal transducer and activator of transcription 1-alpha/beta] |
species 39 |
[Interleukin-6 receptor subunit alpha; Interleukin-6; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; SBO:0000286; Son of sevenless homolog 1; Tyrosine-protein phosphatase non-receptor type 11; Growth factor receptor-bound protein 2; GTPase HRas] |
species 68 |
[Interferon gamma receptor 1; Interferon gamma; Tyrosine-protein kinase JAK2; SBO:0000286; phosphorylated; Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3] |
species 66 |
[Mitogen-activated protein kinase 1; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated] |
species 21 |
[Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 3; phosphorylated] |
species 32 |
[Growth factor receptor-bound protein 2] |
species 29 |
[Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 3; phosphorylated] |
species 30 |
[Suppressor of cytokine signaling 3; SBO:0000278] |
species 17 |
[Interleukin-6; Interleukin-6 receptor subunit beta; Interleukin-6 receptor subunit alpha; Tyrosine-protein kinase JAK1; SBO:0000286; Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3] |
species 12 |
[Signal transducer and activator of transcription 3; phosphorylated] |
species 15 |
[Interleukin-6 receptor subunit alpha; Interleukin-6; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; SBO:0000286; Suppressor of cytokine signaling 3] |
species 94 |
[Signal transducer and activator of transcription 1-alpha/beta; phosphorylated; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
s118 |
[Interferon gamma; Interferon gamma receptor 1; Tyrosine-protein kinase JAK2; SBO:0000286; Signal transducer and activator of transcription 3] |
s119 |
[Interferon gamma receptor 1; Interferon gamma; Tyrosine-protein kinase JAK2; SBO:0000286; phosphorylated; Signal transducer and activator of transcription 3] |
species 37 |
[Interleukin-6 receptor subunit alpha; Interleukin-6; Interleukin-6 receptor subunit beta; Tyrosine-protein kinase JAK1; SBO:0000286; Son of sevenless homolog 1; Tyrosine-protein phosphatase non-receptor type 11; Growth factor receptor-bound protein 2] |
species 38 |
[GTPase HRas; inactive] |
species 42 |
[RAF proto-oncogene serine/threonine-protein kinase; GTPase HRas] |
species 74 |
[CCAAT/enhancer-binding protein beta] |
species 64 |
[Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
species 11 |
[Signal transducer and activator of transcription 3] |
species 85 |
[Signal transducer and activator of transcription 1-alpha/beta; phosphorylated] |
species 95 |
[Signal transducer and activator of transcription 1-alpha/beta; phosphorylated] |
species 43 |
[GTPase HRas; phosphorylated; active] |
species 22 |
[Signal transducer and activator of transcription 3; Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; SBO:0000608; phosphorylated] |
species 82 |
[Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; SBO:0000286; phosphorylated] |
s126 |
[Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated] |
species 41 |
[RAF proto-oncogene serine/threonine-protein kinase] |
species 99 |
[Suppressor of cytokine signaling 1] |
species 26 |
[Signal transducer and activator of transcription 3; phosphorylated] |
species 40 |
[GTPase HRas; active] |
Observables: none
MODEL1108260015
@ v0.0.1
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…
DetailsThe paper described a limited part of the coagulation pathway, and in particular the inhibitory effects of activated protein C in the context of thrombin production. This is a computational modeling study with various assumption made of kinetic rates laws and their summation. The level of complexity and assumed parameters makes conclusions uncertain. However, an interesting outcome is that kinetic reaction rates may show oscillation behavior under particular, high levels of protein C feedback inhibition. The model would defy quantitative practical use, but could have predictive value as a qualitative descriptor of coagulation. link: http://identifiers.org/pubmed/15121060
Parameters: none
States: none
Observables: none
MODEL6185511733
@ v0.0.1
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…
DetailsPhysicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics. link: http://identifiers.org/pubmed/17907797
Parameters: none
States: none
Observables: none
MODEL6185746832
@ v0.0.1
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…
DetailsPhysicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics. link: http://identifiers.org/pubmed/17907797
Parameters: none
States: none
Observables: none
MODEL1409240002
@ v0.0.1
Qosa2014 - Mechanistic modeling that describes Aβ clearance across BBBQosa2014 - Mechanistic modeling that describes Aβ…
DetailsAlzheimer's disease (AD) has a characteristic hallmark of amyloid-β (Aβ) accumulation in the brain. This accumulation of Aβ has been related to its faulty cerebral clearance. Indeed, preclinical studies that used mice to investigate Aβ clearance showed that efflux across blood-brain barrier (BBB) and brain degradation mediate efficient Aβ clearance. However, the contribution of each process to Aβ clearance remains unclear. Moreover, it is still uncertain how species differences between mouse and human could affect Aβ clearance. Here, a modified form of the brain efflux index method was used to estimate the contribution of BBB and brain degradation to Aβ clearance from the brain of wild type mice. We estimated that 62% of intracerebrally injected (125)I-Aβ40 is cleared across BBB while 38% is cleared by brain degradation. Furthermore, in vitro and in silico studies were performed to compare Aβ clearance between mouse and human BBB models. Kinetic studies for Aβ40 disposition in bEnd3 and hCMEC/D3 cells, representative in vitro mouse and human BBB models, respectively, demonstrated 30-fold higher rate of (125)I-Aβ40 uptake and 15-fold higher rate of degradation by bEnd3 compared to hCMEC/D3 cells. Expression studies showed both cells to express different levels of P-glycoprotein and RAGE, while LRP1 levels were comparable. Finally, we established a mechanistic model, which could successfully predict cellular levels of (125)I-Aβ40 and the rate of each process. Established mechanistic model suggested significantly higher rates of Aβ uptake and degradation in bEnd3 cells as rationale for the observed differences in (125)I-Aβ40 disposition between mouse and human BBB models. In conclusion, current study demonstrates the important role of BBB in the clearance of Aβ from the brain. Moreover, it provides insight into the differences between mouse and human BBB with regards to Aβ clearance and offer, for the first time, a mathematical model that describes Aβ clearance across BBB. link: http://identifiers.org/pubmed/24467845
Parameters: none
States: none
Observables: none
BIOMD0000000110
@ v0.0.1
This model is from the article: Dynamics of the cell cycle: checkpoints, sizers, and timers. Qu Z, MacLellan WR…
DetailsWe have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings. link: http://identifiers.org/pubmed/14645053
Parameters:
Name | Description |
---|---|
k4 = 30.0; k3 = 30.0 |
Reaction: y => x1; c, Rate Law: cell*(k3*c*y-x1*k4) |
k16u = 25.0; k16 = 2.0 |
Reaction: ixp => x, Rate Law: cell*k16*k16u*ixp |
bi = 0.1; ci = 1.0; ai = 10.0 |
Reaction: ix => ixp; x, Rate Law: cell*((bi+ci*x)*ix-ai*ixp) |
k1 = 300.0 |
Reaction: => y, Rate Law: k1*cell |
k11 = 1.0 |
Reaction: w0 =>, Rate Law: cell*w0*k11 |
k2 = 5.0; k2u = 50.0 |
Reaction: y => ; u, Rate Law: cell*(k2+k2u*u)*y |
k10 = 10.0 |
Reaction: => w0, Rate Law: k10*cell |
bw = 0.1; cw = 1.0; aw = 10.0 |
Reaction: w0 => w1; x, Rate Law: cell*((bw+cw*x)*w0-aw*w1) |
a = 4.0; Tau = 25.0 |
Reaction: => u; x, Rate Law: cell*x^2/(a^2+x^2)/Tau |
cz = 1.0; bz = 0.1; az = 10.0 |
Reaction: z0 => z1; x, Rate Law: cell*((bz+cz*x)*z0-z1*az) |
k7u = 0.0; k7 = 10.0 |
Reaction: x => ; u, Rate Law: cell*(k7+k7u*u)*x |
Tau = 25.0 |
Reaction: u =>, Rate Law: cell*u/Tau |
k14 = 1.0; k15 = 1.0 |
Reaction: i + x => ix, Rate Law: (k14*x*i-k15*ix)*cell |
k5 = 0.1; k6 = 1.0 |
Reaction: x => x1; z2, w0, Rate Law: cell*((k6+w0)*x-(k5+z2)*x1) |
k13 = 1.0 |
Reaction: i =>, Rate Law: cell*k13*i |
k12 = 0.0 |
Reaction: => i, Rate Law: k12*cell |
k9 = 1.0 |
Reaction: z0 =>, Rate Law: cell*k9*z0 |
k8 = 100.0 |
Reaction: => z0, Rate Law: cell*k8 |
States:
Name | Description |
---|---|
ix |
[IPR003175; IPR006670; cyclin-dependent protein kinase holoenzyme complex] |
i |
[IPR003175] |
c |
[cyclin-dependent protein kinase holoenzyme complex] |
z1 |
[Cell division control protein 25] |
x |
[IPR006670; cyclin-dependent protein kinase holoenzyme complex] |
z0 |
[Cell division control protein 25] |
w1 |
[Wee1-like protein kinase] |
x1 |
[IPR006670; cyclin-dependent protein kinase holoenzyme complex] |
totalCyclin |
[IPR006670] |
ixp |
[IPR003175; IPR006670; cyclin-dependent protein kinase holoenzyme complex] |
u |
[IPR001810; anaphase-promoting complex] |
z2 |
[Cell division control protein 25] |
w0 |
[Wee1-like protein kinase] |
y |
[IPR006670] |
Observables: none
MODEL1507180067
@ v0.0.1
Quek2008 - Genome-scale metabolic network of Mus musculusThis model is described in the article: [On the reconstruction…
DetailsGenome-scale metabolic modeling is a systems-based approach that attempts to capture the metabolic complexity of the whole cell, for the purpose of gaining insight into metabolic function and regulation. This is achieved by organizing the metabolic components and their corresponding interactions into a single context. The reconstruction process is a challenging and laborious task, especially during the stage of manual curation. For the mouse genome-scale metabolic model, however, we were able to rapidly reconstruct a compartmentalized model from well-curated metabolic databases online. The prototype model was comprehensive. Apart from minor compound naming and compartmentalization issues, only nine additional reactions without gene associations were added during model curation before the model was able to simulate growth in silico. Further curation led to a metabolic model that consists of 1399 genes mapped to 1757 reactions, with a total of 2037 reactions compartmentalized into the cytoplasm and mitochondria, capable of reproducing metabolic functions inferred from literatures. The reconstruction is made more tractable by developing a formal system to update the model against online databases. Effectively, we can focus our curation efforts into establishing better model annotations and gene-protein-reaction associations within the core metabolism, while relying on genome and proteome databases to build new annotations for peripheral pathways, which may bear less relevance to our modeling interest. link: http://identifiers.org/pubmed/19425150
Parameters: none
States: none
Observables: none
MODEL1504080000
@ v0.0.1
Quek2014 - Metabolic flux analysis of HEK cell culture using Recon 2 (reduced version of Recon 2)This model is described…
DetailsA representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis. link: http://identifiers.org/pubmed/24907410
Parameters: none
States: none
Observables: none
BIOMD0000000953
@ v0.0.1
Mathematical model of mitotic exit in budding yeast.
DetailsAfter anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the "mitotic exit network" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast. link: http://identifiers.org/pubmed/16713564
Parameters: none
States: none
Observables: none
BIOMD0000000409
@ v0.0.1
This model is from the article: Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in buddin…
DetailsAfter anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the "mitotic exit network" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast. link: http://identifiers.org/pubmed/16713564
Parameters:
Name | Description |
---|---|
kd = 0.45; Jnet = 0.2; kad = 0.1 |
Reaction: Net1P => Net1; Cdc14, Clb2, PP2A, Rate Law: (kad*Cdc14+kd*PP2A)*Net1P/(Jnet+Net1P) |
kssecurin = 0.03 |
Reaction: AA => securinT + securin, Rate Law: kssecurin |
ldnet = 1.0 |
Reaction: Net1Cdc14 => Net1, Rate Law: ldnet*Net1Cdc14 |
kadpolo = 0.25; kdpolo = 0.01 |
Reaction: Polo => degr; Cdh1, Rate Law: (kdpolo+kadpolo*Cdh1)*Polo |
Jpolo = 0.25; kipolo = 0.1 |
Reaction: Polo => Polo_i, Rate Law: kipolo*Polo/(Jpolo+Polo) |
PP2AT = 1.0; kpp = 0.1; ki = 20.0 |
Reaction: PP2A = (1+kpp*ki*separase)/(1+ki*separase)*PP2AT, Rate Law: missing |
kdsecurin = 0.05; kadsecurin = 2.0 |
Reaction: securinT + securin => degr; Cdc20, Rate Law: (kdsecurin+kadsecurin*Cdc20)*securinT |
kaicdc15 = 0.2; kicdc15 = 0.0; Jcdc15 = 0.2; Cdk = NaN |
Reaction: Cdc15 => Cdc15_i, Rate Law: (kicdc15+kaicdc15*Cdk)*Cdc15/(Jcdc15+Cdc15) |
Cdh1T = 1.0; Jcdh = 0.0015; kadcdh = 1.0; kdcdh = 0.01 |
Reaction: Cdh1_i => Cdh1; Cdc14, Rate Law: (kdcdh+kadcdh*Cdc14)*(Cdh1T-Cdh1)/((Jcdh+Cdh1T)-Cdh1) |
kitem = 0.1; kaitem = 1.0; Jtem1 = 0.005 |
Reaction: Tem1 => Tem1_i; PP2A, Rate Law: (kitem+kaitem*PP2A)*Tem1/(Jtem1+Tem1) |
kdcdc20 = 0.05; kadcdc20 = 2.0 |
Reaction: Cdc20 => degr; Cdh1, Rate Law: (kdcdc20+kadcdc20*Cdh1)*Cdc20 |
lamen = 10.0 |
Reaction: AA => MEN; Tem1, Cdc15, Rate Law: lamen*(Tem1-MEN)*(Cdc15-MEN) |
kp = 0.4; Jnet = 0.2; kap = 2.0; Cdk = NaN |
Reaction: Net1Cdc14 => Net1P; MEN, Net1, Clb2, Rate Law: (kp*Cdk+kap*MEN)*Net1Cdc14/(Jnet+Net1+Net1Cdc14) |
Cdc14T = 0.5 |
Reaction: Cdc14 = Cdc14T-Net1Cdc14, Rate Law: missing |
ksclb2 = 0.03 |
Reaction: AA => Clb2, Rate Law: ksclb2 |
Net1T = 1.0 |
Reaction: Net1P = (Net1T-Net1)-Net1Cdc14, Rate Law: missing |
Tem1T = 1.0 |
Reaction: Tem1_i = Tem1T-Tem1, Rate Law: missing |
kaacdc15 = 0.5; Jcdc15 = 0.2; kacdc15 = 0.02; Cdc15T = 1.0 |
Reaction: Cdc15_i => Cdc15; Cdc14, Rate Law: (kacdc15+kaacdc15*Cdc14)*(Cdc15T-Cdc15)/((Jcdc15+Cdc15T)-Cdc15) |
kadclb2 = 0.2; kaadclb2 = 2.0; kdclb2 = 0.03 |
Reaction: Clb2 => degr; Cdc20, Cdh1, Rate Law: (kdclb2+kadclb2*Cdc20+kaadclb2*Cdh1)*Clb2 |
kscdc20 = 0.015 |
Reaction: AA => Cdc20, Rate Law: kscdc20 |
ksseparase = 0.001 |
Reaction: AA => separaseT + separase, Rate Law: ksseparase |
lanet = 500.0 |
Reaction: Net1 => Net1Cdc14; Cdc14, Rate Law: lanet*Net1*Cdc14 |
ldmen = 0.1 |
Reaction: MEN => degr, Rate Law: ldmen*MEN |
kaapolo = 0.5; Jpolo = 0.25; kapolo = 0.0; Cdk = NaN |
Reaction: Polo_i => Polo; PoloT, Rate Law: (kapolo+kaapolo*Cdk)*(PoloT-Polo)/((Jpolo+PoloT)-Polo) |
kspolo = 0.01 |
Reaction: AA => PoloT + Polo_i, Rate Law: kspolo |
Jcdh = 0.0015; Cdk = NaN; kapcdh = 1.0 |
Reaction: Cdh1 => Cdh1_i, Rate Law: kapcdh*Cdk*Cdh1/(Jcdh+Cdh1) |
kaatem = 0.5; katem = 0.0; Tem1T = 1.0; Jtem1 = 0.005 |
Reaction: Tem1_i => Tem1; Polo, Rate Law: (katem+kaatem*Polo)*(Tem1T-Tem1)/((Jtem1+Tem1T)-Tem1) |
ldsecurin = 1.0; lasecurin = 500.0 |
Reaction: securin + separase => securinseparase, Rate Law: lasecurin*securin*separase-ldsecurin*securinseparase |
Cdc15T = 1.0 |
Reaction: Cdc15_i = Cdc15T-Cdc15, Rate Law: missing |
kdseparase = 0.004 |
Reaction: separaseT + separase => degr, Rate Law: kdseparase*separaseT |
Cdh1T = 1.0 |
Reaction: Cdh1_i = Cdh1T-Cdh1, Rate Law: missing |
States:
Name | Description |
---|---|
Polo |
[Cell cycle serine/threonine-protein kinase CDC5/MSD2] |
securinT |
[Securin] |
Polo i |
[Cell cycle serine/threonine-protein kinase CDC5/MSD2] |
MEN |
[Protein TEM1; Cell division control protein 15] |
Tem1 |
[Protein TEM1] |
Tem1 i |
[Protein TEM1] |
Net1 |
[Nucleolar protein NET1] |
degr |
degr |
Cdh1 i |
[APC/C activator protein CDH1] |
separaseT |
[Separin] |
Clb2 |
[G2/mitotic-specific cyclin-2] |
separase |
[Separin] |
Cdc15 |
[Cell division control protein 15] |
Cdc14 |
[Tyrosine-protein phosphatase CDC14] |
securinseparase |
[Securin; Separin] |
Net1Cdc14 |
[Nucleolar protein NET1; Tyrosine-protein phosphatase CDC14] |
securin |
[Securin] |
PP2A |
[Protein phosphatase PP2A regulatory subunit B] |
PoloT |
[Cell cycle serine/threonine-protein kinase CDC5/MSD2] |
Net1P |
[Nucleolar protein NET1; Phosphoprotein] |
AA |
[alpha-amino acid] |
Cdh1 |
[APC/C activator protein CDH1] |
Cdc20 |
[APC/C activator protein CDC20] |
Cdc15 i |
[Cell division control protein 15] |
Observables: none
BIOMD0000000836
@ v0.0.1
This model is from the article: Epidemics of panic during a bioterrorist attack--a mathematical model. Radosavljevic…
DetailsA bioterrorist attacks usually cause epidemics of panic in a targeted population. We have presented epidemiologic aspect of this phenomenon as a three-component model–host, information on an attack and social network. We have proposed a mathematical model of panic and counter-measures as the function of time in a population exposed to a bioterrorist attack. The model comprises ordinary differential equations and graphically presented combinations of the equations parameters. Clinically, we have presented a model through a sequence of psychic conditions and disorders initiated by an act of bioterrorism. This model might be helpful for an attacked community to timely and properly apply counter-measures and to minimize human mental suffering during a bioterrorist attack. link: http://identifiers.org/pubmed/19423234
Parameters:
Name | Description |
---|---|
delta = 1.0; gamma = 0.0 |
Reaction: P = ((-gamma)+delta*S)*P, Rate Law: ((-gamma)+delta*S)*P |
alpha = 6.0; C = 10.0; beta = 2.8 |
Reaction: S = (alpha*(1-S/C)-beta*P)*S, Rate Law: (alpha*(1-S/C)-beta*P)*S |
States:
Name | Description |
---|---|
S |
panic_intensity |
P |
protection+prevention_intensity |
Observables: none
MODEL7743576806
@ v0.0.1
Radulescu2008 - NF-κB hierarchy ℳ(16,34,46)This is a model of NF-κB pathway functioning from hierarchy of models of decr…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
MODEL7743212613
@ v0.0.1
# NFkB model M(5,8,12) - minimal model This is a model of NFkB pathway functioning from hierarchy of models of decreasi…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
MODEL7743315447
@ v0.0.1
# NFkB model M(6,10,15) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
MODEL7743358405
@ v0.0.1
# NFkB model M(8,12,19) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
BIOMD0000000226
@ v0.0.1
# NFkB model M(14,25,28) - Lipniacky's NFkB model This is a model of NFkB pathway functioning from hierarchy of models…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters:
Name | Description |
---|---|
kr13 = 0.0; kf13 = 18.4 |
Reaction: s160 + s161 => s135, Rate Law: kf13*s161*s160-kr13*s135 |
k8 = 0.1 |
Reaction: s139 => s132, Rate Law: k8*s139 |
kf28 = 0.01; kr28 = 0.0 |
Reaction: s159 => s135, Rate Law: kf28*s159-kr28*s135 |
k3 = 2.5E-6 |
Reaction: s150 => s133, Rate Law: k3 |
k10 = 0.1 |
Reaction: s152 => s161 + s132, Rate Law: k10*s152 |
k19 = 0.0; k20 = 5.0E-7 |
Reaction: s126 => s127; s164, Rate Law: k19+k20*s164 |
k6 = 1.25E-4 |
Reaction: s132 => s134, Rate Law: k6*s132 |
k21 = 1.0E-4 |
Reaction: s160 => s122, Rate Law: k21*s160 |
kr23 = 5.0E-4; kf23 = 0.001 |
Reaction: s160 => s167, Rate Law: kf23*s160-kr23*s167 |
k27 = 4.0E-4 |
Reaction: s125 => s124, Rate Law: k27*s125 |
k11 = 1.25E-4 |
Reaction: s130 => s129, Rate Law: k11*s130 |
k9 = 1.0 |
Reaction: s132 + s135 => s152, Rate Law: k9*s132*s135 |
k17 = 4.0E-4 |
Reaction: s127 => s153, Rate Law: k17*s127 |
k26 = 5.0E-7 |
Reaction: s121 => s125; s164, Rate Law: k26*s164 |
k2 = 1.25E-4 |
Reaction: s133 => s131, Rate Law: k2*s133 |
kr14 = 0.0; kf14 = 18.4 |
Reaction: s164 + s167 => s159, Rate Law: kf14*s164*s167-kr14*s159 |
k7 = 0.2 |
Reaction: s160 + s132 => s139, Rate Law: k7*s132*s160 |
k5 = 0.0015; k4 = 0.1 |
Reaction: s132 => s130; s128, Rate Law: k5*s132+k4*s132*s128 |
k22 = 0.5 |
Reaction: s125 => s160 + s125, Rate Law: k22*s125 |
k18 = 3.0E-4 |
Reaction: s128 => s154, Rate Law: k18*s128 |
k1 = 0.0025 |
Reaction: s133 => s132, Rate Law: k1*s133 |
k16 = 0.5 |
Reaction: s127 => s128 + s127, Rate Law: k16*s127 |
k12 = 2.0E-5 |
Reaction: s135 => s161; s132, Rate Law: k12*s135 |
kf15 = 0.0025; kr15 = 0.0 |
Reaction: s161 => s164, Rate Law: kf15*s161-kr15*s164 |
States:
Name | Description |
---|---|
s150 |
[Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s124 |
sa12_degraded |
s135 |
[Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha] |
s159 |
[Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha] |
s121 |
[NF-kappa-B inhibitor alpha] |
s153 |
sa96_degraded |
s122 |
sa13_degraded |
s128 |
[Tumor necrosis factor alpha-induced protein 3] |
s132 |
[Inhibitor of nuclear factor kappa-B kinase subunit beta; Inhibitor of nuclear factor kappa-B kinase subunit alpha; NF-kappa-B essential modulator] |
s167 |
[NF-kappa-B inhibitor alpha] |
s160 |
[NF-kappa-B inhibitor alpha] |
s127 |
[Tnfaip3-201] |
s129 |
sa444_degraded |
s152 |
[Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s134 |
sa20_degraded |
s154 |
sa97_degraded |
s130 |
[Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s164 |
[Nuclear factor NF-kappa-B p105 subunit] |
s131 |
sa19_degraded |
s139 |
[NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s133 |
[Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s161 |
[Nuclear factor NF-kappa-B p105 subunit] |
s126 |
[Tumor necrosis factor alpha-induced protein 3] |
s125 |
[Nfkbia-201] |
Observables: none
MODEL7743444866
@ v0.0.1
# NFkB model M(14,25,33) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
MODEL7743528808
@ v0.0.1
# NFkB model M(14,30,41) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
MODEL7743608569
@ v0.0.1
# NFkB model M(24,45,62) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
MODEL7743631122
@ v0.0.1
# NFkB model M(34,60,82) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters: none
States: none
Observables: none
BIOMD0000000227
@ v0.0.1
# NFkB model M(39,65,90) - most complex model This is a model of NFkB pathway functioning from hierarchy of models of d…
DetailsBACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041
Parameters:
Name | Description |
---|---|
k8 = 0.1 |
Reaction: s191 => s132, Rate Law: k8*s191 |
k37 = 5.0E-5 |
Reaction: s113 => s112, Rate Law: k37*s113 |
kf59 = 0.0038; kr59 = 8.0E-13 |
Reaction: s213 => s205 + s192, Rate Law: kf59*s213-kr59*s192*s205 |
k10 = 0.1 |
Reaction: s189 => s190 + s132, Rate Law: k10*s189 |
k53 = 2.0E-4 |
Reaction: s190 => s156, Rate Law: k53*s190 |
kr23 = 5.0E-4; kf23 = 0.001 |
Reaction: s123 => s93, Rate Law: kf23*s123-kr23*s93 |
k45 = 0.0053 |
Reaction: s117 => s119 + s117, Rate Law: k45*s117 |
kf52 = 0.003; kr52 = 0.001 |
Reaction: s114 + s119 => s190, Rate Law: kf52*s114*s119-kr52*s190 |
k7 = 0.24 |
Reaction: s123 + s132 => s191, Rate Law: k7*s132*s123 |
k39 = 1.3E-4 |
Reaction: s110 => s114, Rate Law: k39*s110 |
kf57 = 18.4; kr57 = 0.055 |
Reaction: s93 + s212 => s213, Rate Law: kf57*s93*s212-kr57*s213 |
kr56 = 4.8E-4; kf56 = 0.62 |
Reaction: s195 + s206 => s214, Rate Law: kf56*s195*s206-kr56*s214 |
kf66 = 18.4; kr66 = 0.055 |
Reaction: s200 + s93 => s201, Rate Law: kf66*s93*s200-kr66*s201 |
k43 = 0.1; k42 = 5.0E-4 |
Reaction: s160 => s165; s193, s194, Rate Law: k42*s193+k43*s194 |
k72 = 2.0E-4 |
Reaction: s192 => s158, Rate Law: k72*s192 |
k5 = 0.0015; k4 = 0.1 |
Reaction: s132 => s130; s128, Rate Law: k5*s132+k4*s132*s128 |
kf64 = 0.62; kr64 = 4.8E-4 |
Reaction: s199 + s195 => s200, Rate Law: kf64*s195*s199-kr64*s200 |
k47 = 6.4E-5 |
Reaction: s119 => s120, Rate Law: k47*s119 |
kr14 = 0.0; kf14 = 0.5 |
Reaction: s195 + s93 => s192, Rate Law: kf14*s195*s93-kr14*s192 |
k22 = 0.5 |
Reaction: s178 => s123 + s178, Rate Law: k22*s178 |
k18 = 3.0E-4 |
Reaction: s128 => s154, Rate Law: k18*s128 |
k61 = 0.06; k49 = 5.0E-4; k50 = 0.02; k62 = 0.6 |
Reaction: s209 => s185; s214, s212, s205, s206, Rate Law: k49*s205+k50*s206+k62*s214+k61*s212 |
k12 = 2.0E-5 |
Reaction: s188 => s190, Rate Law: k12*s188 |
k33 = 5.0E-4; k70 = 0.06; k69 = 0.006; k34 = 0.1 |
Reaction: s170 => s173; s200, s199, s198, s196, Rate Law: k33*s198+k34*s199+k69*s196+k70*s200 |
k36 = 0.0041 |
Reaction: s113 => s110 + s113, Rate Law: k36*s113 |
k9 = 1.2 |
Reaction: s132 + s188 => s189, Rate Law: k9*s132*s188 |
k51 = 0.025 |
Reaction: s185 => s178, Rate Law: k51*s185 |
kf28 = 0.01; kr28 = 0.0 |
Reaction: s192 => s188, Rate Law: kf28*s192-kr28*s188 |
k19 = 0.0; k20 = 5.0E-7 |
Reaction: s126 => s127; s195, Rate Law: k19+k20*s195 |
kr13 = 0.0; kf13 = 0.5 |
Reaction: s123 + s190 => s188, Rate Law: kf13*s190*s123-kr13*s188 |
k6 = 1.25E-4 |
Reaction: s132 => s134, Rate Law: k6*s132 |
k21 = 1.0E-4 |
Reaction: s123 => s122, Rate Law: k21*s123 |
k1 = 0.0 |
Reaction: s133 => s132, Rate Law: k1*s133 |
k71 = 2.0E-4 |
Reaction: s188 => s157, Rate Law: k71*s188 |
k46 = 5.0E-5 |
Reaction: s117 => s118, Rate Law: k46*s117 |
k38 = 6.0E-5 |
Reaction: s110 => s109, Rate Law: k38*s110 |
k27 = 4.0E-4 |
Reaction: s178 => s124, Rate Law: k27*s178 |
k11 = 1.25E-4 |
Reaction: s130 => s129, Rate Law: k11*s130 |
kf32 = 10.0; kr32 = 1.0E-4 |
Reaction: s22 + s198 => s199, Rate Law: kf32*s198*s22-kr32*s199 |
kr58 = 0.055; kf58 = 18.4 |
Reaction: s93 + s214 => s215, Rate Law: kf58*s93*s214-kr58*s215 |
kr68 = 8.0E-13; kf68 = 0.0038 |
Reaction: s201 => s199 + s192, Rate Law: kf68*s201-kr68*s192*s199 |
kr65 = 0.055; kf65 = 18.4 |
Reaction: s196 + s93 => s197, Rate Law: kf65*s93*s196-kr65*s197 |
kr67 = 8.0E-13; kf67 = 0.0038 |
Reaction: s197 => s198 + s192, Rate Law: kf67*s197-kr67*s192*s198 |
k17 = 4.0E-4 |
Reaction: s127 => s153, Rate Law: k17*s127 |
k40 = 6.4E-5 |
Reaction: s114 => s111, Rate Law: k40*s114 |
k2 = 1.25E-4 |
Reaction: s133 => s131, Rate Law: k2*s133 |
kr55 = 4.8E-4; kf55 = 0.62 |
Reaction: s195 + s205 => s212, Rate Law: kf55*s195*s205-kr55*s212 |
kr63 = 4.8E-4; kf63 = 0.62 |
Reaction: s195 + s198 => s196, Rate Law: kf63*s195*s198-kr63*s196 |
kr41 = 1.0E-4; kf41 = 10.0 |
Reaction: s193 + s36 => s194, Rate Law: kf41*s36*s193-kr41*s194 |
k44 = 0.016 |
Reaction: s165 => s117, Rate Law: k44*s165 |
kr48 = 1.0E-4; kf48 = 10.0 |
Reaction: s65 + s205 => s206, Rate Law: kf48*s65*s205-kr48*s206 |
k16 = 0.5 |
Reaction: s127 => s128 + s127, Rate Law: k16*s127 |
k3 = 1.0E-5 |
Reaction: s150 => s133, Rate Law: k3 |
k35 = 0.01 |
Reaction: s173 => s113, Rate Law: k35*s173 |
k54 = 2.0E-4 |
Reaction: s195 => s108, Rate Law: k54*s195 |
States:
Name | Description |
---|---|
s113 |
[Nfkb1-201] |
s213 |
PromIkBa:RNAP3:p50p65:IkBa |
s122 |
sa13_degraded |
s128 |
[Tumor necrosis factor alpha-induced protein 3] |
s36 |
[Transcription factor RelB] |
s197 |
Promp105:RNAP1:p50p65:IkBa |
s178 |
[Nfkbia-201] |
s198 |
Promp105:RNAP |
s189 |
[Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator; Transcription factor p65] |
s160 |
InactivePRaseonp65 |
s127 |
[Tnfaip3-201] |
s134 |
sa20_degraded |
s129 |
sa444_degraded |
s192 |
[Nuclear factor NF-kappa-B p105 subunit; Transcription factor p65; NF-kappa-B inhibitor alpha] |
s119 |
[Transcription factor p65] |
s118 |
sa8_degraded |
s205 |
PromIkBa:RNAP3 |
s131 |
sa19_degraded |
s133 |
[Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s126 |
[Tumor necrosis factor alpha-induced protein 3] |
s193 |
Promp65:RNAP2 |
s111 |
sa438_degraded |
s112 |
sa3_degraded |
s124 |
sa12_degraded |
s156 |
csa21_degraded |
s109 |
sa4_degraded |
s214 |
IkBa:RNAP3:FTAz:p50p65 |
s93 |
[NF-kappa-B inhibitor alpha] |
s117 |
[Rela-201] |
s120 |
sa9_degraded |
s165 |
ActivePRaseonp65 |
s132 |
[Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s206 |
PromIkBa:RNAP3:FTAz |
s22 |
[Transcription factor RelB] |
s185 |
ActivePRaseonIkB_alpha |
s199 |
Promp105:RNAP:FTAX |
s170 |
InactivePRaseonp105 |
s130 |
[Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator] |
s215 |
PromIkBa:RNAP3:FTAz:p50p65:IkB_alpha |
s195 |
[Nuclear factor NF-kappa-B p105 subunit; Transcription factor p65] |
s188 |
[Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha; Transcription factor p65] |
s108 |
csa17_degraded |
s123 |
[NF-kappa-B inhibitor alpha] |
s201 |
Promp105:RNAP1:FTAx:p50p65:IkBa |
s114 |
[Nuclear factor NF-kappa-B p105 subunit] |
s173 |
ActivePRaseonp105 |
s190 |
[Nuclear factor NF-kappa-B p105 subunit; Transcription factor p65] |
s65 |
[Transcription factor RelB] |
s110 |
[Nuclear factor NF-kappa-B p105 subunit] |
s200 |
Promp105:RNAP1:FTAx:p50p65 |
s158 |
csa9_degraded |
Observables: none
MODEL1507180058
@ v0.0.1
Raghunathan2009 - Genome-scale metabolic network of Salmonella typhimurium (iRR1083)This model is described in the artic…
DetailsBACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. CONCLUSION: Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains. link: http://identifiers.org/pubmed/19356237
Parameters: none
States: none
Observables: none
MODEL1507180003
@ v0.0.1
Raghunathan2010 - Genome-scale metabolic network of Francisella tularensis (iRS605)This model is described in the articl…
DetailsBACKGROUND: Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell. RESULTS: We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection. CONCLUSIONS: This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic. link: http://identifiers.org/pubmed/20731870
Parameters: none
States: none
Observables: none
BIOMD0000000313
@ v0.0.1
This is the model of IL13 induced signalling in MedB-1 cell described in the article: **Dynamic Mathematical Modeling o…
DetailsPrimary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. link: http://identifiers.org/pubmed/21127196
Parameters:
Name | Description |
---|---|
pSTAT5_dephosphorylation = 3.43392E-4 |
Reaction: pSTAT5 => STAT5; SHP1, Rate Law: pSTAT5_dephosphorylation*pSTAT5*SHP1*cell |
Kon_IL13Rec = 0.00341992 |
Reaction: Rec => IL13_Rec; IL13, Rate Law: Kon_IL13Rec*IL13*Rec*cell |
STAT5_phosphorylation = 0.0382596 |
Reaction: STAT5 => pSTAT5; pJAK2, Rate Law: STAT5_phosphorylation*STAT5*pJAK2*cell |
SOCS3_degradation = 0.0429186 |
Reaction: SOCS3 =>, Rate Law: SOCS3_degradation*SOCS3*cell |
DecoyR_binding = 1.24391E-4 |
Reaction: DecoyR => IL13_DecoyR; IL13, Rate Law: DecoyR_binding*IL13*DecoyR*cell |
Rec_intern = 0.103346 |
Reaction: Rec => Rec_i, Rate Law: Rec_intern*Rec*cell |
CD274mRNA_production = 8.21752E-5 |
Reaction: => CD274mRNA; pSTAT5, Rate Law: pSTAT5*CD274mRNA_production*cell |
pJAK2_dephosphorylation = 6.21906E-4 |
Reaction: pJAK2 => JAK2; SHP1, Rate Law: pJAK2_dephosphorylation*pJAK2*SHP1*cell |
pRec_degradation = 0.172928 |
Reaction: p_IL13_Rec_i =>, Rate Law: pRec_degradation*p_IL13_Rec_i*cell |
SOCS3_accumulation = 3.70803; SOCS3_translation = 11.9086 |
Reaction: => SOCS3; SOCS3mRNA, Rate Law: SOCS3mRNA*SOCS3_translation/(SOCS3_accumulation+SOCS3mRNA)*cell |
SOCS3mRNA_production = 0.00215826 |
Reaction: => SOCS3mRNA; pSTAT5, Rate Law: pSTAT5*SOCS3mRNA_production*cell |
pRec_intern = 0.15254 |
Reaction: p_IL13_Rec => p_IL13_Rec_i, Rate Law: pRec_intern*p_IL13_Rec*cell |
Rec_recycle = 0.00135598 |
Reaction: Rec_i => Rec, Rate Law: Rec_recycle*Rec_i*cell |
IL13stimulation = 1.0 ng_per_ml |
Reaction: IL13 = 2.265*IL13stimulation, Rate Law: missing |
Rec_phosphorylation = 999.631 |
Reaction: IL13_Rec => p_IL13_Rec; pJAK2, Rate Law: Rec_phosphorylation*IL13_Rec*pJAK2*cell |
JAK2_phosphorylation = 0.157057; JAK2_p_inhibition = 0.0168268 |
Reaction: JAK2 => pJAK2; IL13_Rec, SOCS3, Rate Law: JAK2_phosphorylation*IL13_Rec*JAK2/(1+JAK2_p_inhibition*SOCS3)*cell |
States:
Name | Description |
---|---|
p IL13 Rec |
[MOD:00048; Interleukin-13; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; Phosphoprotein; Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex; urn:miriam:mod:MOD%3A00048] |
SOCS3 |
[Suppressor of cytokine signaling 3] |
IL13 DecoyR |
[Interleukin-13; Interleukin-13 receptor subunit alpha-2] |
SOCS3mRNA |
[messenger RNA; RNA; Suppressor of cytokine signaling 3] |
pSTAT5 |
[MOD:00048; Signal transducer and activator of transcription 5B; urn:miriam:mod:MOD%3A00048; Signal transducer and activator of transcription 5A; Phosphoprotein] |
IL13 |
[Interleukin-13; interleukin-13 receptor binding] |
Rec i |
[Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex; Interleukin-13 receptor subunit alpha-1; receptor internalization; Interleukin-4 receptor subunit alpha] |
CD274mRNA |
[messenger RNA; RNA; T-cell surface glycoprotein CD3 zeta chain] |
STAT5 |
[Signal transducer and activator of transcription 5A; Signal transducer and activator of transcription 5B] |
p IL13 Rec i |
[MOD:00048; Interleukin-13; Non-receptor tyrosine-protein kinase TYK2; urn:miriam:mod:MOD%3A00048; interleukin-4 receptor complex; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; receptor internalization] |
IL13 Rec |
[Interleukin-13; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha; Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex] |
DecoyR |
[Interleukin-13 receptor subunit alpha-2] |
Rec |
[interleukin-4 receptor complex; Non-receptor tyrosine-protein kinase TYK2; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; interleukin-13 binding] |
pJAK2 |
[MOD:00048; Tyrosine-protein kinase JAK2; Phosphoprotein; urn:miriam:mod:MOD%3A00048] |
JAK2 |
[Tyrosine-protein kinase JAK2] |
Observables: none
BIOMD0000000314
@ v0.0.1
This is the model of IL13 induced signalling in L1236 cells described in the article: **Dynamic Mathematical Modeling…
DetailsPrimary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. link: http://identifiers.org/pubmed/21127196
Parameters:
Name | Description |
---|---|
Rec_phosphorylation = 9.07541 |
Reaction: IL13_Rec => p_IL13_Rec; pJAK2, Rate Law: Rec_phosphorylation*IL13_Rec*pJAK2*cell |
pSTAT5_dephosphorylation = 0.0116389 |
Reaction: pSTAT5 => STAT5; SHP1, Rate Law: pSTAT5_dephosphorylation*pSTAT5*SHP1*cell |
CD274mRNA_production = 0.0115928 |
Reaction: => CD274mRNA; pSTAT5, Rate Law: pSTAT5*CD274mRNA_production*cell |
Kon_IL13Rec = 0.00174087 |
Reaction: Rec => IL13_Rec; IL13, Rate Law: Kon_IL13Rec*IL13*Rec*cell |
pRec_degradation = 0.417538 |
Reaction: p_IL13_Rec_i =>, Rate Law: pRec_degradation*p_IL13_Rec_i*cell |
pJAK2_dephosphorylation = 0.0981611 |
Reaction: pJAK2 => JAK2; SHP1, Rate Law: pJAK2_dephosphorylation*pJAK2*SHP1*cell |
JAK2_phosphorylation = 0.300019 |
Reaction: JAK2 => pJAK2; IL13_Rec, Rate Law: JAK2_phosphorylation*JAK2*IL13_Rec*cell |
Rec_recycle = 0.0039243 |
Reaction: Rec_i => Rec, Rate Law: Rec_recycle*Rec_i*cell |
pRec_intern = 0.324132 |
Reaction: p_IL13_Rec => p_IL13_Rec_i, Rate Law: pRec_intern*p_IL13_Rec*cell |
IL13stimulation = 1.0 ng_per_ml |
Reaction: IL13 = 3.776*IL13stimulation, Rate Law: missing |
STAT5_phosphorylation = 0.00426767 |
Reaction: STAT5 => pSTAT5; pJAK2, Rate Law: STAT5_phosphorylation*STAT5*pJAK2*cell |
Rec_intern = 0.259686 |
Reaction: Rec => Rec_i, Rate Law: Rec_intern*Rec*cell |
States:
Name | Description |
---|---|
p IL13 Rec |
[Non-receptor tyrosine-protein kinase TYK2; Interleukin-13; interleukin-4 receptor complex; urn:miriam:mod:MOD%3A00048; Phosphoprotein; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha] |
pSTAT5 |
[Signal transducer and activator of transcription 5A; Signal transducer and activator of transcription 5B; Phosphoprotein; urn:miriam:mod:MOD%3A00048] |
IL13 |
[Interleukin-13; interleukin-13 receptor binding] |
Rec i |
[Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; receptor internalization] |
CD274mRNA |
[messenger RNA; RNA; T-cell surface glycoprotein CD3 zeta chain] |
STAT5 |
[Signal transducer and activator of transcription 5B; Signal transducer and activator of transcription 5A] |
p IL13 Rec i |
[urn:miriam:mod:MOD%3A00048; interleukin-4 receptor complex; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha; Non-receptor tyrosine-protein kinase TYK2; Interleukin-13; receptor internalization] |
IL13 Rec |
[Non-receptor tyrosine-protein kinase TYK2; Interleukin-13; interleukin-4 receptor complex; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1] |
Rec |
[interleukin-4 receptor complex; Non-receptor tyrosine-protein kinase TYK2; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha; interleukin-13 binding] |
pJAK2 |
[Tyrosine-protein kinase JAK2; Phosphoprotein; urn:miriam:mod:MOD%3A00048] |
JAK2 |
[Tyrosine-protein kinase JAK2] |
Observables: none
BIOMD0000000247
@ v0.0.1
This is the model with unfitted parameters described in the article **Dynamic rerouting of the carbohydrate flux is k…
DetailsEukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions. link: http://identifiers.org/pubmed/18154684
Parameters:
Name | Description |
---|---|
VmALD=322.258 mMpermin; KeqTPI=0.045 dimensionless; KeqALD=0.069 dimensionless; KmALDDHAP=2.4 mM; KmALDGAPi=10.0 mM; KmALDF16P=0.3 mM; KmALDGAP=2.0 mM |
Reaction: F16P => DHAP + GA3P, Rate Law: cytoplasm*VmALD*F16P/KmALDF16P*(1-DHAP*GA3P/(F16P*KeqALD))/(1+F16P/KmALDF16P+DHAP/KmALDDHAP+GA3P/KmALDGAP+F16P*GA3P/(KmALDF16P*KmALDGAPi)+DHAP*GA3P/(KmALDDHAP*KmALDGAP)) |
KmEry4P=0.09 mM; KmGA3P=2.1 mM; VmTransk2f=3.2 mMpermin; KmXyl5P=0.16 mM; KmF6P=1.1 mM; VmTransk2r=43.0 mMpermin |
Reaction: Xyl5P + Erythrose4P => GA3P + F6P, Rate Law: cytoplasm*(VmTransk2f*Erythrose4P*Xyl5P/(KmEry4P*KmXyl5P)-VmTransk2r*F6P*GA3P/(KmF6P*KmGA3P))/((1+Xyl5P/KmXyl5P+GA3P/KmGA3P)*(1+Erythrose4P/KmEry4P+F6P/KmF6P)) |
VmG6PDH=4.0 mMpermin; KmG6P=0.04 mM; KmNADP=0.02 mM; KiNADPH=0.017 mM |
Reaction: G6P + NADP => D6PGluconoLactone + NADPH; NADPH, Rate Law: cytoplasm*VmG6PDH*G6P*NADP/(KmG6P*KmNADP)/((1+G6P/KmG6P+NADPH/KiNADPH)*(1+NADP/KmNADP)) |
VmPPIf=3458.0 mMpermin; KmRibu5P=1.6 mM; KmRibo5P=1.6 mM; VmPPIr=3458.0 mMpermin |
Reaction: Ribulose5P => Ribose5P, Rate Law: cytoplasm*(VmPPIf*Ribulose5P/KmRibu5P-VmPPIr*Ribose5P/KmRibo5P)/(1+Ribulose5P/KmRibu5P+Ribose5P/KmRibo5P) |
KeqENO=6.7 dimensionless; KmENOP2G=0.04 mM; KmENOPEP=0.5 mM; VmENO=365.806 mMpermin |
Reaction: P2G => PEP, Rate Law: cytoplasm*VmENO/KmENOP2G*(P2G-PEP/KeqENO)/(1+P2G/KmENOP2G+PEP/KmENOPEP) |
KeqAK=0.45 dimensionless; KATPASE=39.5 permin; SUMAXP = 4.1 |
Reaction: P => X, Rate Law: cytoplasm*KATPASE*(((P-4*KeqAK*P)-SUMAXP)+(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(2-8*KeqAK) |
KmPGMP3G=1.2 mM; KeqPGM=0.19 dimensionless; VmPGM=2525.81 mMpermin; KmPGMP2G=0.08 mM |
Reaction: P3G => P2G, Rate Law: cytoplasm*VmPGM/KmPGMP3G*(P3G-P2G/KeqPGM)/(1+P3G/KmPGMP3G+P2G/KmPGMP2G) |
VmGAPDHr=6549.68 mMpermin; VmGAPDHf=1184.52 mMpermin; k_rel_GAPDH = 1.0 dimensionless; KeqTPI=0.045 dimensionless; KmGAPDHNAD=0.09 mM; KeqGAPDH=0.005 dimensionless; KmGAPDHBPG=0.0098 mM; KmGAPDHGAP=0.21 mM; KmGAPDHNADH=0.06 mM |
Reaction: GA3P + NAD => BPG + NADH, Rate Law: cytoplasm*k_rel_GAPDH*VmGAPDHf*GA3P*NAD/(KmGAPDHGAP*KmGAPDHNAD)*(1-BPG*NADH/(GA3P*NAD*KeqGAPDH))/((1+GA3P/KmGAPDHGAP+BPG/KmGAPDHBPG)*(1+NAD/KmGAPDHNAD+NADH/KmGAPDHNADH)) |
KmADHNAD=0.17 mM; KiADHETOH=90.0 mM; KiADHNADH=0.031 mM; KiADHACE=1.1 mM; KmADHETOH=17.0 mM; KeqADH=6.9E-5 dimensionless; KmADHNADH=0.11 mM; KiADHNAD=0.92 mM; VmADH=810.0 mMpermin; KmADHACE=1.11 mM |
Reaction: ACE + NADH => ETOH + NAD, Rate Law: cytoplasm*(-VmADH/(KiADHNAD*KmADHETOH)*(NAD*ETOH-NADH*ACE/KeqADH)/(1+NAD/KiADHNAD+KmADHNAD*ETOH/(KiADHNAD*KmADHETOH)+KmADHNADH*ACE/(KiADHNADH*KmADHACE)+NADH/KiADHNADH+NAD*ETOH/(KiADHNAD*KmADHETOH)+KmADHNADH*NAD*ACE/(KiADHNAD*KiADHNADH*KmADHACE)+KmADHNAD*ETOH*NADH/(KiADHNAD*KmADHETOH*KiADHNADH)+NADH*ACE/(KiADHNADH*KmADHACE)+NAD*ETOH*ACE/(KiADHNAD*KmADHETOH*KiADHACE)+ETOH*NADH*ACE/(KiADHETOH*KiADHNADH*KmADHACE))) |
Km6PGL=0.8 mM; Vm6PGL=4.0 mMpermin |
Reaction: D6PGluconoLactone => D6PGluconate, Rate Law: cytoplasm*Vm6PGL*D6PGluconoLactone/(Km6PGL+D6PGluconoLactone) |
KSUCC=21.4 permin |
Reaction: ACE + NAD => NADH + SUCC, Rate Law: cytoplasm*KSUCC*ACE |
kNADPH=2.0 permin |
Reaction: NADPH => NADP, Rate Law: cytoplasm*kNADPH*NADPH |
CPFKATP=3.0 dimensionless; CPFKF16BP=0.397 dimensionless; CPFKF26BP=0.0174 dimensionless; VmPFK=182.903 mMpermin; L0=0.66 dimensionless; SUMAXP = 4.1; KmPFKF6P=0.1 mM; KeqAK=0.45 dimensionless; KPFKF26BP=6.82E-4 mM; CPFKAMP=0.0845 dimensionless; KPFKAMP=0.0995 mM; CiPFKATP=100.0 dimensionless; KPFKF16BP=0.111 mM; KmPFKATP=0.71 mM; gR=5.12 dimensionless; KiPFKATP=0.65 mM |
Reaction: F6P + P => F16P; F26BP, Rate Law: cytoplasm*gR*VmPFK*F6P*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)*(1+F6P/KmPFKF6P+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP)+gR*F6P*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP*KmPFKF6P))/((2-8*KeqAK)*KmPFKATP*KmPFKF6P*(L0*(1+CPFKF26BP*F26BP/KPFKF26BP+CPFKF16BP*F16P/KPFKF16BP)^2*(1+2*CPFKAMP*KeqAK*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)^2/((-1+4*KeqAK)*KPFKAMP*(((SUMAXP-P)+4*KeqAK*P)-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)))^2*(1+CiPFKATP*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KiPFKATP))^2*(1+CPFKATP*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP))^2/((1+F26BP/KPFKF26BP+F16P/KPFKF16BP)^2*(1+2*KeqAK*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)^2/((-1+4*KeqAK)*KPFKAMP*(((SUMAXP-P)+4*KeqAK*P)-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)))^2*(1+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KiPFKATP))^2)+(1+F6P/KmPFKF6P+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP)+gR*F6P*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP*KmPFKF6P))^2)) |
VmTransaldf=55.0 mMpermin; KmF6P=0.32 mM; KmGA3P=0.22 mM; VmTransaldr=10.0 mMpermin; KmSeduhept=0.18 mM; KmEry4P=0.018 mM |
Reaction: Seduhept7P + GA3P => F6P + Erythrose4P, Rate Law: cytoplasm*(VmTransaldf*GA3P*Seduhept7P/(KmGA3P*KmSeduhept)-VmTransaldr*F6P*Erythrose4P/(KmF6P*KmEry4P))/((1+GA3P/KmGA3P+F6P/KmF6P)*(1+Seduhept7P/KmSeduhept+Erythrose4P/KmEry4P)) |
KmG3PDHGLY=1.0 mM; KeqTPI=0.045 dimensionless; KeqG3PDH=4300.0 dimensionless; KmG3PDHDHAP=0.4 mM; KmG3PDHNADH=0.023 mM; KmG3PDHNAD=0.93 mM; VmG3PDH=70.15 mMpermin |
Reaction: DHAP + NADH => GLY + NAD, Rate Law: cytoplasm*VmG3PDH*((-GLY*NAD/KeqG3PDH)+NADH*DHAP/(1+KeqTPI))/(KmG3PDHDHAP*KmG3PDHNADH*(1+NAD/KmG3PDHNAD+NADH/KmG3PDHNADH)*(1+GLY/KmG3PDHGLY+DHAP/((1+KeqTPI)*KmG3PDHDHAP))) |
VmGluDH=4.0 mMpermin; KmGluconate=0.02 mM; KmNADP=0.03 mM; KiNADPH=0.03 mM |
Reaction: D6PGluconate + NADP => Ribulose5P + NADPH; NADPH, Rate Law: cytoplasm*VmGluDH*D6PGluconate*NADP/(KmGluconate*KmNADP)/((1+D6PGluconate/KmGluconate+NADPH/KiNADPH)*(1+NADP/KmNADP)) |
VmGA3P=555.0 mMpermin; KmDHAP=1.23 mM; k_rel_TPI = 1.0 dimensionless; KmGA3P=1.27 mM; VmDHAP=10900.0 mMpermin |
Reaction: GA3P => DHAP, Rate Law: cytoplasm*k_rel_TPI*(VmDHAP*GA3P/KmGA3P-VmGA3P*DHAP/KmDHAP)/(1+GA3P/KmGA3P+DHAP/KmDHAP) |
KmXyl=1.5 mM; KmRibu5P=1.5 mM; VmR5PIr=1039.0 mMpermin; VmR5PIf=1039.0 mMpermin |
Reaction: Ribulose5P => Xyl5P, Rate Law: cytoplasm*(VmR5PIf*Ribulose5P/KmRibu5P-VmR5PIr*Xyl5P/KmXyl)/(1+Ribulose5P/KmRibu5P+Xyl5P/KmXyl) |
VmGLT=97.264 mMpermin; KeqGLT=1.0 mM; KmGLTGLCo=1.1918 mM; KmGLTGLCi=1.1918 mM |
Reaction: GLCo => GLCi, Rate Law: cytoplasm*VmGLT*(GLCo-GLCi/KeqGLT)/(KmGLTGLCo*(1+GLCo/KmGLTGLCo+GLCi/KmGLTGLCi+0.91*GLCo*GLCi/(KmGLTGLCi*KmGLTGLCo))) |
KeqAK=0.45 dimensionless; KmPGKBPG=0.003 mM; KeqPGK=3200.0 dimensionless; KmPGKADP=0.2 mM; KmPGKATP=0.3 mM; VmPGK=1306.45 mMpermin; KmPGKP3G=0.53 mM; SUMAXP = 4.1 |
Reaction: BPG => P3G + P, Rate Law: cytoplasm*VmPGK*(KeqPGK*BPG*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/(1-4*KeqAK)-((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)*P3G/(2-8*KeqAK))/(KmPGKATP*KmPGKP3G*(1+(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((1-4*KeqAK)*KmPGKADP)+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPGKATP))*(1+BPG/KmPGKBPG+P3G/KmPGKP3G)) |
VmPYK=1088.71 mMpermin; KeqAK=0.45 dimensionless; KmPYKADP=0.53 mM; KmPYKPEP=0.14 mM; KmPYKATP=1.5 mM; KeqPYK=6500.0 dimensionless; KmPYKPYR=21.0 mM; SUMAXP = 4.1 |
Reaction: PEP => PYR + P, Rate Law: cytoplasm*VmPYK/(KmPYKPEP*KmPYKADP)*(PEP*(SUMAXP-(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(1-4*KeqAK)-PYR*(((P-4*KeqAK*P)-SUMAXP)+(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(2-8*KeqAK)/KeqPYK)/((1+PEP/KmPYKPEP+PYR/KmPYKPYR)*(1+(((P-4*KeqAK*P)-SUMAXP)+(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(2-8*KeqAK)/KmPYKATP+(SUMAXP-(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(1-4*KeqAK)/KmPYKADP)) |
VmPDC=174.194 mMpermin; KmPDCPYR=4.33 mM; nPDC=1.9 dimensionless |
Reaction: PYR => ACE + CO2, Rate Law: cytoplasm*VmPDC*PYR^nPDC/KmPDCPYR^nPDC/(1+PYR^nPDC/KmPDCPYR^nPDC) |
KmPGIF6P=0.3 mM; KeqPGI=0.314 dimensionless; VmPGI=339.677 mMpermin; KmPGIG6P=1.4 mM |
Reaction: G6P => F6P, Rate Law: cytoplasm*VmPGI/KmPGIG6P*(G6P-F6P/KeqPGI)/(1+G6P/KmPGIG6P+F6P/KmPGIF6P) |
KmSeduhept=0.15 mM; KmXyl5P=0.15 mM; VmTransk1f=4.0 mMpermin; KmRibose5P=0.1 mM; KmGA3P=0.1 mM; VmTransk1r=2.0 mMpermin |
Reaction: Ribose5P + Xyl5P => GA3P + Seduhept7P, Rate Law: cytoplasm*(VmTransk1f*Ribose5P*Xyl5P/(KmRibose5P*KmXyl5P)-VmTransk1r*GA3P*Seduhept7P/(KmGA3P*KmSeduhept))/((1+Ribose5P/KmRibose5P+GA3P/KmGA3P)*(1+Xyl5P/KmXyl5P+Seduhept7P/KmSeduhept)) |
KeqAK=0.45 dimensionless; KmGLKADP=0.23 mM; KmGLKGLCi=0.08 mM; VmGLK=226.452 mMpermin; KmGLKG6P=30.0 mM; KeqGLK=3800.0 dimensionless; KmGLKATP=0.15 mM; SUMAXP = 4.1 |
Reaction: GLCi + P => G6P, Rate Law: cytoplasm*VmGLK*((-G6P*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((1-4*KeqAK)*KeqGLK))+GLCi*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/(2-8*KeqAK))/(KmGLKATP*KmGLKGLCi*(1+G6P/KmGLKG6P+GLCi/KmGLKGLCi)*(1+(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((1-4*KeqAK)*KmGLKADP)+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmGLKATP))) |
States:
Name | Description |
---|---|
Seduhept7P |
[sedoheptulose 7-phosphate] |
P |
[ADP; ATP; ADP; ADP; ATP] |
GLY |
[glycerol; Glycerol] |
DHAP |
[dihydroxyacetone phosphate] |
F16P |
[keto-D-fructose 1,6-bisphosphate; D-Fructose 1,6-bisphosphate] |
NADPH |
[NADPH] |
Xyl5P |
[D-xylulose 5-phosphate] |
GLCi |
[glucose; C00293] |
P2G |
[2-phospho-D-glyceric acid; 2-Phospho-D-glycerate] |
Ribulose5P |
[D-ribulose 5-phosphate] |
P3G |
[3-phospho-D-glyceric acid; 3-Phospho-D-glycerate] |
GLCo |
[glucose; C00293] |
Ribose5P |
[aldehydo-D-ribose 5-phosphate] |
NADH |
[NADH; NADH] |
PYR |
[pyruvate; Pyruvate] |
X |
X |
NADP |
[NADP(+)] |
Erythrose4P |
[D-erythrose 4-phosphate] |
GA3P |
[glyceraldehyde 3-phosphate] |
SUCC |
[succinate(2-)] |
BPG |
[3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate] |
F6P |
[keto-D-fructose 6-phosphate; beta-D-Fructose 6-phosphate] |
CO2 |
[carbon dioxide; CO2] |
G6P |
[alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate] |
D6PGluconoLactone |
[6-O-phosphono-D-glucono-1,5-lactone] |
D6PGluconate |
[6-phospho-D-gluconate] |
PEP |
[phosphoenolpyruvate; Phosphoenolpyruvate] |
NAD |
[NAD(+); NAD+] |
ETOH |
[ethanol; Ethanol] |
ACE |
[acetaldehyde; Acetaldehyde] |
Observables: none
MODEL8568434338
@ v0.0.1
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…
DetailsMycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing. link: http://identifiers.org/pubmed/16261191
Parameters: none
States: none
Observables: none
MODEL1909160002
@ v0.0.1
This is a mathematical model provides a platform for investigating the efficacy of dendritic cell vaccines during cancer…
DetailsTherapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist's experience. Clinical efficacy of dendritic cell (DC) vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles. In addition, the model considers a therapy given by bolus injections of small duration as opposed to a continual dose. Doses timing defines the therapeutic protocols, which in turn are improved to minimize the tumor mass by an optimal control algorithm. We intend to supplement therapist's experience and intuition in the protocol's implementation. Experimental results made on mice infected with melanoma with and without therapy agree with the model. It is shown that the dendritic cells' percentage that manages to reach the lymph nodes has a crucial impact on the therapy outcome. This suggests that efforts in finding better methods to deliver DC vaccines should be pursued. link: http://identifiers.org/pubmed/28912828
Parameters: none
States: none
Observables: none
MODEL1510230002
@ v0.0.1
Rantasalo2015-Synthetic_expresion_modulator_constitutiveSTF_B42 This model is part of a family of models describing a m…
DetailsThis work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320
Parameters: none
States: none
Observables: none
MODEL1510230001
@ v0.0.1
Rantasalo2015-Synthetic_expresion_modulator_constitutiveSTF_VP16 This model is part of a family of models describing a…
DetailsThis work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320
Parameters: none
States: none
Observables: none
MODEL1510230003
@ v0.0.1
Rantasalo2015-Synthetic_expresion_modulator_constitutiveSTF_VP16_pBID2-EDcorePromoter This model is part of a family of…
DetailsThis work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320
Parameters: none
States: none
Observables: none
MODEL1510230004
@ v0.0.1
Rantasalo2015-Synthetic_expresion_modulator_inducedSTF_B42 This model is part of a family of models describing a modula…
DetailsThis work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320
Parameters: none
States: none
Observables: none
MODEL1510230005
@ v0.0.1
Rantasalo2015-Synthetic_expresion_modulator_inducedSTF_VP16 This model is part of a family of models describing a modul…
DetailsThis work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320
Parameters: none
States: none
Observables: none
BIOMD0000000835
@ v0.0.1
This represents the reduced version of the "time course model" of Van Eunen et al (2013): Biochemical competition makes…
DetailsBACKGROUND: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. RESULTS: We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. CONCLUSIONS: The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment. link: http://identifiers.org/pubmed/24885656
Parameters:
Name | Description |
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Kmcpt2C10AcylCarMAT = 51.0; Keqcpt2 = 2.22; Kmcpt2C12AcylCarMAT = 51.0; Kmcpt2C16AcylCoAMAT = 38.0; Vcpt2 = 0.391; Kmcpt2C12AcylCoAMAT = 38.0; Kmcpt2C10AcylCoAMAT = 38.0; sfcpt2C12=0.95; Kmcpt2C16AcylCarMAT = 51.0; Kmcpt2C14AcylCoAMAT = 38.0; Kmcpt2C14AcylCarMAT = 51.0; Kmcpt2CoAMAT = 30.0; Kmcpt2C6AcylCoAMAT = 1000.0; Kmcpt2C4AcylCoAMAT = 1000000.0; Kmcpt2C8AcylCoAMAT = 38.0; Kmcpt2C8AcylCarMAT = 51.0; Kmcpt2C4AcylCarMAT = 51.0; Kmcpt2C6AcylCarMAT = 51.0; Kmcpt2CarMAT = 350.0 |
Reaction: C12AcylCarMAT => C12AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C12AcylCarMAT, C12AcylCoAMAT, Rate Law: VMAT*sfcpt2C12*Vcpt2*(C12AcylCarMAT*CoAMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT)-C12AcylCoAMAT*CarMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))/VMAT |
KmlcadC10EnoylCoAMAT = 1.08; KmlcadC14AcylCoAMAT = 7.4; Keqlcad = 6.0; KmlcadFADH = 24.2; sflcadC12=0.9; KmlcadC12AcylCoAMAT = 9.0; KmlcadFAD = 0.12; KmlcadC12EnoylCoAMAT = 1.08; KmlcadC10AcylCoAMAT = 24.3; KmlcadC16EnoylCoAMAT = 1.08; Vlcad = 0.01; KmlcadC16AcylCoAMAT = 2.5; KmlcadC8AcylCoAMAT = 123.0; KmlcadC8EnoylCoAMAT = 1.08; KmlcadC14EnoylCoAMAT = 1.08 |
Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C12AcylCoAMAT, FADHMAT, Rate Law: VMAT*sflcadC12*Vlcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC12AcylCoAMAT*KmlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmlcadC12AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C14EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))/VMAT |
Keqmckat = 1051.0; KmmckatC4AcylCoAMAT = 13.83; Vmckat = 0.377; KmmckatC8KetoacylCoAMAT = 3.2; KmmckatCoAMAT = 26.6; KmmckatC16KetoacylCoAMAT = 1.1; KmmckatC6KetoacylCoAMAT = 6.7; KmmckatC16AcylCoAMAT = 13.83; KmmckatC10AcylCoAMAT = 13.83; KmmckatC8AcylCoAMAT = 13.83; KmmckatC14KetoacylCoAMAT = 1.2; KmmckatC12KetoacylCoAMAT = 1.3; sfmckatC4=0.49; KmmckatAcetylCoAMAT = 30.0; KmmckatC12AcylCoAMAT = 13.83; KmmckatC6AcylCoAMAT = 13.83; KmmckatC10KetoacylCoAMAT = 2.1; KmmckatC4AcetoacylCoAMAT = 12.4; KmmckatC14AcylCoAMAT = 13.83 |
Reaction: C4AcetoacylCoAMAT => AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: VMAT*sfmckatC4*Vmckat*(C4AcetoacylCoAMAT*CoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT)-AcetylCoAMAT*AcetylCoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))/VMAT |
KmmtpC6AcylCoAMAT = 13.83; sfmtpC12=0.81; Keqmtp = 0.71; KmmtpC14EnoylCoAMAT = 25.0; KmmtpC10AcylCoAMAT = 13.83; KmmtpC12AcylCoAMAT = 13.83; KmmtpAcetylCoAMAT = 30.0; KmmtpC8AcylCoAMAT = 13.83; KmmtpC16EnoylCoAMAT = 25.0; KmmtpC14AcylCoAMAT = 13.83; KmmtpC10EnoylCoAMAT = 25.0; KicrotC4AcetoacylCoA = 1.6; KmmtpCoAMAT = 30.0; Vmtp = 2.84; KmmtpC12EnoylCoAMAT = 25.0; KmmtpNADMAT = 60.0; KmmtpC16AcylCoAMAT = 13.83; KmmtpC8EnoylCoAMAT = 25.0; KmmtpNADHMAT = 50.0 |
Reaction: C12EnoylCoAMAT => C10AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, AcetylCoAMAT, C10AcylCoAMAT, C12EnoylCoAMAT, NADHMAT, Rate Law: VMAT*sfmtpC12*Vmtp*(C12EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C10AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))/VMAT |
KmmcadC12EnoylCoAMAT = 1.08; KmmcadC8AcylCoAMAT = 4.0; KmmcadC6EnoylCoAMAT = 1.08; KmmcadC12AcylCoAMAT = 5.7; KmmcadC6AcylCoAMAT = 9.4; KmmcadC4AcylCoAMAT = 135.0; Vmcad = 0.081; Keqmcad = 6.0; KmmcadFADH = 24.2; KmmcadC10AcylCoAMAT = 5.4; KmmcadFAD = 0.12; KmmcadC10EnoylCoAMAT = 1.08; KmmcadC4EnoylCoAMAT = 1.08; sfmcadC10=0.8; KmmcadC8EnoylCoAMAT = 1.08 |
Reaction: C10AcylCoAMAT => C10EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10AcylCoAMAT, C10EnoylCoAMAT, FADHMAT, Rate Law: VMAT*sfmcadC10*Vmcad*(C10AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC10AcylCoAMAT*KmmcadFAD)-C10EnoylCoAMAT*FADHMAT/(KmmcadC10AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))/VMAT |
KmmtpC6AcylCoAMAT = 13.83; Keqmtp = 0.71; KmmtpC14EnoylCoAMAT = 25.0; KmmtpC10AcylCoAMAT = 13.83; sfmtpC8=0.34; KmmtpC12AcylCoAMAT = 13.83; KmmtpAcetylCoAMAT = 30.0; KmmtpC8AcylCoAMAT = 13.83; KmmtpC16EnoylCoAMAT = 25.0; KmmtpC14AcylCoAMAT = 13.83; KmmtpC10EnoylCoAMAT = 25.0; KicrotC4AcetoacylCoA = 1.6; KmmtpCoAMAT = 30.0; Vmtp = 2.84; KmmtpC12EnoylCoAMAT = 25.0; KmmtpNADMAT = 60.0; KmmtpC16AcylCoAMAT = 13.83; KmmtpC8EnoylCoAMAT = 25.0; KmmtpNADHMAT = 50.0 |
Reaction: C8EnoylCoAMAT => C6AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcetoacylCoAMAT, AcetylCoAMAT, C6AcylCoAMAT, C8EnoylCoAMAT, NADHMAT, Rate Law: VMAT*sfmtpC8*Vmtp*(C8EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C6AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))/VMAT |
Kmcpt2C10AcylCarMAT = 51.0; Keqcpt2 = 2.22; Kmcpt2C12AcylCarMAT = 51.0; Kmcpt2C16AcylCoAMAT = 38.0; Vcpt2 = 0.391; Kmcpt2C12AcylCoAMAT = 38.0; Kmcpt2C10AcylCoAMAT = 38.0; Kmcpt2C16AcylCarMAT = 51.0; Kmcpt2C14AcylCoAMAT = 38.0; Kmcpt2C14AcylCarMAT = 51.0; Kmcpt2CoAMAT = 30.0; Kmcpt2C6AcylCoAMAT = 1000.0; Kmcpt2C4AcylCoAMAT = 1000000.0; Kmcpt2C8AcylCoAMAT = 38.0; sfcpt2C8=0.35; Kmcpt2C8AcylCarMAT = 51.0; Kmcpt2C4AcylCarMAT = 51.0; Kmcpt2C6AcylCarMAT = 51.0; Kmcpt2CarMAT = 350.0 |
Reaction: C8AcylCarMAT => C8AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C8AcylCarMAT, C8AcylCoAMAT, Rate Law: VMAT*sfcpt2C8*Vcpt2*(C8AcylCarMAT*CoAMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT)-C8AcylCoAMAT*CarMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))/VMAT |
KmcactCarMAT = 130.0; KmcactCarCYT = 130.0; KicactC16AcylCarCYT=56.0; KmcactC16AcylCarMAT=15.0; KmcactC16AcylCarCYT=15.0; Vfcact = 0.42; Keqcact = 1.0; KicactCarCYT = 200.0; Vrcact = 0.42 |
Reaction: C16AcylCarCYT => C16AcylCarMAT; CarMAT, CarCYT, C16AcylCarCYT, C16AcylCarMAT, Rate Law: Vfcact*(C16AcylCarCYT*CarMAT-C16AcylCarMAT*CarCYT/Keqcact)/(C16AcylCarCYT*CarMAT+KmcactCarMAT*C16AcylCarCYT+KmcactC16AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C16AcylCarMAT*(1+C16AcylCarCYT/KicactC16AcylCarCYT)+CarCYT*(KmcactC16AcylCarMAT+C16AcylCarMAT))) |
Kmcpt2C10AcylCarMAT = 51.0; Keqcpt2 = 2.22; Kmcpt2C12AcylCarMAT = 51.0; Kmcpt2C16AcylCoAMAT = 38.0; Vcpt2 = 0.391; Kmcpt2C12AcylCoAMAT = 38.0; Kmcpt2C10AcylCoAMAT = 38.0; Kmcpt2C16AcylCarMAT = 51.0; Kmcpt2C14AcylCoAMAT = 38.0; Kmcpt2C14AcylCarMAT = 51.0; Kmcpt2CoAMAT = 30.0; Kmcpt2C6AcylCoAMAT = 1000.0; sfcpt2C16=0.85; Kmcpt2C4AcylCoAMAT = 1000000.0; Kmcpt2C8AcylCoAMAT = 38.0; Kmcpt2C8AcylCarMAT = 51.0; Kmcpt2C4AcylCarMAT = 51.0; Kmcpt2C6AcylCarMAT = 51.0; Kmcpt2CarMAT = 350.0 |
Reaction: C16AcylCarMAT => C16AcylCoAMAT; C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C16AcylCarMAT, C16AcylCoAMAT, Rate Law: VMAT*sfcpt2C16*Vcpt2* |