SBMLBioModels: S - S
S
MODEL1808150001
— v0.0.1Mathematical model of blood coagulation investigating the effects of varied rFVIIa and TF concentration.
Details
Recombinant factor VIIa (rFVIIa) is used for treatment of hemophilia patients with inhibitors, as well for off-label treatment of severe bleeding in trauma and surgery. Effective bleeding control requires supraphysiological doses of rFVIIa, posing both high expense and uncertain thrombotic risk. Two major competing theories offer different explanations for the supraphysiological rFVIIa dosing requirement: (1) the need to overcome competition between FVIIa and FVII zymogen for tissue factor (TF) binding, and (2) a high-dose-requiring phospholipid-related pathway of FVIIa action. In the present study, we found experimental conditions in which both mechanisms contribute simultaneously and independently to rFVIIa-driven thrombin generation in FVII-deficient human plasma. From mathematical simulations of our model of FX activation, which were confirmed by thrombin-generation experiments, we conclude that the action of rFVIIa at pharmacologic doses is dominated by the TF-dependent pathway with a minor contribution from a phospholipid-dependent mechanism. We established a dose-response curve for rFVIIa that is useful to explain dosing strategies. In the present study, we present a pathway to reconcile the 2 major mechanisms of rFVIIa action, a necessary step to understanding future dose optimization and evaluation of new rFVIIa analogs currently under development. link: http://identifiers.org/pubmed/22563088
MODEL2937159804
— v0.0.1Shimoni2009 - Escherichia Coli SOS Simple model, involving only the basic components of the circuit, sufficient to expl…
Details
BACKGROUND: DNA damage in Escherichia coli evokes a response mechanism called the SOS response. The genetic circuit of this mechanism includes the genes recA and lexA, which regulate each other via a mixed feedback loop involving transcriptional regulation and protein-protein interaction. Under normal conditions, recA is transcriptionally repressed by LexA, which also functions as an auto-repressor. In presence of DNA damage, RecA proteins recognize stalled replication forks and participate in the DNA repair process. Under these conditions, RecA marks LexA for fast degradation. Generally, such mixed feedback loops are known to exhibit either bi-stability or a single steady state. However, when the dynamics of the SOS system following DNA damage was recently studied in single cells, ordered peaks were observed in the promoter activity of both genes (Friedman et al., 2005, PLoS Biol. 3(7):e238). This surprising phenomenon was masked in previous studies of cell populations. Previous attempts to explain these results harnessed additional genes to the system and deployed complex deterministic mathematical models that were only partially successful in explaining the results. METHODOLOGY/PRINCIPAL FINDINGS: Here we apply stochastic methods, which are better suited for dynamic simulations of single cells. We show that a simple model, involving only the basic components of the circuit, is sufficient to explain the peaks in the promoter activities of recA and lexA. Notably, deterministic simulations of the same model do not produce peaks in the promoter activities. CONCLUSION/SIGNIFICANCE: We conclude that the double negative mixed feedback loop with auto-repression accounts for the experimentally observed peaks in the promoter activities. In addition to explaining the experimental results, this result shows that including additional regulations in a mixed feedback loop may dramatically change the dynamic functionality of this regulatory module. Furthermore, our results suggests that stochastic fluctuations strongly affect the qualitative behavior of important regulatory modules even under biologically relevant conditions, thus emphasizing the importance of stochastic analysis of regulatory circuits. link: http://identifiers.org/pubmed/19424504
BIOMD0000000832
— v0.0.1This is a mathematical model describing Hippo signalling pathway activity. It includes descriptions of crosstalk with th…
Details
The Hippo signalling pathway has recently emerged as an important regulator of cell apoptosis and proliferation with significant implications in human diseases. In mammals, the pathway contains the core kinases MST1/2, which phosphorylate and activate LATS1/2 kinases. The pro-apoptotic function of the MST/LATS signalling axis was previously linked to the Akt and ERK MAPK pathways, demonstrating that the Hippo pathway does not act alone but crosstalks with other signalling pathways to coordinate network dynamics and cellular outcomes. These crosstalks were characterised by a multitude of complex regulatory mechanisms involving competitive protein-protein interactions and phosphorylation mediated feedback loops. However, how these different mechanisms interplay in different cellular contexts to drive the context-specific network dynamics of Hippo-ERK signalling remains elusive. Using mathematical modelling and computational analysis, we uncovered that the Hippo-ERK network can generate highly diverse dynamical profiles that can be clustered into distinct dose-response patterns. For each pattern, we offered mechanistic explanation that defines when and how the observed phenomenon can arise. We demonstrated that Akt displays opposing, dose-dependent functions towards ERK, which are mediated by the balance between the Raf-1/MST2 protein interaction module and the LATS1 mediated feedback regulation. Moreover, Ras displays a multi-functional role and drives biphasic responses of both MST2 and ERK activities; which are critically governed by the competitive protein interaction between MST2 and Raf-1. Our study represents the first in-depth and systematic analysis of the Hippo-ERK network dynamics and provides a concrete foundation for future studies. link: http://identifiers.org/pubmed/27527217
Parameters:
Name | Description |
---|---|
Km_93 = 0.9015; kc_92 = 0.9203 | Reaction: iRaf1 => Raf1; RasGTP, Rate Law: compartment*kc_92*iRaf1*RasGTP/(Km_93+iRaf1) |
Km_122 = 297.2; V_121 = 1027.0 | Reaction: RasGTP => RasGDP, Rate Law: compartment*V_121*RasGTP/(Km_122+RasGTP) |
ka_41 = 0.4237; kd_41 = 1.226 | Reaction: aMST2 + RASSF1A => aMST2uRASSF1A, Rate Law: compartment*(ka_41*aMST2*RASSF1A-kd_41*aMST2uRASSF1A) |
kd_31 = 0.6117 | Reaction: dMST2 => aMST2, Rate Law: compartment*kd_31*dMST2 |
Km_91 = 0.8821; V_91 = 2.071 | Reaction: Raf1 => iRaf1, Rate Law: compartment*V_91*Raf1/(Km_91+Raf1) |
V_22 = 7511.0; Km_22 = 816.2 | Reaction: iMST2 => MST2, Rate Law: compartment*V_22*iMST2/(Km_22+iMST2) |
V_81 = 2261.0; Km_81 = 0.08503 | Reaction: aLATS1 => LATS1, Rate Law: compartment*V_81*aLATS1/(Km_81+aLATS1) |
Km_92 = 10.68; kc_91 = 0.1177 | Reaction: Raf1 => iRaf1; aLATS1, Rate Law: compartment*kc_91*aLATS1*Raf1/(Km_92+Raf1) |
aEGFR = 500.0; Km_11 = 51.21; kc_11 = 0.001149 | Reaction: Akt => pAkt, Rate Law: compartment*kc_11*aEGFR*Akt/(Km_11+Akt) |
Km_13 = 0.744; kc_12 = 0.717 | Reaction: Akt => pAkt; RasGTP, Rate Law: compartment*kc_12*Akt*RasGTP/(Km_13+Akt) |
Km_101 = 457.5; V_101 = 994.8 | Reaction: aRaf1 => Raf1, Rate Law: compartment*V_101*aRaf1/(Km_101+aRaf1) |
V_11 = 0.08687; Km_12 = 0.01497 | Reaction: pAkt => Akt, Rate Law: compartment*V_11*pAkt/(Km_12+pAkt) |
Km_21 = 427.3; V_21 = 1414.0 | Reaction: aMST2 => MST2, Rate Law: compartment*V_21*aMST2/(Km_21+aMST2) |
V_131 = 995.3; Km_132 = 151.0 | Reaction: ppERK => ERK, Rate Law: compartment*V_131*ppERK/(Km_132+ppERK) |
ka_22 = 0.0684; kd_21 = 0.113 | Reaction: MST2 + RASSF1A => MST2uRASSF1A, Rate Law: compartment*(ka_22*MST2*RASSF1A-kd_21*MST2uRASSF1A) |
Km_111 = 0.07678; V_111 = 254.7 | Reaction: ipRaf1 => aRaf1, Rate Law: compartment*V_111*ipRaf1/(Km_111+ipRaf1) |
ka_71 = 28.12; kd_71 = 4.886E-4 | Reaction: iMST2 + iRaf1 => iRaf1uiMST2, Rate Law: compartment*(ka_71*iMST2*iRaf1-kd_71*iRaf1uiMST2) |
V_102 = 317.3; Km_102 = 3.197 | Reaction: Raf1 => aRaf1, Rate Law: compartment*V_102*Raf1/(Km_102+Raf1) |
kc_112 = 0.002742; Km_112 = 207.1 | Reaction: aRaf1 => ipRaf1; ppERK, Rate Law: compartment*kc_112*aRaf1*ppERK/(Km_112+aRaf1) |
kc_131 = 5.342; Km_131 = 0.03676 | Reaction: ERK => ppERK; aRaf1, Rate Law: compartment*kc_131*aRaf1*ERK/(Km_131+ERK) |
kc_21 = 6684.0; Km_23 = 8.313E-4 | Reaction: MST2 => iMST2; pAkt, Rate Law: compartment*kc_21*MST2*pAkt/(Km_23+MST2) |
kc_82 = 2.93E-4; Km_83 = 22.26 | Reaction: LATS1 => aLATS1; aMST2uRASSF1A, Rate Law: compartment*kc_82*aMST2uRASSF1A*LATS1/(Km_83+LATS1) |
aEGFR = 500.0; kc_121 = 0.2061; Km_121 = 120.5 | Reaction: RasGDP => RasGTP, Rate Law: compartment*kc_121*aEGFR*RasGDP/(Km_121+RasGDP) |
ka_21 = 4472.0 | Reaction: MST2 => dMST2, Rate Law: compartment*ka_21*MST2^2 |
Km_51 = 6.708; V_51 = 5.688E-4 | Reaction: MST2uRASSF1A => aMST2uRASSF1A, Rate Law: compartment*V_51*MST2uRASSF1A/(Km_51+MST2uRASSF1A) |
kc_81 = 6189.0; Km_82 = 3961.0 | Reaction: LATS1 => aLATS1; aMST2, Rate Law: compartment*kc_81*aMST2*LATS1/(Km_82+LATS1) |
States:
Name | Description |
---|---|
MST2uRASSF1A | [Ras Association Domain-Containing Protein 1; STE20-Like Serine/Threonine-Protein Kinase] |
aMST2uRASSF1A | [STE20-Like Serine/Threonine-Protein Kinase; Ras Association Domain-Containing Protein 1] |
MST2 | [STE20-Like Serine/Threonine-Protein Kinase] |
aMST2 | [STE20-Like Serine/Threonine-Protein Kinase] |
Akt | [AKT kinase] |
iRaf1 | [RAF proto-oncogene serine/threonine-protein kinase] |
ipRaf1 | [RAF proto-oncogene serine/threonine-protein kinase] |
Raf1 | [RAF proto-oncogene serine/threonine-protein kinase] |
aLATS1 | [serine/threonine-protein kinase LATS1] |
LATS1 | [serine/threonine-protein kinase LATS1] |
RasGDP | [RAS Family Gene] |
ppERK | [Mitogen-activated protein kinase 3] |
pAkt | [AKT kinase] |
dMST2 | [STE20-Like Serine/Threonine-Protein Kinase] |
aRaf1 | [RAF proto-oncogene serine/threonine-protein kinase] |
RasGTP | [RAS Family Gene] |
RASSF1A | [Ras Association Domain-Containing Protein 1] |
iMST2 | [STE20-Like Serine/Threonine-Protein Kinase] |
ERK | [Mitogen-activated protein kinase 3] |
iRaf1uiMST2 | [RAF proto-oncogene serine/threonine-protein kinase; STE20-Like Serine/Threonine-Protein Kinase] |
MODEL2003200003
— v0.0.1A properly functioning immune system is vital for an organism's wellbeing. Immune tolerance is a critical feature of the…
Details
Dendritic cells are a promising immunotherapy tool for boosting an individual's antigen-specific immune response to cancer. We develop a mathematical model using differential and delay-differential equations to describe the interactions between dendritic cells, effector-immune cells, and tumor cells. We account for the trafficking of immune cells between lymph, blood, and tumor compartments. Our model reflects experimental results both for dendritic cell trafficking and for immune suppression of tumor growth in mice. In addition, in silico experiments suggest more effective immunotherapy treatment protocols can be achieved by modifying dose location and schedule. A sensitivity analysis of the model reveals which patient-specific parameters have the greatest impact on treatment efficacy. link: http://identifiers.org/pubmed/23516248
MODEL2003200002
— v0.0.1A properly functioning immune system is vital for an organism's wellbeing. Immune tolerance is a critical feature of the…
Details
A properly functioning immune system is vital for an organism's wellbeing. Immune tolerance is a critical feature of the immune system that allows immune cells to mount effective responses against exogenous pathogens such as viruses and bacteria, while preventing attack to self-tissues. Activation-induced cell death (AICD) in T lymphocytes, in which repeated stimulations of the T-cell receptor (TCR) lead to activation and then apoptosis of T cells, is a major mechanism for T cell homeostasis and helps maintain peripheral immune tolerance. Defects in AICD can lead to development of autoimmune diseases. Despite its importance, the regulatory mechanisms that underlie AICD remain poorly understood, particularly at an integrative network level. Here, we develop a dynamic multi-pathway model of the integrated TCR signalling network and perform model-based analysis to characterize the network-level properties of AICD. Model simulation and analysis show that amplified activation of the transcriptional factor NFAT in response to repeated TCR stimulations, a phenomenon central to AICD, is tightly modulated by a coupled positive-negative feedback mechanism. NFAT amplification is predominantly enabled by a positive feedback self-regulated by NFAT, while opposed by a NFAT-induced negative feedback via Carabin. Furthermore, model analysis predicts an optimal therapeutic window for drugs that help minimize proliferation while maximize AICD of T cells. Overall, our study provides a comprehensive mathematical model of TCR signalling and model-based analysis offers new network-level insights into the regulation of activation-induced cell death in T cells. link: http://identifiers.org/pubmed/31337782
BIOMD0000000826
— v0.0.1Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for…
Details
Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types. link: http://identifiers.org/pubmed/29920512
Parameters:
Name | Description |
---|---|
PF396 = 0.0; kc11 = 0.321366; STAT3tot = 144.212; Ki3b = 1.0; Km11 = 20.6063 | Reaction: => pSTAT3; STAT3uStattic, pPYK2, Rate Law: rootCompartment*kc11*pPYK2*rootCompartment/(1+PF396/Ki3b)*((STAT3tot-pSTAT3*rootCompartment)-STAT3uStattic*rootCompartment)/(Km11+((STAT3tot-pSTAT3*rootCompartment)-STAT3uStattic*rootCompartment))/rootCompartment |
Vmax24 = 4.39542E9; Km24 = 0.156675 | Reaction: pERK =>, Rate Law: rootCompartment*Vmax24*pERK*rootCompartment/(Km24+pERK*rootCompartment)/rootCompartment |
Km17 = 9.81748; HGF = 0.0; kc17 = 8.10961E-4; caHGF = 0.0090365 | Reaction: cMET => pcMET, Rate Law: rootCompartment*(kc17*HGF+caHGF)*cMET*rootCompartment/(Km17+cMET*rootCompartment)/rootCompartment |
Vmax22 = 0.034914; Km22 = 46.4515 | Reaction: aPTP =>, Rate Law: rootCompartment*Vmax22*aPTP*rootCompartment/(Km22+aPTP*rootCompartment)/rootCompartment |
Stattictot = 0.0; ka25 = 127.35; STAT3tot = 144.212; kd25 = 11.749 | Reaction: => STAT3uStattic; pSTAT3, Rate Law: rootCompartment*(ka25*((STAT3tot-pSTAT3*rootCompartment)-STAT3uStattic*rootCompartment)*(Stattictot-STAT3uStattic*rootCompartment)-kd25*STAT3uStattic*rootCompartment)/rootCompartment |
Vs13 = 0.0937562; Vmax13 = 0.354813; Km13 = 38.7258 | Reaction: => cMETm; pSTAT3, Rate Law: rootCompartment*(Vs13+Vmax13*pSTAT3*rootCompartment/(Km13+pSTAT3*rootCompartment))/rootCompartment |
kdeg6 = 53.5797 | Reaction: PYK2m =>, Rate Law: rootCompartment*kdeg6*PYK2m*rootCompartment/rootCompartment |
kdeg14 = 4.56037 | Reaction: cMETm =>, Rate Law: rootCompartment*kdeg14*cMETm*rootCompartment/rootCompartment |
kc10 = 0.00610942; Vmax10 = 0.530884; Km10 = 9.14113 | Reaction: pPYK2 => PYK2; aPTP, Rate Law: rootCompartment*(Vmax10+kc10*aPTP*rootCompartment)*pPYK2*rootCompartment/(Km10+pPYK2*rootCompartment)/rootCompartment |
EGF = 10.0; caEGF = 0.0891251; kc1 = 413.048; Km1 = 248.886; Gefitinib = 0.0; EGFRtot = 398.107; Ki1 = 1.0 | Reaction: => pEGFR; EGFRub, Rate Law: rootCompartment*kc1*(EGF/(1+Gefitinib/Ki1)+caEGF)*((EGFRtot-pEGFR*rootCompartment)-EGFRub*rootCompartment)/(Km1+((EGFRtot-pEGFR*rootCompartment)-EGFRub*rootCompartment))/rootCompartment |
Vs5 = 26.5461; Km5 = 4.74242; Vmax5 = 34.0408 | Reaction: => PYK2m; pSTAT3, Rate Law: rootCompartment*(Vs5+Vmax5*pSTAT3*rootCompartment/(Km5+pSTAT3*rootCompartment))/rootCompartment |
Vmax4 = 11.1173; Km4 = 90.7821 | Reaction: EGFRub =>, Rate Law: rootCompartment*Vmax4*EGFRub*rootCompartment/(Km4+EGFRub*rootCompartment)/rootCompartment |
Km20 = 24.322; Vmax20 = 0.0483059; kc20 = 35.6451 | Reaction: pCbl => ; aPTP, Rate Law: rootCompartment*(Vmax20+kc20*aPTP*rootCompartment)*pCbl*rootCompartment/(Km20+pCbl*rootCompartment)/rootCompartment |
Km7 = 3.33426; Vmax7 = 3.34965 | Reaction: => PYK2; PYK2m, Rate Law: rootCompartment*Vmax7*PYK2m*rootCompartment/(Km7+PYK2m*rootCompartment)/rootCompartment |
EMD = 0.0; Km9 = 34.914; kc9a = 0.463447; kc9b = 0.988553; Ki9 = 1.65577 | Reaction: PYK2 => pPYK2; pEGFR, pcMET, Rate Law: rootCompartment*(kc9a*pEGFR*rootCompartment+kc9b*pcMET*rootCompartment/(1+EMD/Ki9))*PYK2*rootCompartment/(Km9+PYK2*rootCompartment)/rootCompartment |
Km2 = 3.80189; Vmax2 = 112.202; kc2 = 1406.05 | Reaction: pEGFR => ; aPTP, Rate Law: rootCompartment*(Vmax2+kc2*aPTP*rootCompartment)*pEGFR*rootCompartment/(Km2+pEGFR*rootCompartment)/rootCompartment |
kc16 = 1.1749; Km16 = 528.445; kdeg16 = 24.4906 | Reaction: cMET => ; pCbl, Rate Law: rootCompartment*(kdeg16+kc16*pCbl*rootCompartment)*cMET*rootCompartment/(Km16+cMET*rootCompartment)/rootCompartment |
Km18 = 9.95405; Vmax18 = 0.0606736 | Reaction: pcMET => cMET, Rate Law: rootCompartment*Vmax18*pcMET*rootCompartment/(Km18+pcMET*rootCompartment)/rootCompartment |
kc19 = 52.723; Km19 = 13.3045; Cbltot = 174.985 | Reaction: => pCbl; pEGFR, Rate Law: rootCompartment*kc19*pEGFR*rootCompartment*(Cbltot-pCbl*rootCompartment)/(Km19+(Cbltot-pCbl*rootCompartment))/rootCompartment |
Km12 = 11.5878; kc12 = 2.89734E-4; Vmax12 = 7.63836 | Reaction: pSTAT3 => ; aPTP, Rate Law: rootCompartment*(Vmax12+kc12*aPTP*rootCompartment)*pSTAT3*rootCompartment/(Km12+pSTAT3*rootCompartment)/rootCompartment |
kdeg8 = 0.0566239 | Reaction: PYK2 =>, Rate Law: rootCompartment*kdeg8*PYK2*rootCompartment/rootCompartment |
EMD = 0.0; ERKtot = 166.725; kc23b = 8.43335E8; kc23a = 7.03072E9; Km23 = 2.83139; Ki23 = 13.4896 | Reaction: => pERK; pEGFR, pcMET, Rate Law: rootCompartment*(kc23a*pcMET*rootCompartment/(1+EMD/Ki23)+kc23b*pEGFR*rootCompartment)*(ERKtot-pERK*rootCompartment)/(Km23+(ERKtot-pERK*rootCompartment))/rootCompartment |
Vmax15 = 91.4113; Km15 = 6.45654 | Reaction: => cMET; cMETm, Rate Law: rootCompartment*Vmax15*cMETm*rootCompartment/(Km15+cMETm*rootCompartment)/rootCompartment |
kc3 = 10.7895; PF396 = 0.0; Ki3a = 0.0835603; Ki3b = 1.0; Vmax3 = 1.03753E-4; EGFRtot = 398.107; Km3 = 2.2856 | Reaction: => EGFRub; PYK2, pCbl, pEGFR, pPYK2, Rate Law: rootCompartment*(Vmax3+kc3*pCbl*rootCompartment)*((EGFRtot-pEGFR*rootCompartment)-EGFRub*rootCompartment)/(Km3+((EGFRtot-pEGFR*rootCompartment)-EGFRub*rootCompartment))*Ki3a/(Ki3a+(PYK2*rootCompartment+pPYK2*rootCompartment)/(1+PF396/Ki3b))/rootCompartment |
kc21 = 0.00397192; PTPtot = 296.483; Km21 = 52.723 | Reaction: => aPTP; pEGFR, Rate Law: rootCompartment*kc21*pEGFR*rootCompartment*(PTPtot-aPTP*rootCompartment)/(Km21+(PTPtot-aPTP*rootCompartment))/rootCompartment |
States:
Name | Description |
---|---|
pcMET | [PR:P08581] |
STAT3uStattic | [signal transducer and activator of transcription 3; stattic] |
pCbl | [E3 Ubiquitin-Protein Ligase CBL] |
pPYK2 | [Protein Tyrosine Kinase] |
aPTP | [Protein Tyrosine Phosphatase] |
pSTAT3 | [signal transducer and activator of transcription 3] |
cMET | [PR:P08581] |
PYK2m | [Protein Tyrosine Kinase; messenger RNA] |
PYK2 | [Protein Tyrosine Kinase] |
pERK | [mitogen-activated protein kinase] |
pEGFR | [epidermal growth factor receptor] |
cMETm | [PR:P08581; messenger RNA] |
EGFRub | [epidermal growth factor receptor; ubiquinated] |
MODEL1105100000
— v0.0.1Shlomi2011 - Warburg effect, metabolic modelUsing a genome-scale human metabolic network model accounting for stoichiome…
Details
The Warburg effect–a classical hallmark of cancer metabolism–is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids. link: http://identifiers.org/pubmed/21423717
MODEL0912160004
— v0.0.1This a model from the article: A mathematical model of fatigue in skeletal muscle force contraction. Shorten PR, O'C…
Details
The ability for muscle to repeatedly generate force is limited by fatigue. The cellular mechanisms behind muscle fatigue are complex and potentially include breakdown at many points along the excitation-contraction pathway. In this paper we construct a mathematical model of the skeletal muscle excitation-contraction pathway based on the cellular biochemical events that link excitation to contraction. The model includes descriptions of membrane voltage, calcium cycling and crossbridge dynamics and was parameterised and validated using the response characteristics of mouse skeletal muscle to a range of electrical stimuli. This model was used to uncover the complexities of skeletal muscle fatigue. We also parameterised our model to describe force kinetics in fast and slow twitch fibre types, which have a number of biochemical and biophysical differences. How these differences interact to generate different force/fatigue responses in fast- and slow- twitch fibres is not well understood and we used our modelling approach to bring new insights to this relationship. link: http://identifiers.org/pubmed/18080210
BIOMD0000000277
— v0.0.1This a model from the article: A mathematical model of parathyroid hormone response to acute changes in plasma ioniz…
Details
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis. link: http://identifiers.org/pubmed/20406649
Parameters:
Name | Description |
---|---|
alpha = 0.0569; t0 = 575.0; Ca0 = 1.22; Ca1 = 0.2624 | Reaction: Ca = piecewise(Ca0, time < t0, Ca0+Ca1*(1-exp((-alpha)*(time-t0)))), Rate Law: missing |
lambda_Ca = 170.0; k = 9.8436755; lambda_1 = 0.0125 | Reaction: x1 = (k-lambda_Ca*x1)-lambda_1*x1, Rate Law: (k-lambda_Ca*x1)-lambda_1*x1 |
lambda_Ca = 170.0; lambda_2 = 0.5595 | Reaction: x2 = lambda_Ca*x1-lambda_2*x2, Rate Law: lambda_Ca*x1-lambda_2*x2 |
States:
Name | Description |
---|---|
x1 | [Parathyroid hormone] |
x2 | [Parathyroid hormone] |
Ca | [calcium(2+); Calcium cation] |
BIOMD0000000276
— v0.0.1This a model from the article: A mathematical model of parathyroid hormone response to acute changes in plasma ioniz…
Details
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis. link: http://identifiers.org/pubmed/20406649
Parameters:
Name | Description |
---|---|
Ca1 = 0.1817; t0 = 575.0; Ca0 = 1.255; alpha = 0.0442 | Reaction: Ca = piecewise(Ca0, time < t0, Ca0-Ca1*(1-exp((-alpha)*(time-t0)))), Rate Law: missing |
lambda_Ca = 170.0; k = 9.8436755; lambda_1 = 0.0125 | Reaction: x1 = (k-lambda_Ca*x1)-lambda_1*x1, Rate Law: (k-lambda_Ca*x1)-lambda_1*x1 |
lambda_Ca = 170.0; lambda_2 = 0.5595 | Reaction: x2 = lambda_Ca*x1-lambda_2*x2, Rate Law: lambda_Ca*x1-lambda_2*x2 |
States:
Name | Description |
---|---|
x1 | [Parathyroid hormone] |
x2 | [Parathyroid hormone] |
Ca | [calcium(2+); Calcium cation] |
BIOMD0000000942
— v0.0.1Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigatin…
Details
Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems. link: http://identifiers.org/pubmed/17189866
Parameters:
Name | Description |
---|---|
KKa = 0.1; ka = 0.02 | Reaction: => Cdc25_phosphorylated; Cyclin_Cdk1_MPF, Cdc25_total, Rate Law: nuclear*ka*Cyclin_Cdk1_MPF*(Cdc25_total-Cdc25_phosphorylated)/((KKa+Cdc25_total)-Cdc25_phosphorylated) |
k2 = 0.25 | Reaction: Cyclin_Cdk1_MPF =>, Rate Law: nuclear*k2*Cyclin_Cdk1_MPF |
kh = 0.15; KKh = 0.01 | Reaction: IE_phosphorylated => ; ppase, Rate Law: nuclear*kh*ppase*IE_phosphorylated/(KKh+IE_phosphorylated) |
kd = 0.13; KKd = 1.0 | Reaction: APC_active => ; ppase, Rate Law: nuclear*kd*ppase*APC_active/(KKd+APC_active) |
KKc = 0.01; kc = 0.13 | Reaction: => APC_active; IE_phosphorylated, APC_total, Rate Law: nuclear*kc*IE_phosphorylated*(APC_total-APC_active)/((KKc+APC_total)-APC_active) |
kwee = 1.0 | Reaction: Cyclin_Cdk1_MPF => Cyclin_Cdk1_preMPF; Wee1, Rate Law: nuclear*kwee*Cyclin_Cdk1_MPF |
k3 = 0.005 | Reaction: Cyclin => Cyclin_Cdk1_MPF; Cdk1, Rate Law: nuclear*k3*Cdk1*Cyclin |
k1 = 1.0 | Reaction: => Cyclin, Rate Law: nuclear*k1 |
k25 = 0.017 | Reaction: Cyclin_Cdk1_preMPF => Cyclin_Cdk1_MPF; Cdc25_phosphorylated, Rate Law: nuclear*k25*Cyclin_Cdk1_preMPF |
kb = 0.1; KKb = 1.0 | Reaction: Cdc25_phosphorylated => ; ppase, Rate Law: nuclear*kb*ppase*Cdc25_phosphorylated/(KKb+Cdc25_phosphorylated) |
ke = 0.02; KKe = 0.1 | Reaction: => Wee1_phosphorylated; Cyclin_Cdk1_MPF, Wee1_total, Rate Law: nuclear*ke*Cyclin_Cdk1_MPF*(Wee1_total-Wee1_phosphorylated)/((KKe+Wee1_total)-Wee1_phosphorylated) |
KKf = 1.0; kf = 0.1 | Reaction: Wee1_phosphorylated => ; ppase, Rate Law: nuclear*kf*ppase*Wee1_phosphorylated/(KKf+Wee1_phosphorylated) |
KKg = 0.01; kg = 0.02 | Reaction: => IE_phosphorylated; Cyclin_Cdk1_MPF, IE_total, Rate Law: nuclear*kg*Cyclin_Cdk1_MPF*(IE_total-IE_phosphorylated)/((KKg+IE_total)-IE_phosphorylated) |
States:
Name | Description |
---|---|
Wee1 | [Wee1-like protein kinase 1-B] |
APC active | [Adenomatous polyposis coli homolog; active] |
Cyclin Cdk1 MPF | [G2/mitotic-specific cyclin-B1; Cyclin-dependent kinase 1-A] |
Wee1 phosphorylated | [Wee1-like protein kinase 1-B; phosphorylated] |
IE | IE |
Cdc25 | [M-phase inducer phosphatase 1-B] |
Cdc25 phosphorylated | [M-phase inducer phosphatase 1-B; phosphorylated] |
Cyclin | [G2/mitotic-specific cyclin-B1] |
Cyclin total | [G2/mitotic-specific cyclin-B1] |
IE phosphorylated | [phosphorylated] |
Cdk1 | [Cyclin-dependent kinase 1-A] |
Cyclin Cdk1 preMPF | [G2/mitotic-specific cyclin-B1; Cyclin-dependent kinase 1-A; phosphorylated] |
MODEL1006230119
— v0.0.1This a model from the article: Nonlinearities make a difference: comparison of two common Hill-type models with real m…
Details
Compared to complex structural Huxley-type models, Hill-type models phenomenologically describe muscle contraction using only few state variables. The Hill-type models dominate in the ever expanding field of musculoskeletal simulations for simplicity and low computational cost. Reasonable parameters are required to gain insight into mechanics of movement. The two most common Hill-type muscle models used contain three components. The series elastic component is connected in series to the contractile component. A parallel elastic component is either connected in parallel to both the contractile and the series elastic component (model [CC+SEC]), or is connected in parallel only with the contractile component (model [CC]). As soon as at least one of the components exhibits substantial nonlinearities, as, e.g., the contractile component by the ability to turn on and off, the two models are mechanically different. We tested which model ([CC+SEC] or [CC]) represents the cat soleus better. Ramp experiments consisting of an isometric and an isokinetic part were performed with an in situ cat soleus preparation using supramaximal nerve stimulation. Hill-type models containing force-length and force-velocity relationship, excitation-contraction coupling and series and parallel elastic force-elongation relations were fitted to the data. To test which model might represent the muscle better, the obtained parameters were compared with experimentally determined parameters. Determined in situations with negligible passive force, the force-velocity relation and the series elastic component relation are independent of the chosen model. In contrast to model [CC+SEC], these relations predicted by model [CC] were in accordance with experimental relations. In conclusion model [CC] seemed to better represent the cat soleus contraction dynamics and should be preferred in the nonlinear regression of muscle parameters and in musculoskeletal modeling. link: http://identifiers.org/pubmed/18049823
MODEL1006230120
— v0.0.1This a model from the article: Nonlinearities make a difference: comparison of two common Hill-type models with real m…
Details
Compared to complex structural Huxley-type models, Hill-type models phenomenologically describe muscle contraction using only few state variables. The Hill-type models dominate in the ever expanding field of musculoskeletal simulations for simplicity and low computational cost. Reasonable parameters are required to gain insight into mechanics of movement. The two most common Hill-type muscle models used contain three components. The series elastic component is connected in series to the contractile component. A parallel elastic component is either connected in parallel to both the contractile and the series elastic component (model [CC+SEC]), or is connected in parallel only with the contractile component (model [CC]). As soon as at least one of the components exhibits substantial nonlinearities, as, e.g., the contractile component by the ability to turn on and off, the two models are mechanically different. We tested which model ([CC+SEC] or [CC]) represents the cat soleus better. Ramp experiments consisting of an isometric and an isokinetic part were performed with an in situ cat soleus preparation using supramaximal nerve stimulation. Hill-type models containing force-length and force-velocity relationship, excitation-contraction coupling and series and parallel elastic force-elongation relations were fitted to the data. To test which model might represent the muscle better, the obtained parameters were compared with experimentally determined parameters. Determined in situations with negligible passive force, the force-velocity relation and the series elastic component relation are independent of the chosen model. In contrast to model [CC+SEC], these relations predicted by model [CC] were in accordance with experimental relations. In conclusion model [CC] seemed to better represent the cat soleus contraction dynamics and should be preferred in the nonlinear regression of muscle parameters and in musculoskeletal modeling. link: http://identifiers.org/pubmed/18049823
MODEL1711210002
— v0.0.1Using scaling from PhysB model Blood flow in L/hr Compartments in Kg Baseline as ~0.003nM Free E2 in Blood_venous E2 bi…
Details
Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In addition, endogenous (e.g. genetic variation) and external (e.g. exposure to estrogen-like chemicals) factors are known to impact estrogen biology. To understand how these multiple factors interact to determine an individual's response to therapy is complex, and may be best approached through a systems approach.We present a physiologically-based pharmacokinetic model (PBPK) of estradiol, and validate it against plasma kinetics in humans following intravenous and oral exposure. We extend this model by replacing the intrinsic clearance term with: a detailed kinetic model of estrogen metabolism in the liver; or, a genome-scale model of liver metabolism. Both models were validated by their ability to reproduce clinical data on estradiol exposure. We hypothesise that the enhanced mechanistic information contained within these models will lead to more robust predictions of the biological phenotype that emerges from the complex interactions between estrogens and the body.To demonstrate the utility of these models we examine the known drug-drug interactions between phenytoin and oral estradiol. We are able to reproduce the approximate 50% reduction in area under the concentration-time curve for estradiol associated with this interaction. Importantly, the inclusion of a genome-scale metabolic model allows the prediction of this interaction without directly specifying it within the model. In addition, we predict that PXR activation by drugs results in an enhanced ability of the liver to excrete glucose. This has important implications for the relationship between drug treatment and metabolic syndrome.We demonstrate how the novel coupling of PBPK models with genome-scale metabolic networks has the potential to aid prediction of drug action, including both drug-drug interactions and changes to the metabolic landscape that may predispose an individual to disease development. link: http://identifiers.org/pubmed/29246152
MODEL1711210003
— v0.0.1Physiologically-based Pharmacokinetic (PBPK) model of estradiol disposition in humans. Based on Sier et al_2017_estroge…
Details
Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In addition, endogenous (e.g. genetic variation) and external (e.g. exposure to estrogen-like chemicals) factors are known to impact estrogen biology. To understand how these multiple factors interact to determine an individual's response to therapy is complex, and may be best approached through a systems approach.We present a physiologically-based pharmacokinetic model (PBPK) of estradiol, and validate it against plasma kinetics in humans following intravenous and oral exposure. We extend this model by replacing the intrinsic clearance term with: a detailed kinetic model of estrogen metabolism in the liver; or, a genome-scale model of liver metabolism. Both models were validated by their ability to reproduce clinical data on estradiol exposure. We hypothesise that the enhanced mechanistic information contained within these models will lead to more robust predictions of the biological phenotype that emerges from the complex interactions between estrogens and the body.To demonstrate the utility of these models we examine the known drug-drug interactions between phenytoin and oral estradiol. We are able to reproduce the approximate 50% reduction in area under the concentration-time curve for estradiol associated with this interaction. Importantly, the inclusion of a genome-scale metabolic model allows the prediction of this interaction without directly specifying it within the model. In addition, we predict that PXR activation by drugs results in an enhanced ability of the liver to excrete glucose. This has important implications for the relationship between drug treatment and metabolic syndrome.We demonstrate how the novel coupling of PBPK models with genome-scale metabolic networks has the potential to aid prediction of drug action, including both drug-drug interactions and changes to the metabolic landscape that may predispose an individual to disease development. link: http://identifiers.org/pubmed/29246152
MODEL1911270001
— v0.0.1This is a mathematical model studies how specific immune system components, namely dendritic cells and cytotoxic T-cells…
Details
The cancer stem cell hypothesis states that tumors are heterogeneous and comprised of several different cell types that have a range of reproductive potentials. Cancer stem cells (CSCs), represent one class of cells that has both reproductive potential and the ability to differentiate. These cells are thought to drive the progression of aggressive and recurring cancers since they give rise to all other constituent cells within a tumor. With the development of immunotherapy in the last decade, the specific targeting of CSCs has become feasible and presents a novel therapeutic approach. In this paper, we construct a mathematical model to study how specific components of the immune system, namely dendritic cells and cytotoxic T-cells interact with different cancer cell types (CSCs and non-CSCs). Using a system of ordinary differential equations, we model the effects of immunotherapy, specifically dendritic cell vaccines and T-cell adoptive therapy, on tumor growth, with and without chemotherapy. The model reproduces several results observed in the literature, including temporal measurements of tumor size from in vivo experiments, and it is used to predict the optimal treatment schedule when combining different treatment modalities. Importantly, the model also demonstrates that chemotherapy increases tumorigenicity whereas CSC-targeted immunotherapy decreases it. link: http://identifiers.org/pubmed/31622595
MODEL1507180055
— v0.0.1Sigurdsson2010 - Genome-scale metabolic model of Mus Musculus (iMM1415)This model is described in the article: [A detai…
Details
BACKGROUND: Well-curated and validated network reconstructions are extremely valuable tools in systems biology. Detailed metabolic reconstructions of mammals have recently emerged, including human reconstructions. They raise the question if the various successful applications of microbial reconstructions can be replicated in complex organisms. RESULTS: We mapped the published, detailed reconstruction of human metabolism (Recon 1) to other mammals. By searching for genes homologous to Recon 1 genes within mammalian genomes, we were able to create draft metabolic reconstructions of five mammals, including the mouse. Each draft reconstruction was created in compartmentalized and non-compartmentalized version via two different approaches. Using gap-filling algorithms, we were able to produce all cellular components with three out of four versions of the mouse metabolic reconstruction. We finalized a functional model by iterative testing until it passed a predefined set of 260 validation tests. The reconstruction is the largest, most comprehensive mouse reconstruction to-date, accounting for 1,415 genes coding for 2,212 gene-associated reactions and 1,514 non-gene-associated reactions.We tested the mouse model for phenotype prediction capabilities. The majority of predicted essential genes were also essential in vivo. However, our non-tissue specific model was unable to predict gene essentiality for many of the metabolic genes shown to be essential in vivo. Our knockout simulation of the lipoprotein lipase gene correlated well with experimental results, suggesting that softer phenotypes can also be simulated. CONCLUSIONS: We have created a high-quality mouse genome-scale metabolic reconstruction, iMM1415 (Mus Musculus, 1415 genes). We demonstrate that the mouse model can be used to perform phenotype simulations, similar to models of microbe metabolism. Since the mouse is an important experimental organism, this model should become an essential tool for studying metabolic phenotypes in mice, including outcomes from drug screening. link: http://identifiers.org/pubmed/20959003
MODEL1112110004
— v0.0.1This a model from the article: An integrated model for glucose and insulin regulation in healthy volunteers and type 2…
Details
An integrated model for the regulation of glucose and insulin concentrations following intravenous glucose provocations in healthy volunteers and type 2 diabetic patients was developed. Data from 72 individuals were included. Total glucose, labeled glucose, and insulin concentrations were determined. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Integrated models for glucose, labeled glucose, and insulin were developed. Control mechanisms for regulation of glucose production, insulin secretion, and glucose uptake were incorporated. Physiologically relevant differences between healthy volunteers and patients were identified in the regulation of glucose production, elimination rate of glucose, and secretion of insulin. The model was able to describe the insulin and glucose profiles well and also showed a good ability to simulate data. The features of the present model are likely to be of interest for analysis of data collected in antidiabetic drug development and for optimization of study design. link: http://identifiers.org/pubmed/17766701
BIOMD0000000870
— v0.0.1This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with mass action kinetic…
Details
Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose–response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis–Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context. link: http://identifiers.org/doi/10.1073/pnas.1816000116
Parameters:
Name | Description |
---|---|
k1=0.05 | Reaction: p100_NIK => p52 + NIK, Rate Law: compartment*k1*p100_NIK |
k1=1.6E-5; k2=2.4E-4 | Reaction: p100 => IkBd, Rate Law: compartment*(k1*p100^2-k2*IkBd) |
k1=3.8E-4 | Reaction: p52 =>, Rate Law: compartment*k1*p52 |
k1=0.005; k2=2.4E-4 | Reaction: p100 + NIK => p100_NIK, Rate Law: compartment*(k1*p100*NIK-k2*p100_NIK) |
k1=0.2 | Reaction: p100t => p100, Rate Law: compartment*k1*p100t |
States:
Name | Description |
---|---|
IkBd | [Nuclear factor NF-kappa-B p100 subunit] |
p100t | [ENSG00000077150] |
p52 | [Nuclear factor NF-kappa-B p100 subunit] |
NIK | [Mitogen-activated protein kinase kinase kinase 14] |
IkBd NIK | [Mitogen-activated protein kinase kinase kinase 14; Nuclear factor NF-kappa-B p100 subunit] |
p100 NIK | [Mitogen-activated protein kinase kinase kinase 14; Nuclear factor NF-kappa-B p100 subunit] |
p100 | [Nuclear factor NF-kappa-B p100 subunit] |
BIOMD0000000869
— v0.0.1This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with Michaelis-Menten ki…
Details
Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose–response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis–Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context. link: http://identifiers.org/doi/10.1073/pnas.1816000116
Parameters:
Name | Description |
---|---|
k1=1.6E-5; k2=2.4E-4 | Reaction: p100 => IkBd, Rate Law: compartment*(k1*p100^2-k2*IkBd) |
Km=10.0; kcat=0.05 | Reaction: IkBd => ; NIK, Rate Law: compartment*NIK*kcat*IkBd/(Km+IkBd) |
k1=3.8E-4 | Reaction: p100 =>, Rate Law: compartment*k1*p100 |
k1=0.2 | Reaction: p100t => p100, Rate Law: compartment*k1*p100t |
States:
Name | Description |
---|---|
IkBd | [Nuclear factor NF-kappa-B p100 subunit] |
p100t | [ENSG00000077150] |
p52 | [Nuclear factor NF-kappa-B p100 subunit] |
p100 | [Nuclear factor NF-kappa-B p100 subunit] |
BIOMD0000000868
— v0.0.1This model represents NIK-dependent p100 processing into p52 with mass action kinetics. While this model shows identical…
Details
Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose–response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis–Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context. link: http://identifiers.org/doi/10.1073/pnas.1816000116
Parameters:
Name | Description |
---|---|
k1=0.05 | Reaction: p100_NIK => p52 + NIK, Rate Law: compartment*k1*p100_NIK |
k1=0.005; k2=2.4E-4 | Reaction: p100 + NIK => p100_NIK, Rate Law: compartment*(k1*p100*NIK-k2*p100_NIK) |
k1=3.8E-4 | Reaction: p100 =>, Rate Law: compartment*k1*p100 |
k1=0.2 | Reaction: p100t => p100, Rate Law: compartment*k1*p100t |
States:
Name | Description |
---|---|
p100 NIK | [Mitogen-activated protein kinase kinase kinase 14; Nuclear factor NF-kappa-B p100 subunit] |
NIK | [Mitogen-activated protein kinase kinase kinase 14] |
p52 | [Nuclear factor NF-kappa-B p100 subunit] |
p100t | [ENSG00000077150] |
p100 | [Nuclear factor NF-kappa-B p100 subunit] |
BIOMD0000000866
— v0.0.1This model represents NIK-dependent p100 processing into p52 with Michaelis-Menten kinetics. While this model shows iden…
Details
Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose–response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis–Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context. link: http://identifiers.org/doi/10.1073/pnas.1816000116
Parameters:
Name | Description |
---|---|
k1=3.8E-4 | Reaction: p100 =>, Rate Law: compartment*k1*p100 |
Km=10.0; kcat=0.05 | Reaction: p100 => p52; NIK, Rate Law: compartment*NIK*kcat*p100/(Km+p100) |
k1=0.2 | Reaction: p100t => p100, Rate Law: compartment*k1*p100t |
States:
Name | Description |
---|---|
p100t | [ENSG00000077150] |
p52 | [Nuclear factor NF-kappa-B p100 subunit] |
p100 | [Nuclear factor NF-kappa-B p100 subunit] |
BIOMD0000000151
— v0.0.1Cytokines like interleukin-6 (IL-6) play an important role in triggering the acute phase response of the body to injury…
Details
The model reproduces Fig 2, Fig3A and Fig 3B of the paper. The ODE for x1(gp180) and x3 (gp 130) is wrong and the authors have communicated to the curator that the species ought to have a constant value. There are a few other differences from the paper and these were made in consultation with the authors. Model was successfully tested on MathSBML
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Parameters:
Name | Description |
---|---|
kf13 = 2.0E-7; kr13 = 0.2 | Reaction: x10 + x9 => x14, Rate Law: cytosol*(kf13*x9*x10-kr13*x14) |
k49 = 0.058 | Reaction: x58 => x59 + x55, Rate Law: cytosol*k49*x58 |
kr46 = 0.001833; kf46 = 0.011 | Reaction: x55 + x51 => x56, Rate Law: cytosol*(kf46*x55*x51-kr46*x56) |
kr44 = 0.001833; kf44 = 0.011 | Reaction: x53 + x51 => x54, Rate Law: cytosol*(kf44*x51*x53-kr44*x54) |
kf39 = 0.3; kr39 = 9.0E-4 | Reaction: x40 => x45 + x8, Rate Law: cytosol*(kf39*x40-kr39*x45*x8) |
k23 = 5.0E-4 | Reaction: x32 => x33 + x29, Rate Law: cytosol*k23*x32 |
kr11 = 0.2; kf11 = 0.001 | Reaction: x17 + x10 => x18, Rate Law: cytosol*(kf11*x10*x17-kr11*x18) |
k6 = 0.4 | Reaction: x11 => x10 + x8, Rate Law: cytosol*k6*x11 |
kr29 = 7.0E-4; kf29 = 1.0 | Reaction: x48 => x51 + x49, Rate Law: cytosol*(kf29*x48-kr29*x49*x51) |
kf34 = 6.0; kr34 = 0.06 | Reaction: x16 => x39, Rate Law: cytosol*(kf34*x16-kr34*x39) |
kr42 = 0.2; kf42 = 0.0717 | Reaction: x51 + x50 => x52, Rate Law: cytosol*(kf42*x50*x51-kr42*x52) |
kf25 = 0.01; kr25 = 0.0214 | Reaction: x40 + x35 => x41, Rate Law: cytosol*(kf25*x35*x40-kr25*x41) |
kr38 = 0.55; kf38 = 0.01 | Reaction: x46 + x34 => x45, Rate Law: cytosol*(kf38*x34*x46-kr38*x45) |
k43 = 1.0 | Reaction: x52 => x47 + x50, Rate Law: cytosol*k43*x52 |
kf40 = 0.03; kr40 = 0.064 | Reaction: x45 + x35 => x44, Rate Law: cytosol*(kf40*x35*x45-kr40*x44) |
kf2 = 0.02; kr2 = 0.02 | Reaction: x6 => x5 + x2, Rate Law: cytosol*(kr2*x6-kf2*x2*x5) |
kr52 = 0.033; kf52 = 1.1E-4 | Reaction: x61 + x57 => x62, Rate Law: cytosol*(kf52*x57*x61-kr52*x62) |
kf56 = 0.014; kr56 = 0.6 | Reaction: x66 + x65 => x67, Rate Law: cytosol*(kf56*x65*x66-kr56*x67) |
k53 = 16.0 | Reaction: x62 => x63 + x57, Rate Law: cytosol*k53*x62 |
Km = 340.0; Vm = 1.7 | Reaction: x46 => x15, Rate Law: cytosol*Vm*x46/(Km+x46) |
kf50 = 2.5E-4; kr50 = 0.5 | Reaction: x59 + x55 => x60, Rate Law: cytosol*(kf50*x55*x59-kr50*x60) |
kr24 = 0.55; kf24 = 0.01 | Reaction: x39 + x34 => x40, Rate Law: cytosol*(kf24*x39*x34-kr24*x40) |
k51 = 0.058 | Reaction: x60 => x59 + x53, Rate Law: cytosol*k51*x60 |
kf3 = 0.04 | Reaction: x6 => x7, Rate Law: cytosol*kf3*x6^2 |
k12 = 0.003 | Reaction: x18 => x17 + x9, Rate Law: cytosol*k12*x18 |
kr8 = 0.1; kf8 = 0.02 | Reaction: x10 => x13, Rate Law: cytosol*(2*kf8*x10^2-2*kr8*x13) |
k45 = 3.5 | Reaction: x54 => x55 + x51, Rate Law: cytosol*k45*x54 |
kr26 = 1.3; kf26 = 0.015 | Reaction: x41 + x36 => x42, Rate Law: cytosol*(kf26*x36*x41-kr26*x42) |
kf48 = 0.0143; kr48 = 0.8 | Reaction: x59 + x57 => x58, Rate Law: cytosol*(kf48*x57*x59-kr48*x58) |
kf54 = 1.1E-4; kr54 = 0.033 | Reaction: x64 => x63 + x57, Rate Law: cytosol*(kr54*x64-kf54*x57*x63) |
k14 = 0.005 | Reaction: x13 => x20, Rate Law: cytosol*k14*x13 |
k20 = 0.01 | Reaction: => x29; x26, Rate Law: nucleus*k20*x26 |
kf32 = 0.1; kr32 = 2.45E-4 | Reaction: x41 => x44 + x8, Rate Law: cytosol*(kf32*x41-kr32*x44*x8) |
kr1 = 0.05; kf1 = 0.1 | Reaction: x4 => x5; x3, Rate Law: cytosol*(kf1*x3*x4-kr1*x5) |
kf37 = 0.3; kr37 = 9.0E-4 | Reaction: x39 => x46 + x8, Rate Law: cytosol*(kf37*x39-kr37*x8*x46) |
kf58 = 0.005; kr58 = 0.5 | Reaction: x66 + x63 => x68, Rate Law: cytosol*(kf58*x63*x66-kr58*x68) |
kr3 = 0.2 | Reaction: x7 => x6, Rate Law: cytosol*kr3*x7 |
k4 = 0.005 | Reaction: x7 => x8, Rate Law: cytosol*k4*x7 |
k10 = 0.003 | Reaction: x32 => x29 + x15 + x9 + x7, Rate Law: cytosol*k10*x32 |
kf7 = 0.005; kr7 = 0.5 | Reaction: x10 + x8 => x12, Rate Law: cytosol*(kf7*x8*x10-kr7*x12) |
kf21 = 0.02; kr21 = 0.1 | Reaction: x29 + x8 => x30, Rate Law: cytosol*(kf21*x29*x8-kr21*x30) |
kf9 = 0.001; kr9 = 0.2 | Reaction: x15 + x8 => x16, Rate Law: cytosol*(kf9*x8*x15-kr9*x16) |
kr5 = 0.8; kf5 = 0.008 | Reaction: x30 + x9 => x31, Rate Law: cytosol*(kf5*x9*x30-kr5*x31) |
k17 = 0.05 | Reaction: x22 => x9, Rate Law: nucleus*k17*x22 |
k47 = 2.9 | Reaction: x56 => x51 + x57, Rate Law: cytosol*k47*x56 |
k55 = 6.7 | Reaction: x64 => x65 + x57, Rate Law: cytosol*k55*x64 |
kf15 = 0.001; kr15 = 0.2 | Reaction: x23 + x20 => x27, Rate Law: nucleus*(kf15*x23*x20-kr15*x27) |
kr33 = 0.021; kf33 = 0.3 | Reaction: x44 => x46 + x38, Rate Law: cytosol*(kf33*x44-kr33*x38*x46) |
k59 = 0.3 | Reaction: x68 => x66 + x61, Rate Law: cytosol*k59*x68 |
States:
Name | Description |
---|---|
x16 | [Tyrosine-protein phosphatase non-receptor type 11; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x54 | [Mitogen activated protein kinase kinase 1Putative uncharacterized protein; RAF proto-oncogene serine/threonine-protein kinase] |
x32 | [Suppressor of cytokine signaling 3; Signal transducer and activator of transcription 3; Tyrosine-protein phosphatase non-receptor type 11; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x60 | [Mitogen activated protein kinase kinase 1Putative uncharacterized protein] |
x62 | [Mitogen-activated protein kinase 1; Mitogen activated protein kinase kinase 1Putative uncharacterized protein] |
x38 | [Son of sevenless homolog 1; Growth factor receptor-bound protein 2] |
x45 | [Growth factor receptor-bound protein 2; Tyrosine-protein phosphatase non-receptor type 11] |
x23 | PP2 |
x66 | Phosp3 |
x51 | [RAF proto-oncogene serine/threonine-protein kinase] |
x46 | [Tyrosine-protein phosphatase non-receptor type 11] |
x11 | [Signal transducer and activator of transcription 3; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x29 | [Suppressor of cytokine signaling 3] |
x53 | [Mitogen activated protein kinase kinase 1Putative uncharacterized protein] |
x59 | Phosp2 |
x12 | [Signal transducer and activator of transcription 3; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x5 | [Interleukin 6 signal transducer; Tyrosine-protein kinase JAK1] |
x40 | [Growth factor receptor-bound protein 2; Tyrosine-protein phosphatase non-receptor type 11; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x19 | [Signal transducer and activator of transcription 3] |
x63 | [Mitogen-activated protein kinase 1] |
x57 | [Mitogen activated protein kinase kinase 1Putative uncharacterized protein] |
x55 | [Mitogen activated protein kinase kinase 1Putative uncharacterized protein] |
x20 | [Signal transducer and activator of transcription 3] |
x41 | [Growth factor receptor-bound protein 2; Tyrosine-protein phosphatase non-receptor type 11; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x39 | [Tyrosine-protein phosphatase non-receptor type 11; Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x50 | Phosp1 |
x61 | [Mitogen-activated protein kinase 1] |
x9 | [Signal transducer and activator of transcription 3] |
x15 | [Tyrosine-protein phosphatase non-receptor type 11] |
x8 | [Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x7 | [Tyrosine-protein kinase JAK1; Interleukin 6 signal transducer; Interleukin-6; Interleukin-6 receptor subunit alpha] |
x13 | [Signal transducer and activator of transcription 3] |
x52 | [RAF proto-oncogene serine/threonine-protein kinase] |
x21 | [Signal transducer and activator of transcription 3] |
x10 | [Signal transducer and activator of transcription 3] |
x64 | [Mitogen-activated protein kinase 1; Mitogen activated protein kinase kinase 1Putative uncharacterized protein] |
BIOMD0000000221
— v0.0.1This a model from the article: Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tub…
Details
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment. link: http://identifiers.org/pubmed/16887020
Parameters:
Name | Description |
---|---|
Kgly_ms=2.0 mmol*l^(-1); Vr_ms=0.285 mmol*l^(-1)*(60*s)^(-1); Kmal_ms=1.0 mmol*l^(-1); Kaca_ms=0.01 mmol*l^(-1); Vf_ms=28.5 mmol*l^(-1)*(60*s)^(-1); Kcoa_ms=0.1 mmol*l^(-1) | Reaction: gly + aca => mal + coa, Rate Law: cell*(Vf_ms*gly/Kgly_ms*aca/Kaca_ms-Vr_ms*mal/Kmal_ms*coa/Kcoa_ms)/((1+gly/Kgly_ms+mal/Kmal_ms)*(1+aca/Kaca_ms+coa/Kcoa_ms)) |
Vr_icl=0.285 mmol*l^(-1)*(60*s)^(-1); Vf_icl=28.5 mmol*l^(-1)*(60*s)^(-1); Kicit_icl=0.604 mmol*l^(-1); Kgly_icl=0.13 mmol*l^(-1); Ksuc_icl=0.59 mmol*l^(-1) | Reaction: icit => suc + gly, Rate Law: cell*(Vf_icl*icit/Kicit_icl-Vr_icl*suc/Ksuc_icl*gly/Kgly_icl)/(1+icit/Kicit_icl+suc/Ksuc_icl+gly/Kgly_icl+icit/Kicit_icl*suc/Ksuc_icl+suc/Ksuc_icl*gly/Kgly_icl) |
Vr_fum=144.67 mmol*l^(-1)*(60*s)^(-1); Kmal_fum=0.04 mmol*l^(-1); Vf_fum=156.24 mmol*l^(-1)*(60*s)^(-1); Kfa_fum=0.15 mmol*l^(-1) | Reaction: fa => mal, Rate Law: cell*(Vf_fum*fa/Kfa_fum-Vr_fum*mal/Kmal_fum)/(1+fa/Kfa_fum+mal/Kmal_fum) |
Vf_scas=8.96 mmol*l^(-1)*(60*s)^(-1); Ksca_scas=0.02 mmol*l^(-1); Ksuc_scas=5.0 mmol*l^(-1); Vr_scas=0.0896 mmol*l^(-1)*(60*s)^(-1) | Reaction: sca => suc, Rate Law: cell*(Vf_scas*sca/Ksca_scas-Vr_scas*suc/Ksuc_scas)/(1+sca/Ksca_scas+suc/Ksuc_scas) |
Kaca_cs=0.03 mmol*l^(-1); Kcit_cs=0.7 mmol*l^(-1); Vf_cs=446.88 mmol*l^(-1)*(60*s)^(-1); Koaa_cs=0.07 mmol*l^(-1); Vr_cs=4.4688 mmol*l^(-1)*(60*s)^(-1); Kcoa_cs=0.3 mmol*l^(-1) | Reaction: aca + oaa => coa + cit, Rate Law: cell*(Vf_cs*aca/Kaca_cs*oaa/Koaa_cs-Vr_cs*coa/Kcoa_cs*cit/Kcit_cs)/((1+aca/Kaca_cs+coa/Kcoa_cs)*(1+oaa/Koaa_cs+cit/Kcit_cs)) |
Vr_acn=6.2928 mmol*l^(-1)*(60*s)^(-1); Kcit_acn=1.7 mmol*l^(-1); Kicit_acn=3.33 mmol*l^(-1); Vf_acn=629.28 mmol*l^(-1)*(60*s)^(-1) | Reaction: cit => icit, Rate Law: cell*(Vf_acn*cit/Kcit_acn-Vr_acn*icit/Kicit_acn)/(1+cit/Kcit_acn+icit/Kicit_acn) |
Ksca_kdh=1.0 mmol*l^(-1); Kakg_kdh=0.1 mmol*l^(-1); Vr_kdh=0.57344 mmol*l^(-1)*(60*s)^(-1); Vf_kdh=57.344 mmol*l^(-1)*(60*s)^(-1) | Reaction: akg => sca, Rate Law: cell*(Vf_kdh*akg/Kakg_kdh-Vr_kdh*sca/Ksca_kdh)/(1+akg/Kakg_kdh+sca/Ksca_kdh) |
Vf_icd=6.625 mmol*l^(-1)*(60*s)^(-1); Vr_icd=0.06625 mmol*l^(-1)*(60*s)^(-1); Kakg_icd=0.13 mmol*l^(-1); Kicit_icd=0.008 mmol*l^(-1) | Reaction: icit => akg, Rate Law: cell*(Vf_icd*icit/Kicit_icd-Vr_icd*akg/Kakg_icd)/(1+icit/Kicit_icd+akg/Kakg_icd) |
Vf_mdh=1390.9 mmol*l^(-1)*(60*s)^(-1); Koaa_mdh=0.04 mmol*l^(-1); Kmal_mdh=2.6 mmol*l^(-1); Vr_mdh=1276.06 mmol*l^(-1)*(60*s)^(-1) | Reaction: mal => oaa, Rate Law: cell*(Vf_mdh*mal/Kmal_mdh-Vr_mdh*oaa/Koaa_mdh)/(1+mal/Kmal_mdh+oaa/Koaa_mdh) |
Vr_sdh=16.24 mmol*l^(-1)*(60*s)^(-1); Vf_sdh=17.7 mmol*l^(-1)*(60*s)^(-1); Ksuc_sdh=0.02 mmol*l^(-1); Kfa_sdh=0.4 mmol*l^(-1) | Reaction: suc => fa, Rate Law: cell*(Vf_sdh*suc/Ksuc_sdh-Vr_sdh*fa/Kfa_sdh)/(1+suc/Ksuc_sdh+fa/Kfa_sdh) |
States:
Name | Description |
---|---|
gly | [glyoxylic acid; Glyoxylate] |
icit | [isocitric acid; Isocitrate] |
coa | [coenzyme A; CoA] |
mal | [malic acid; Malate] |
akg | [2-oxoglutaric acid; 2-Oxoglutarate] |
aca | [acetyl-CoA; Acetyl-CoA] |
cit | [citric acid; Citrate] |
oaa | [oxaloacetic acid; Oxaloacetate] |
biosyn | biosyn |
fa | [fumaric acid; Fumarate] |
suc | [succinic acid; Succinate] |
sca | [succinyl-CoA; Succinyl-CoA] |
BIOMD0000000222
— v0.0.1This a model from the article: Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tub…
Details
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment. link: http://identifiers.org/pubmed/16887020
Parameters:
Name | Description |
---|---|
Vf_scas=3.5 mM_per_min; Ksca_scas=0.02 mM; Vr_scas=0.035 mM_per_min; Ksuc_scas=5.0 mM | Reaction: sca => suc, Rate Law: cell*(Vf_scas*sca/Ksca_scas-Vr_scas*suc/Ksuc_scas)/(1+sca/Ksca_scas+suc/Ksuc_scas) |
Kaca_cs=0.03 mM; Kcit_cs=0.7 mM; Vf_cs=91.2 mM_per_min; Koaa_cs=0.07 mM; Kcoa_cs=0.3 mM; Vr_cs=0.912 mM_per_min | Reaction: aca + oaa => coa + cit, Rate Law: cell*(Vf_cs*aca/Kaca_cs*oaa/Koaa_cs-Vr_cs*coa/Kcoa_cs*cit/Kcit_cs)/((1+aca/Kaca_cs+coa/Kcoa_cs)*(1+oaa/Koaa_cs+cit/Kcit_cs)) |
Vr_sdh=7.31 mM_per_min; Vf_sdh=7.38 mM_per_min; Ksuc_sdh=0.02 mM; Kfa_sdh=0.4 mM | Reaction: suc => fa, Rate Law: cell*(Vf_sdh*suc/Ksuc_sdh-Vr_sdh*fa/Kfa_sdh)/(1+suc/Ksuc_sdh+fa/Kfa_sdh) |
Koaa_mdh=0.04 mM; Kmal_mdh=2.6 mM; Vr_mdh=353.11 mM_per_min; Vf_mdh=356.64 mM_per_min | Reaction: mal => oaa, Rate Law: cell*(Vf_mdh*mal/Kmal_mdh-Vr_mdh*oaa/Koaa_mdh)/(1+mal/Kmal_mdh+oaa/Koaa_mdh) |
Ksuc_icl=0.59 mM; Kgly_icl=0.13 mM; Vf_icl=1.9 mM_per_min; Vr_icl=0.019 mM_per_min; Kicit_icl=0.604 mM | Reaction: icit => suc + gly, Rate Law: cell*(Vf_icl*icit/Kicit_icl-Vr_icl*suc/Ksuc_icl*gly/Kgly_icl)/(1+icit/Kicit_icl+suc/Ksuc_icl+gly/Kgly_icl+icit/Kicit_icl*suc/Ksuc_icl+suc/Ksuc_icl*gly/Kgly_icl) |
Vf_fum=44.64 mM_per_min; Vr_fum=37.2 mM_per_min; Kmal_fum=0.04 mM; Kfa_fum=0.15 mM | Reaction: fa => mal, Rate Law: cell*(Vf_fum*fa/Kfa_fum-Vr_fum*mal/Kmal_fum)/(1+fa/Kfa_fum+mal/Kmal_fum) |
Kicit_acn=3.33 mM; Vr_acn=0.912 mM_per_min; Kcit_acn=1.7 mM; Vf_acn=91.2 mM_per_min | Reaction: cit => icit, Rate Law: cell*(Vf_acn*cit/Kcit_acn-Vr_acn*icit/Kicit_acn)/(1+cit/Kcit_acn+icit/Kicit_acn) |
Kakg_kdh=0.1 mM; Vr_kdh=0.3584 mM_per_min; Vf_kdh=35.84 mM_per_min; Ksca_kdh=1.0 mM | Reaction: akg => sca, Rate Law: cell*(Vf_kdh*akg/Kakg_kdh-Vr_kdh*sca/Ksca_kdh)/(1+akg/Kakg_kdh+sca/Ksca_kdh) |
Kakg_icd=0.13 mM; Kicit_icd=0.008 mM; Vr_icd=0.1472 mM_per_min; Vf_icd=14.72 mM_per_min | Reaction: icit => akg, Rate Law: cell*(Vf_icd*icit/Kicit_icd-Vr_icd*akg/Kakg_icd)/(1+icit/Kicit_icd+akg/Kakg_icd) |
Kmal_ms=1.0 mM; Vf_ms=1.9 mM_per_min; Vr_ms=0.019 mM_per_min; Kgly_ms=2.0 mM; Kcoa_ms=0.1 mM; Kaca_ms=0.01 mM | Reaction: gly + aca => mal + coa, Rate Law: cell*(Vf_ms*gly/Kgly_ms*aca/Kaca_ms-Vr_ms*mal/Kmal_ms*coa/Kcoa_ms)/((1+gly/Kgly_ms+mal/Kmal_ms)*(1+aca/Kaca_ms+coa/Kcoa_ms)) |
States:
Name | Description |
---|---|
gly | [glyoxylic acid; Glyoxylate] |
icit | [isocitric acid; Isocitrate] |
coa | [coenzyme A; C000010] |
mal | [malic acid; Malate] |
akg | [2-oxoglutaric acid; 2-Oxoglutarate] |
aca | [acetyl-CoA; Acetyl-CoA] |
cit | [citric acid; Citrate] |
fa | [fumaric acid; Fumarate] |
biosyn | biosyn |
oaa | [oxaloacetic acid; Oxaloacetate] |
suc | [succinic acid; Succinate] |
sca | [succinyl-CoA; Succinyl-CoA] |
BIOMD0000000219
— v0.0.1This a model from the article: Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tub…
Details
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment. link: http://identifiers.org/pubmed/16887020
Parameters:
Name | Description |
---|---|
Ksuc_ssadh=0.15 mM; Vr_ssadh=0.0651 mM_per_min; Vf_ssadh=6.51 mM_per_min; Kssa_ssadh=0.015 mM | Reaction: ssa => suc, Rate Law: cell*(Vf_ssadh*ssa/Kssa_ssadh-Vr_ssadh*suc/Ksuc_ssadh)/(1+ssa/Kssa_ssadh+suc/Ksuc_ssadh) |
Vr_scas=0.012 mM_per_min; Ksca_scas=0.02 mM; Vf_scas=1.2 mM_per_min; Ksuc_scas=5.0 mM | Reaction: sca => suc, Rate Law: cell*(Vf_scas*sca/Ksca_scas-Vr_scas*suc/Ksuc_scas)/(1+sca/Ksca_scas+suc/Ksuc_scas) |
Kakg_kgd=0.48 mM; Vr_kgd=0.483 mM_per_min; Vf_kgd=48.3 mM_per_min; Kssa_kgd=4.8 mM | Reaction: akg => ssa, Rate Law: cell*(Vf_kgd*akg/Kakg_kgd-Vr_kgd*ssa/Kssa_kgd)/(1+akg/Kakg_kgd+ssa/Kssa_kgd) |
Vr_icl1=0.01172 mM_per_min; Kicit_icl1=0.145 mM; Ksuc_icl1=0.59 mM; Vf_icl1=1.172 mM_per_min; Kgly_icl1=0.13 mM | Reaction: icit => suc + gly, Rate Law: cell*(Vf_icl1*icit/Kicit_icl1-Vr_icl1*suc/Ksuc_icl1*gly/Kgly_icl1)/(1+icit/Kicit_icl1+suc/Ksuc_icl1+gly/Kgly_icl1+icit/Kicit_icl1*suc/Ksuc_icl1+suc/Ksuc_icl1*gly/Kgly_icl1) |
Kfa_sdh=0.15 mM; Ksuc_sdh=0.12 mM; Vf_sdh=1.02 mM_per_min; Vr_sdh=1.02 mM_per_min | Reaction: suc => fa, Rate Law: cell*(Vf_sdh*suc/Ksuc_sdh-Vr_sdh*fa/Kfa_sdh)/(1+suc/Ksuc_sdh+fa/Kfa_sdh) |
Kakg_icd1=0.3 mM; Vf_icd1=10.2 mM_per_min; Vr_icd1=0.102 mM_per_min; Kicit_icd1=0.03 mM | Reaction: icit => akg, Rate Law: cell*(Vf_icd1*icit/Kicit_icd1-Vr_icd1*akg/Kakg_icd1)/(1+icit/Kicit_icd1+akg/Kakg_icd1) |
Vr_icd2=0.09965 mM_per_min; Kakg_icd2=0.6 mM; Vf_icd2=9.965 mM_per_min; Kicit_icd2=0.06 mM | Reaction: icit => akg, Rate Law: cell*(Vf_icd2*icit/Kicit_icd2-Vr_icd2*akg/Kakg_icd2)/(1+icit/Kicit_icd2+akg/Kakg_icd2) |
Kakg_kdh=0.1 mM; Ksca_kdh=1.0 mM; Vr_kdh=0.57344 mM_per_min; Vf_kdh=57.344 mM_per_min | Reaction: akg => sca, Rate Law: cell*(Vf_kdh*akg/Kakg_kdh-Vr_kdh*sca/Ksca_kdh)/(1+akg/Kakg_kdh+sca/Ksca_kdh) |
Kakg_icd1=0.3 mM; Vf_icd1=10.2 mM_per_min; Vr_icd2=0.09965 mM_per_min; Kakg_icd2=0.6 mM; Vr_icd1=0.102 mM_per_min; Vf_icd2=9.965 mM_per_min; Kicit_icd1=0.03 mM; Kicit_icd2=0.06 mM | Reaction: akg => biosyn; icit, Rate Law: cell*0.0341*((Vf_icd1*icit/Kicit_icd1-Vr_icd1*akg/Kakg_icd1)/(1+icit/Kicit_icd1+akg/Kakg_icd1)+(Vf_icd2*icit/Kicit_icd2-Vr_icd2*akg/Kakg_icd2)/(1+icit/Kicit_icd2+akg/Kakg_icd2)) |
Vr_cs=0.648 mM_per_min; Kaca_cs=0.05 mM; Kcit_cs=0.12 mM; Kcoa_cs=0.5 mM; Vf_cs=64.8 mM_per_min; Koaa_cs=0.012 mM | Reaction: aca + oaa => coa + cit, Rate Law: cell*(Vf_cs*aca/Kaca_cs*oaa/Koaa_cs-Vr_cs*coa/Kcoa_cs*cit/Kcit_cs)/((1+aca/Kaca_cs+coa/Kcoa_cs)*(1+oaa/Koaa_cs+cit/Kcit_cs)) |
Vr_acn=0.312 mM_per_min; Kicit_acn=0.7 mM; Vf_acn=31.2 mM_per_min; Kcit_acn=1.7 mM | Reaction: cit => icit, Rate Law: cell*(Vf_acn*cit/Kcit_acn-Vr_acn*icit/Kicit_acn)/(1+cit/Kcit_acn+icit/Kicit_acn) |
Kmal_fum=2.38 mM; Kfa_fum=0.25 mM; Vr_fum=87.7 mM_per_min; Vf_fum=87.7 mM_per_min | Reaction: fa => mal, Rate Law: cell*(Vf_fum*fa/Kfa_fum-Vr_fum*mal/Kmal_fum)/(1+fa/Kfa_fum+mal/Kmal_fum) |
Kicit_icl2=1.3 mM; Kgly_icl2=1.3 mM; Vr_icl2=0.00391 mM_per_min; Ksuc_icl2=5.9 mM; Vf_icl2=0.391 mM_per_min | Reaction: icit => suc + gly, Rate Law: cell*(Vf_icl2*icit/Kicit_icl2-Vr_icl2*suc/Ksuc_icl2*gly/Kgly_icl2)/(1+icit/Kicit_icl2+suc/Ksuc_icl2+gly/Kgly_icl2+icit/Kicit_icl2*suc/Ksuc_icl2+suc/Ksuc_icl2*gly/Kgly_icl2) |
Koaa_mdh=0.0443 mM; Vr_mdh=184.0 mM_per_min; Kmal_mdh=0.833 mM; Vf_mdh=184.0 mM_per_min | Reaction: mal => oaa, Rate Law: cell*(Vf_mdh*mal/Kmal_mdh-Vr_mdh*oaa/Koaa_mdh)/(1+mal/Kmal_mdh+oaa/Koaa_mdh) |
Kmal_ms=1.0 mM; Vf_ms=20.0 mM_per_min; Kgly_ms=0.057 mM; Kaca_ms=0.03 mM; Vr_ms=0.2 mM_per_min; Kcoa_ms=0.1 mM | Reaction: gly + aca => mal + coa, Rate Law: cell*(Vf_ms*gly/Kgly_ms*aca/Kaca_ms-Vr_ms*mal/Kmal_ms*coa/Kcoa_ms)/((1+gly/Kgly_ms+mal/Kmal_ms)*(1+aca/Kaca_ms+coa/Kcoa_ms)) |
States:
Name | Description |
---|---|
gly | [glyoxylic acid; Glyoxylate] |
icit | [isocitric acid; Isocitrate] |
coa | [coenzyme A; CoA] |
mal | [malic acid; Malate] |
ssa | [succinic semialdehyde; Succinate semialdehyde] |
akg | [2-oxoglutaric acid; 2-Oxoglutarate] |
aca | [acetyl-CoA; Acetyl-CoA] |
cit | [citric acid; Citrate] |
oaa | [oxaloacetic acid; Oxaloacetate] |
biosyn | biosyn |
fa | [fumaric acid; Fumarate] |
suc | [succinic acid; Succinate] |
sca | [succinyl-CoA; Succinyl-CoA] |
BIOMD0000000218
— v0.0.1This a model from the article: Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tub…
Details
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment. link: http://identifiers.org/pubmed/16887020
Parameters:
Name | Description |
---|---|
Ksuc_ssadh=0.15 mM; Vr_ssadh=0.0651 mM_per_min; Vf_ssadh=6.51 mM_per_min; Kssa_ssadh=0.015 mM | Reaction: ssa => suc, Rate Law: cell*(Vf_ssadh*ssa/Kssa_ssadh-Vr_ssadh*suc/Ksuc_ssadh)/(1+ssa/Kssa_ssadh+suc/Ksuc_ssadh) |
Vr_scas=0.012 mM_per_min; Ksca_scas=0.02 mM; Vf_scas=1.2 mM_per_min; Ksuc_scas=5.0 mM | Reaction: sca => suc, Rate Law: cell*(Vf_scas*sca/Ksca_scas-Vr_scas*suc/Ksuc_scas)/(1+sca/Ksca_scas+suc/Ksuc_scas) |
Kakg_kgd=0.48 mM; Vr_kgd=0.483 mM_per_min; Vf_kgd=48.3 mM_per_min; Kssa_kgd=4.8 mM | Reaction: akg => ssa, Rate Law: cell*(Vf_kgd*akg/Kakg_kgd-Vr_kgd*ssa/Kssa_kgd)/(1+akg/Kakg_kgd+ssa/Kssa_kgd) |
Vr_icl1=0.01172 mM_per_min; Kicit_icl1=0.145 mM; Ksuc_icl1=0.59 mM; Vf_icl1=1.172 mM_per_min; Kgly_icl1=0.13 mM | Reaction: icit => suc + gly, Rate Law: cell*(Vf_icl1*icit/Kicit_icl1-Vr_icl1*suc/Ksuc_icl1*gly/Kgly_icl1)/(1+icit/Kicit_icl1+suc/Ksuc_icl1+gly/Kgly_icl1+icit/Kicit_icl1*suc/Ksuc_icl1+suc/Ksuc_icl1*gly/Kgly_icl1) |
Kfa_sdh=0.15 mM; Ksuc_sdh=0.12 mM; Vf_sdh=1.02 mM_per_min; Vr_sdh=1.02 mM_per_min | Reaction: suc => fa, Rate Law: cell*(Vf_sdh*suc/Ksuc_sdh-Vr_sdh*fa/Kfa_sdh)/(1+suc/Ksuc_sdh+fa/Kfa_sdh) |
Kakg_icd1=0.3 mM; Vf_icd1=10.2 mM_per_min; Vr_icd1=0.102 mM_per_min; Kicit_icd1=0.03 mM | Reaction: icit => akg, Rate Law: cell*(Vf_icd1*icit/Kicit_icd1-Vr_icd1*akg/Kakg_icd1)/(1+icit/Kicit_icd1+akg/Kakg_icd1) |
Vr_icd2=0.09965 mM_per_min; Kakg_icd2=0.6 mM; Vf_icd2=9.965 mM_per_min; Kicit_icd2=0.06 mM | Reaction: icit => akg, Rate Law: cell*(Vf_icd2*icit/Kicit_icd2-Vr_icd2*akg/Kakg_icd2)/(1+icit/Kicit_icd2+akg/Kakg_icd2) |
Kakg_icd1=0.3 mM; Vf_icd1=10.2 mM_per_min; Vr_icd2=0.09965 mM_per_min; Kakg_icd2=0.6 mM; Vr_icd1=0.102 mM_per_min; Vf_icd2=9.965 mM_per_min; Kicit_icd1=0.03 mM; Kicit_icd2=0.06 mM | Reaction: akg => biosyn; icit, Rate Law: cell*0.0341*((Vf_icd1*icit/Kicit_icd1-Vr_icd1*akg/Kakg_icd1)/(1+icit/Kicit_icd1+akg/Kakg_icd1)+(Vf_icd2*icit/Kicit_icd2-Vr_icd2*akg/Kakg_icd2)/(1+icit/Kicit_icd2+akg/Kakg_icd2)) |
Vr_cs=0.648 mM_per_min; Kaca_cs=0.05 mM; Kcit_cs=0.12 mM; Kcoa_cs=0.5 mM; Vf_cs=64.8 mM_per_min; Koaa_cs=0.012 mM | Reaction: aca + oaa => coa + cit, Rate Law: cell*(Vf_cs*aca/Kaca_cs*oaa/Koaa_cs-Vr_cs*coa/Kcoa_cs*cit/Kcit_cs)/((1+aca/Kaca_cs+coa/Kcoa_cs)*(1+oaa/Koaa_cs+cit/Kcit_cs)) |
Vr_acn=0.312 mM_per_min; Kicit_acn=0.7 mM; Vf_acn=31.2 mM_per_min; Kcit_acn=1.7 mM | Reaction: cit => icit, Rate Law: cell*(Vf_acn*cit/Kcit_acn-Vr_acn*icit/Kicit_acn)/(1+cit/Kcit_acn+icit/Kicit_acn) |
Kmal_fum=2.38 mM; Kfa_fum=0.25 mM; Vr_fum=87.7 mM_per_min; Vf_fum=87.7 mM_per_min | Reaction: fa => mal, Rate Law: cell*(Vf_fum*fa/Kfa_fum-Vr_fum*mal/Kmal_fum)/(1+fa/Kfa_fum+mal/Kmal_fum) |
Kicit_icl2=1.3 mM; Kgly_icl2=1.3 mM; Vr_icl2=0.00391 mM_per_min; Ksuc_icl2=5.9 mM; Vf_icl2=0.391 mM_per_min | Reaction: icit => suc + gly, Rate Law: cell*(Vf_icl2*icit/Kicit_icl2-Vr_icl2*suc/Ksuc_icl2*gly/Kgly_icl2)/(1+icit/Kicit_icl2+suc/Ksuc_icl2+gly/Kgly_icl2+icit/Kicit_icl2*suc/Ksuc_icl2+suc/Ksuc_icl2*gly/Kgly_icl2) |
Koaa_mdh=0.0443 mM; Vr_mdh=184.0 mM_per_min; Kmal_mdh=0.833 mM; Vf_mdh=184.0 mM_per_min | Reaction: mal => oaa, Rate Law: cell*(Vf_mdh*mal/Kmal_mdh-Vr_mdh*oaa/Koaa_mdh)/(1+mal/Kmal_mdh+oaa/Koaa_mdh) |
Kmal_ms=1.0 mM; Vf_ms=20.0 mM_per_min; Kgly_ms=0.057 mM; Kaca_ms=0.03 mM; Vr_ms=0.2 mM_per_min; Kcoa_ms=0.1 mM | Reaction: gly + aca => mal + coa, Rate Law: cell*(Vf_ms*gly/Kgly_ms*aca/Kaca_ms-Vr_ms*mal/Kmal_ms*coa/Kcoa_ms)/((1+gly/Kgly_ms+mal/Kmal_ms)*(1+aca/Kaca_ms+coa/Kcoa_ms)) |
States:
Name | Description |
---|---|
gly | [glyoxylic acid; Glyoxylate] |
icit | [isocitric acid; Isocitrate] |
coa | [coenzyme A; CoA] |
mal | [malic acid; Malate] |
ssa | [succinic semialdehyde; Succinate semialdehyde] |
akg | [2-oxoglutaric acid; 2-Oxoglutarate] |
aca | [acetyl-CoA; Acetyl-CoA] |
cit | [citric acid; Citrate] |
oaa | [oxaloacetic acid; Oxaloacetate] |
biosyn | biosyn |
fa | [fumaric acid; Fumarate] |
suc | [succinic acid; Succinate] |
sca | [succinyl-CoA; Succinyl-CoA] |
MODEL1806080001
— v0.0.1This a model from the article: Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids…
Details
In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease. link: http://identifiers.org/pubmed/26356502
BIOMD0000000394
— v0.0.1Sivakumar2011 - EGF Receptor Signaling Pathway EGFR belongs to the human epidermal receptor (HER) family of receptor ty…
Details
The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs. link: http://identifiers.org/pubmed/21978399
Parameters:
Name | Description |
---|---|
kdiss_r6_s144 = 1.0; kass_r6_s144 = 1.0 | Reaction: s127 => s127; s144, Rate Law: s144*(kass_r6_s144*s127-kdiss_r6_s144*s127) |
kM_r14_s27 = 0.038; kM_r14_s28 = 1.65; kcatn_r14 = 0.725; kcatp_r14 = 0.558 | Reaction: s27 => s28; s26, Rate Law: s26*(kcatp_r14/kM_r14_s27*s27-kcatn_r14/kM_r14_s28*s28)/(1+s27/kM_r14_s27+s28/kM_r14_s28) |
kcatp_r8_s31 = 0.727; kM_r8_s124_s23 = 0.47; kcatn_r8_s31 = 0.636; kM_r8_s31_s23 = 0.614; kI_r8_s29 = 1.219; kM_r8_s31_s24 = 1.367; kI_r8_s22 = 0.583; kcatp_r8_s124 = 0.511; kI_r8_s33 = 0.293; kcatn_r8_s124 = 1.083; kM_r8_s124_s24 = 0.786 | Reaction: s23 => s24; s22, s29, s124, s33, s31, Rate Law: kI_r8_s22/(kI_r8_s22+s22)*kI_r8_s29/(kI_r8_s29+s29)*kI_r8_s33/(kI_r8_s33+s33)*(s124*(kcatp_r8_s124/kM_r8_s124_s23*s23-kcatn_r8_s124/kM_r8_s124_s24*s24)/(1+s23/kM_r8_s124_s23+s24/kM_r8_s124_s24)+s31*(kcatp_r8_s31/kM_r8_s31_s23*s23-kcatn_r8_s31/kM_r8_s31_s24*s24)/(1+s23/kM_r8_s31_s23+s24/kM_r8_s31_s24)) |
kcatp_r9 = 2.0; kcatn_r9 = 0.693; kM_r9_s25 = 0.626; kM_r9_s26 = 0.463 | Reaction: s25 => s26; s24, Rate Law: s24*(kcatp_r9/kM_r9_s25*s25-kcatn_r9/kM_r9_s26*s26)/(1+s25/kM_r9_s25+s26/kM_r9_s26) |
kass_r15 = 2.0; kdiss_r15 = 0.074 | Reaction: s28 => s34, Rate Law: kass_r15*s28-kdiss_r15*s34 |
kdiss_r4_s144 = 1.0; kass_r4_s144 = 1.0 | Reaction: s124 + s125 => s124 + s126; s144, Rate Law: s144*(kass_r4_s144*s124*s125-kdiss_r4_s144*s124*s126) |
kI_re11_s142 = 1.0; kM_re11_s129 = 1.0; Vp_re11 = 1.0; ki_re11_s129 = 1.0; kM_re11_s147 = 1.0 | Reaction: s129 + s147 => s144; s142, Rate Law: kI_re11_s142/(kI_re11_s142+s142)*Vp_re11*s129*s147/(ki_re11_s129*kM_re11_s147+kM_re11_s147*s129+kM_re11_s129*s147+s129*s147) |
kcatn_r11 = 0.566; kM_r11_s30 = 1.021; kM_r11_s29 = 1.459; kcatp_r11 = 0.787 | Reaction: s29 => s30; s127, Rate Law: s127*(kcatp_r11/kM_r11_s29*s29-kcatn_r11/kM_r11_s30*s30)/(1+s29/kM_r11_s29+s30/kM_r11_s30) |
kass_r17_s3 = 0.73; kdiss_r17_s3 = 1.13 | Reaction: s123 => s129; s3, Rate Law: s3*(kass_r17_s3*s123^2-kdiss_r17_s3*s129) |
kass_r7_s144 = 1.0; kdiss_r7_s144 = 1.0 | Reaction: s21 => s22; s144, Rate Law: s144*(kass_r7_s144*s21-kdiss_r7_s144*s22) |
States:
Name | Description |
---|---|
s29 | [Phosphatidylethanolamine-binding protein 1] |
s27 | [MAP kinase activity] |
s23 | [RAF proto-oncogene serine/threonine-protein kinase] |
s124 | [Ras-like protein 1] |
s24 | [RAF proto-oncogene serine/threonine-protein kinase] |
s25 | [Dual specificity mitogen-activated protein kinase kinase 1] |
s34 | Mitogenesis_br_Differentiation |
s30 | [Phosphatidylethanolamine-binding protein 1] |
s147 | [protein complex] |
s123 | [Receptor protein-tyrosine kinase] |
s26 | [Dual specificity mitogen-activated protein kinase kinase 1] |
s21 | [RAC-alpha serine/threonine-protein kinase] |
s22 | [RAC-alpha serine/threonine-protein kinase] |
s28 | [MAP kinase activity] |
s127 | [Protein kinase C alpha type] |
s129 | [Receptor protein-tyrosine kinase] |
s144 | [protein complex] |
s126 | [GTP] |
s125 | [GDP] |
BIOMD0000000395
— v0.0.1Sivakumar2011 - Hedgehog Signaling Pathway This is the current model for the Hedgehog signaling pathway. The best data…
Details
The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs. link: http://identifiers.org/pubmed/21978399
Parameters:
Name | Description |
---|---|
kass_r55 = 1.56 | Reaction: s158 => s75, Rate Law: kass_r55*s158 |
kdiss_r25 = 0.73; kass_r25 = 1.27 | Reaction: s160 => s161 + s69, Rate Law: kass_r25*s160-kdiss_r25*s161*s69 |
kdiss_r23_s21 = 1.0; kass_r23_s21 = 1.0 | Reaction: s159 => s68 + s160; s21, Rate Law: s21*(kass_r23_s21*s159-kdiss_r23_s21*s68*s160) |
kass_r52 = 0.6; kdiss_r52 = 1.67 | Reaction: s140 => s75, Rate Law: kass_r52*s140-kdiss_r52*s75 |
kass_re24_s157 = 1.0 | Reaction: s148 + s150 => s159; s157, Rate Law: s157*kass_re24_s157*s148*s150 |
kcatp_r53 = 1.29; kM_r53_s70 = 0.79; kcatn_r53 = 1.62 | Reaction: s70 => s70; s48, Rate Law: s48*(kcatp_r53/kM_r53_s70*s70-kcatn_r53/kM_r53_s70*s70)/(1+s70/kM_r53_s70+s70/kM_r53_s70) |
kass_r54 = 1.28; kdiss_r54 = 0.71 | Reaction: s70 + s71 => s158, Rate Law: kass_r54*s70*s71-kdiss_r54*s158 |
kass_r7 = 1.13; kdiss_r7 = 1.122 | Reaction: s7 + s1 => s21, Rate Law: kass_r7*s7*s1-kdiss_r7*s21 |
kass_r51 = 1.23; kdiss_r51 = 0.46 | Reaction: s135 + s128 => s140, Rate Law: kass_r51*s135*s128-kdiss_r51*s140 |
kass_r15_s21 = 1.53; kdiss_r15_s21 = 0.89 | Reaction: s46 + s9 => s48 + s10; s21, Rate Law: s21*(kass_r15_s21*s46*s9-kdiss_r15_s21*s48*s10) |
kass_r26 = 1.33; kdiss_r26 = 0.61 | Reaction: s161 => s70, Rate Law: kass_r26*s161-kdiss_r26*s70 |
kM_r14_s46 = 0.215; kcatp_r14 = 1.146; kcatn_r14 = 1.75; kM_r14_s69 = 1.03 | Reaction: s69 => s46; s21, Rate Law: s21*(kcatp_r14/kM_r14_s69*s69-kcatn_r14/kM_r14_s46*s46)/(1+s69/kM_r14_s69+s46/kM_r14_s46) |
States:
Name | Description |
---|---|
s150 | [protein complex] |
s1 | [Protein patched homolog 1] |
s48 | [protein complex] |
s159 | [protein complex] |
s135 | [Sin3-associated polypeptide 18] |
s7 | [Sonic hedgehog protein] |
s68 | [microtubule] |
s75 | [positive regulation of hh target transcription factor activity] |
s71 | [CREB-binding protein] |
s128 | [protein complex] |
s9 | [ATP] |
s161 | Complex_br_(Su(fu)/Cubitus) |
s10 | [ADP] |
s21 | [protein complex] |
s148 | smoothened |
s46 | [protein complex] |
s70 | [Cubitus interruptusCubitus interruptus, isoform A] |
s140 | [protein complex] |
s160 | [protein complex] |
s69 | [protein complex] |
s158 | [protein complex] |
BIOMD0000000396
— v0.0.1Sivakumar2011 - Notch Signaling Pathway Notch is a transmembrane receptor that mediates local cell-cell communication a…
Details
The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs. link: http://identifiers.org/pubmed/21978399
Parameters:
Name | Description |
---|---|
kdiss_r13 = 2.0; kass_r13 = 0.5 | Reaction: s24 + s26 + s27 + s29 => s35, Rate Law: kass_r13*s24*s26*s27*s29-kdiss_r13*s35 |
kcatn_r16 = 1.0; kcatp_r16 = 1.0; kM_r16_s39 = 1.0; ki_r16_s39 = 1.0 | Reaction: s24 + s39 => s37; s38, Rate Law: (kcatp_r16/(ki_r16_s39*kM_r16_s39)*s38*s24*s39-kcatn_r16/kM_r16_s39*s38*s37)/(1+s24/ki_r16_s39+s39/ki_r16_s39+s24*s39/(ki_r16_s39*kM_r16_s39)+s37/kM_r16_s39) |
kcatp_r9 = 1.5; kM_r9_s22 = 0.05; kcatn_r9 = 0.04; kM_r9_s7 = 1.0 | Reaction: s7 => s22; s23, Rate Law: s23*(kcatp_r9/kM_r9_s7*s7-kcatn_r9/kM_r9_s22*s22)/(1+s7/kM_r9_s7+s22/kM_r9_s22) |
kM_r18_s4 = 1.0; ki_r18_s4 = 1.5; kcatn_r18 = 1.5; kcatp_r18 = 1.0 | Reaction: s1 + s4 => s41; s42, Rate Law: (kcatp_r18/(ki_r18_s4*kM_r18_s4)*s42*s1*s4-kcatn_r18/kM_r18_s4*s42*s41)/(1+s1/ki_r18_s4+s4/ki_r18_s4+s1*s4/(ki_r18_s4*kM_r18_s4)+s41/kM_r18_s4) |
kcatp_r28 = 1.71; ki_r28_s41 = 1.28; kcatn_r28 = 1.48; kM_r28_s41 = 1.64 | Reaction: s7 + s41 => s67; s2, Rate Law: (kcatp_r28/(ki_r28_s41*kM_r28_s41)*s2*s7*s41-kcatn_r28/kM_r28_s41*s2*s67)/(1+s7/ki_r28_s41+s41/ki_r28_s41+s7*s41/(ki_r28_s41*kM_r28_s41)+s67/kM_r28_s41) |
kI_r21_s2 = 1.5; kass_r21 = 1.5; kdiss_r21 = 1.5 | Reaction: s41 + s48 => s53; s2, Rate Law: kI_r21_s2/(kI_r21_s2+s2)*(kass_r21*s41*s48-kdiss_r21*s53) |
kM_r26_s25 = 1.7; kM_r26_s64 = 1.61; kcatn_r26 = 1.0; kcatp_r26 = 0.5 | Reaction: s25 => s64; s65, Rate Law: s65*(kcatp_r26/kM_r26_s25*s25-kcatn_r26/kM_r26_s64*s64)/(1+s25/kM_r26_s25+s64/kM_r26_s64) |
kM_r25_s15 = 1.5; kM_r25_s53 = 1.5; kcatn_r25 = 1.5; kM_r25_s60 = 1.25; kcatp_r25 = 1.0 | Reaction: s53 => s60 + s15; s21, Rate Law: s21*(kcatp_r25*s53/kM_r25_s53-kcatn_r25*s60/kM_r25_s60*s15/kM_r25_s15)/(s53/kM_r25_s53+(1+s60/kM_r25_s60)*(1+s15/kM_r25_s15)) |
kcatp_r29 = 1.86; kM_r29_s67 = 1.61; kM_r29_s18 = 0.15; kM_r29_s15 = 1.87; kcatn_r29 = 1.78 | Reaction: s67 => s18 + s15; s21, Rate Law: s21*(kcatp_r29*s67/kM_r29_s67-kcatn_r29*s18/kM_r29_s18*s15/kM_r29_s15)/(s67/kM_r29_s67+(1+s18/kM_r29_s18)*(1+s15/kM_r29_s15)) |
kM_r8_s63 = 1.5; kcatp_r8 = 0.5; kcatn_r8 = 1.5; kM_r8_s15 = 1.0; kM_r8_s19 = 2.0 | Reaction: s15 => s19 + s63; s82, Rate Law: s82*(kcatp_r8*s15/kM_r8_s15-kcatn_r8*s19/kM_r8_s19*s63/kM_r8_s63)/(s15/kM_r8_s15+(1+s19/kM_r8_s19)*(1+s63/kM_r8_s63)) |
kI_re16_s81 = 0.00594; kass_re16 = 0.004; kdiss_re16 = 2.0 | Reaction: s76 + s77 => s82; s81, Rate Law: kI_re16_s81/(kI_re16_s81+s81)*(kass_re16*s76*s77-kdiss_re16*s82) |
kass_r31 = 0.055; kdiss_r31 = 2.0 | Reaction: s35 => s75, Rate Law: kass_r31*s35-kdiss_r31*s75 |
kdiss_r10 = 0.01; kI_r10_s25 = 1.0; kass_r10 = 2.0 | Reaction: s63 => s24; s25, Rate Law: kI_r10_s25/(kI_r10_s25+s25)*(kass_r10*s63-kdiss_r10*s24) |
kass_r30 = 1.95 | Reaction: s32 => s75, Rate Law: kass_r30*s32 |
kcatp_r11 = 0.5; kM_r11_s32 = 1.0; kcatn_r11 = 0.5; kM_r11_s26 = 1.5; kM_r11_s28 = 1.0 | Reaction: s32 => s26 + s28; s24, Rate Law: s24*(kcatp_r11*s32/kM_r11_s32-kcatn_r11*s26/kM_r11_s26*s28/kM_r11_s28)/(s32/kM_r11_s32+(1+s26/kM_r11_s26)*(1+s28/kM_r11_s28)) |
kass_r17 = 1.5; kdiss_r17 = 1.5 | Reaction: s37 => s40, Rate Law: kass_r17*s37-kdiss_r17*s40 |
States:
Name | Description |
---|---|
s76 | [CCO:U0000005] |
s7 | [Delta-like protein 1] |
s24 | [protein complex] |
s35 | [protein complex] |
s18 | [protein complex] |
s37 | [protein complex] |
s40 | [protein] |
s53 | [protein complex] |
s19 | [protein complex] |
s32 | [protein complex] |
s22 | [protein] |
s77 | [CCO:U0000005] |
s15 | [protein complex] |
s1 | [NOTCH1 protein] |
s48 | [Serrate] |
s67 | [protein complex] |
s63 | [protein complex] |
s41 | [NOTCH1 protein] |
s25 | [Protein numb homolog] |
s75 | [Basic helix-loop-helix transcription factorE(Spl)] |
s4 | [L-fucose] |
s82 | [Gamma-secretase subunit PEN-2; Gamma-secretase subunit APH-1A] |
s26 | [Suppressor of hairless protein] |
s64 | a25_degraded |
s28 | CoR |
s39 | [CCO:F0004655] |
s60 | [protein complex] |
s29 | [protein] |
s27 | [Mastermind-like protein 1] |
BIOMD0000000398
— v0.0.1Sivakumar2011_NeuralStemCellDifferentiation_CrosstalkThis model is generated by integrating [BIOMD0000000394](http://ww…
Details
The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs. link: http://identifiers.org/pubmed/21978399
Parameters:
Name | Description |
---|---|
kass_re35_s89 = 1.0; kdiss_re35_s89 = 1.0 | Reaction: s88 => s73; s89, Rate Law: s89*(kass_re35_s89*s88-kdiss_re35_s89*s73) |
kass_re36 = 1.0; kdiss_re36 = 1.0; kI_re36_s101 = 1.0 | Reaction: s96 + s98 => s100; s101, Rate Law: kI_re36_s101/(kI_re36_s101+s101)*(kass_re36*s96*s98-kdiss_re36*s100) |
kdiss_re33 = 1.0; kass_re33 = 1.0 | Reaction: s81 + s83 => s85, Rate Law: kass_re33*s81*s83-kdiss_re33*s85 |
kdiss_re31 = 1.0; kass_re31 = 1.0 | Reaction: s53 + s68 => s72, Rate Law: kass_re31*s53*s68-kdiss_re31*s72 |
kcatn_re40 = 1.0; kcatp_re40 = 1.0; ki_re40_s124 = 1.0; kM_re40_s124 = 1.0 | Reaction: s122 + s124 => s135; s111, Rate Law: (kcatp_re40/(ki_re40_s124*kM_re40_s124)*s111*s122*s124-kcatn_re40/kM_re40_s124*s111*s135)/(1+s122/ki_re40_s124+s124/ki_re40_s124+s122*s124/(ki_re40_s124*kM_re40_s124)+s135/kM_re40_s124) |
kass_re34_s85 = 1.0; kass_re34_s89 = 1.0; kdiss_re34_s89 = 1.0; kdiss_re34_s85 = 1.0 | Reaction: s88 => s88; s85, s89, Rate Law: s85*(kass_re34_s85*s88-kdiss_re34_s85*s88)+s89*(kass_re34_s89*s88-kdiss_re34_s89*s88) |
kM_re29_s60_s58 = 1.0; kV_re29_s60 = 1.0; kG_s57 = 1.0; kM_re29_s60_s53 = 1.0; kM_re29_s60_s57 = 1.0; kG_s58 = 1.0; kG_s53 = 1.0; kI_re29_s61 = 1.0 | Reaction: s57 => s53 + s58; s60, s61, Rate Law: kI_re29_s61/(kI_re29_s61+s61)*s60*kV_re29_s60*(s57/kM_re29_s60_s57*(kG_s57*kM_re29_s60_s57/(kG_s53*kM_re29_s60_s53*kG_s58*kM_re29_s60_s58))^(0.5)-s53/kM_re29_s60_s53*s58/kM_re29_s60_s58*(kG_s53*kM_re29_s60_s53*kG_s58*kM_re29_s60_s58/(kG_s57*kM_re29_s60_s57))^(0.5))/(s57/kM_re29_s60_s57+(1+s53/kM_re29_s60_s53)*(1+s58/kM_re29_s60_s58)) |
kass_re32 = 1.0; kdiss_re32 = 1.0 | Reaction: s72 => s73, Rate Law: kass_re32*s72-kdiss_re32*s73 |
kI_re42_s147 = 1.0; kdiss_re42 = 1.0; kI_re42_s135 = 1.0; kass_re42 = 1.0 | Reaction: s142 + s144 => s146; s147, s135, Rate Law: kI_re42_s147/(kI_re42_s147+s147)*kI_re42_s135/(kI_re42_s135+s135)*(kass_re42*s142*s144-kdiss_re42*s146) |
kass_re37 = 1.0; kdiss_re37 = 1.0 | Reaction: s100 => s73, Rate Law: kass_re37*s100-kdiss_re37*s73 |
kass_re38 = 1.0; kdiss_re38 = 1.0 | Reaction: s107 + s109 => s111, Rate Law: kass_re38*s107*s109-kdiss_re38*s111 |
kass_re43 = 1.0; kdiss_re43 = 1.0 | Reaction: s144 => s73, Rate Law: kass_re43*s144-kdiss_re43*s73 |
States:
Name | Description |
---|---|
s146 | [protein complex] |
s107 | [Protein Wnt-3a] |
s111 | Complex Wnt-Frzzl |
s124 | [protein complex] |
s135 | [protein complex] |
s142 | [Glycogen synthase kinase-3 beta] |
s109 | [Frizzled] |
s57 | [Neurogenic locus Notch protein] |
s58 | [protein] |
s53 | [Neurogenic locus notch homolog protein 1] |
s122 | [Dishevelled, dsh homolog 1 (Drosophila)] |
s100 | [protein complex] |
s81 | [Sonic hedgehog protein] |
s72 | [protein complex] |
s96 | [Pro-epidermal growth factor] |
s98 | [EGFR protein] |
s144 | [Catenin beta-1] |
s68 | [Recombining binding protein suppressor of hairless] |
s73 | [139605] |
s88 | [Smoothened homolog] |
s85 | [protein complex] |
s83 | [Protein patched homolog 1] |
BIOMD0000000397
— v0.0.1Sivakumar2011_WntSignalingPathwayThe secreted protein Wnt activates the heptahelical receptor Frizzled on nieghboring ce…
Details
The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs. link: http://identifiers.org/pubmed/21978399
Parameters:
Name | Description |
---|---|
kass_r107 = 0.91; kdiss_r107 = 1.056 | Reaction: s239 => s5, Rate Law: kass_r107*s239-kdiss_r107*s5 |
kass_r67 = 0.86; kdiss_r67 = 0.7 | Reaction: s188 + s172 => s305, Rate Law: kass_r67*s188*s172-kdiss_r67*s305 |
kass_r66 = 1.99; kdiss_r66 = 0.036 | Reaction: s183 + s173 => s188, Rate Law: kass_r66*s183*s173-kdiss_r66*s188 |
kass_re65 = 1.68 | Reaction: s260 => s232, Rate Law: kass_re65*s260 |
kdiss_r105 = 1.62; kass_r105 = 0.48 | Reaction: s292 => s37, Rate Law: kass_r105*s292-kdiss_r105*s37 |
kass_r91 = 0.36; kdiss_r91 = 1.16 | Reaction: s266 => s155 + s267, Rate Law: kass_r91*s266-kdiss_r91*s155*s267 |
kass_r54 = 0.8; kdiss_r54 = 1.7 | Reaction: s123 + s75 => s159, Rate Law: kass_r54*s123*s75-kdiss_r54*s159 |
kass_r58 = 1.74; kdiss_r58 = 0.25 | Reaction: s36 => s232, Rate Law: kass_r58*s36-kdiss_r58*s232 |
kass_r48 = 0.85; kdiss_r48 = 1.36 | Reaction: s123 + s46 => s129, Rate Law: kass_r48*s123*s46-kdiss_r48*s129 |
kass_r1 = 0.784; kdiss_r1 = 0.82 | Reaction: s5 + s1 => s16, Rate Law: kass_r1*s5*s1-kdiss_r1*s16 |
kass_r103 = 0.45; kdiss_r103 = 1.277 | Reaction: s288 + s102 => s292, Rate Law: kass_r103*s288*s102-kdiss_r103*s292 |
kass_r98 = 1.97; kdiss_r98 = 1.09 | Reaction: s275 => s101 + s278, Rate Law: kass_r98*s275-kdiss_r98*s101*s278 |
kass_r63 = 1.77; kdiss_r63 = 0.61 | Reaction: s174 + s232 => s176, Rate Law: kass_r63*s174*s232-kdiss_r63*s176 |
kass_r64 = 1.29; kdiss_r64 = 0.72 | Reaction: s176 + s170 => s179, Rate Law: kass_r64*s176*s170-kdiss_r64*s179 |
kass_r68 = 2.0 | Reaction: s305 => s195, Rate Law: kass_r68*s305 |
kass_r65 = 1.8; kdiss_r65 = 0.004 | Reaction: s179 + s171 => s183, Rate Law: kass_r65*s179*s171-kdiss_r65*s183 |
kdiss_r5 = 0.92; kass_r5 = 1.15 | Reaction: s28 + s16 => s27, Rate Law: kass_r5*s28*s16-kdiss_r5*s27 |
kass_re64 = 0.83 | Reaction: s270 => s232, Rate Law: kass_re64*s270 |
kdiss_r47 = 0.81; kass_r47 = 1.31 | Reaction: s121 + s36 => s123, Rate Law: kass_r47*s121*s36-kdiss_r47*s123 |
kdiss_r106 = 1.13; kass_r106 = 0.05 | Reaction: s286 => s30, Rate Law: kass_r106*s286-kdiss_r106*s30 |
kass_r102 = 0.163; kdiss_r102 = 1.65 | Reaction: s286 + s31 => s288, Rate Law: kass_r102*s286*s31-kdiss_r102*s288 |
kdiss_r96 = 0.183; kass_r96 = 1.45 | Reaction: s159 + s268 => s275, Rate Law: kass_r96*s159*s268-kdiss_r96*s275 |
kass_r92 = 0.58; kdiss_r92 = 0.92 | Reaction: s267 => s61 + s260, Rate Law: kass_r92*s267-kdiss_r92*s61*s260 |
kdiss_r88 = 1.09; kass_r88 = 0.2 | Reaction: s252 + s61 => s259, Rate Law: kass_r88*s252*s61-kdiss_r88*s259 |
kass_r90 = 0.27; kdiss_r90 = 1.028 | Reaction: s259 + s268 => s266, Rate Law: kass_r90*s259*s268-kdiss_r90*s266 |
kass_r85_s30 = 0.7; kdiss_r85_s30 = 0.649 | Reaction: s129 + s32 => s245 + s33; s30, Rate Law: s30*(kass_r85_s30*s129*s32-kdiss_r85_s30*s245*s33) |
kass_r104_s30 = 0.39; kdiss_r104_s30 = 1.278 | Reaction: s107 + s32 => s286 + s33; s27, s30, Rate Law: s30*(kass_r104_s30*s107*s32-kdiss_r104_s30*s286*s33) |
kdiss_r99 = 0.854; kass_r99 = 0.51 | Reaction: s278 => s164 + s270, Rate Law: kass_r99*s278-kdiss_r99*s164*s270 |
kI_r86_s304 = 1.43; kass_r86_s37 = 0.87; kdiss_r86_s37 = 1.32 | Reaction: s245 + s32 + s32 + s32 => s252 + s33 + s33 + s33; s37, s304, Rate Law: kI_r86_s304/(kI_r86_s304+s304)*s37*(kass_r86_s37*s245*s32*s32*s32-kdiss_r86_s37*s252*s33*s33*s33) |
States:
Name | Description |
---|---|
s107 | Complex_br_(Dishevelled/Beta-Arrestin/_br_Frodo) |
s260 | [Catenin beta-1] |
s159 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br_PP2A/_Beta_-Catenin/_br_Siah-1/Ebi) |
s172 | [CREB-binding protein] |
s232 | [Catenin beta-1] |
s5 | [Protein Wnt-3a] |
s121 | Complex_br_(Axin/PP2A/_br_Adenomatous Polyposis Coli) |
s278 | Complex_br_(Adenomatous Polyposis Coli/_Beta_-Catenin/_br_Axin/PP2A) |
s61 | [Beta-TrCP] |
s37 | [Glycogen synthase kinase-3 beta] |
s36 | [Catenin beta-1] |
s183 | Complex_br_(Bcl9/_Beta_-Catenin/_br_TCF/Smad4/_br_Pygo) |
s31 | [Casein kinase II subunit beta; Casein kinase II subunit alpha] |
s266 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br_PP2A/Diversin/_br_Casein Kinase 1/_Beta_-Catenin/_br__beta_TrCP/Glycogen Synthase Kinase-3_Beta_) |
s46 | [Diversin] |
s268 | Ubiquitin |
s129 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br_Diversin/_Beta_-Catenin/_br_PP2A) |
s1 | [Frizzled] |
s292 | Complex_br_(Dishevelled/Casein Kinase 2/_br_Beta-Arrestin/Frodo/_br_FRAT) |
s267 | Complex_br_(_beta_TrCP/_Beta_-Catenin) |
s75 | Complex_br_(Ebi/Siah-1) |
s33 | [ADP] |
s101 | Complex_br_(Siah-1/Ebi) |
s16 | Complex_br_(Wnt/Frizzled) |
s179 | Complex_br_(TCF/_Beta_-Catenin/_br_Smad4/Bcl9) |
s155 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br_Diversin/Casein Kinase 1/_br_Glycogen Synthase Kinase-3_Beta_/PP2A) |
s28 | [Low-density lipoprotein receptor-related protein 6; Low-density lipoprotein receptor-related protein 5] |
s174 | Complex_br_(TCF/Smad4) |
s270 | [Catenin beta-1] |
s245 | Complex_br_(Adenomatous Polyposis Coli/_Beta_-Catenin/_br_Axin/PP2A/_br_Diversin/Casein Kinase 1) |
s252 | Complex_br_(Adenomatous Polyposis Coli/_Beta_-Catenin/_br_Glycogen Synthase Kinase-3_Beta_/Axin/_br_PP2A/Diversin/_br_Casein Kinase 1) |
s239 | [Wingless-type MMTV integration site family member 3a] |
s32 | [ATP] |
s170 | [B-cell CLL/lymphoma 9 protein] |
s195 | Wnt Target Genes |
s305 | Complex_br_(Bcl9/Pygo/../Smad4) |
s275 | Complex_br_(Adenomatous Polyposis Coli/_Beta_-Catenin/_br_Siah-1/Ebi/_br_Axin/PP2A) |
s164 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br_PP2A) |
s176 | Complex_br_(TCF/Smad4/_br__Beta_-Catenin) |
s188 | Complex_br_(_Beta_-Catenin/TCF/_br_Smad4/Bcl9/_br_Pygo/SWI/_br_SNF) |
s30 | [Casein kinase I isoform alpha] |
s171 | [Protein pygopus] |
s123 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br__Beta_-Catenin/PP2A) |
s288 | Complex_br_(Dishevelled/Beta-Arrestin/_br_Frodo/Casein Kinase 2) |
s173 | [SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A member 5] |
s286 | Complex_br_(Dishevelled/Beta-Arrestin/_br_Frodo) |
s259 | Complex_br_(Adenomatous Polyposis Coli/Axin/_br_PP2A/Diversin/_br_Casein Kinase 1/_Beta_-Catenin/_br__beta_TrCP/Glycogen Synthase Kinase-3_Beta_) |
s102 | [Proto-oncogene FRAT1] |
s27 | Complex_br_(Frizzled/Wnt/_br_LRP5/6) |
MODEL2006170002
— v0.0.1This 89-node Boolean model of mammalian growth factor signaling can reproduce oscillations in PI3K signaling in cycling…
Details
The PI3K/AKT signaling pathway plays a role in most cellular functions linked to cancer progression, including cell growth, proliferation, cell survival, tissue invasion and angiogenesis. It is generally recognized that hyperactive PI3K/AKT1 are oncogenic due to their boost to cell survival, cell cycle entry and growth-promoting metabolism. That said, the dynamics of PI3K and AKT1 during cell cycle progression are highly nonlinear. In addition to negative feedback that curtails their activity, protein expression of PI3K subunits has been shown to oscillate in dividing cells. The low-PI3K/low-AKT1 phase of these oscillations is required for cytokinesis, indicating that oncogenic PI3K may directly contribute to genome duplication. To explore this, we construct a Boolean model of growth factor signaling that can reproduce PI3K oscillations and link them to cell cycle progression and apoptosis. The resulting modular model reproduces hyperactive PI3K-driven cytokinesis failure and genome duplication and predicts the molecular drivers responsible for these failures by linking hyperactive PI3K to mis-regulation of Polo-like kinase 1 (Plk1) expression late in G2. To do this, our model captures the role of Plk1 in cell cycle progression and accurately reproduces multiple effects of its loss: G2 arrest, mitotic catastrophe, chromosome mis-segregation / aneuploidy due to premature anaphase, and cytokinesis failure leading to genome duplication, depending on the timing of Plk1 inhibition along the cell cycle. Finally, we offer testable predictions on the molecular drivers of PI3K oscillations, the timing of these oscillations with respect to division, and the role of altered Plk1 and FoxO activity in genome-level defects caused by hyperactive PI3K. Our model is an important starting point for the predictive modeling of cell fate decisions that include AKT1-driven senescence, as well as the non-intuitive effects of drugs that interfere with mitosis. link: http://identifiers.org/pubmed/30875364
BIOMD0000000619
— v0.0.1# Basic PBPK (Physiologically Based PharmacoKinetic) model of Acetaminophen. This is a basic model of Acetaminophen (APA…
Details
We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics. link: http://identifiers.org/pubmed/27636091
Parameters:
Name | Description |
---|---|
QRest = 188.8 Volumetric_Flow | Reaction: CArt => CRest, Rate Law: QRest*CArt/VArt |
kGutabs = 1.5 first_order_rate_constant | Reaction: AGutlumen => CGut, Rate Law: kGutabs*AGutlumen |
Kkidney2plasma = 1.0 dimensionless; Qgfr = 0.9438 Volumetric_Flow | Reaction: CKidney => CTubules, Rate Law: Qgfr/VKidney*CKidney/Kkidney2plasma |
QGut = 74.42 Volumetric_Flow | Reaction: CArt => CGut, Rate Law: QGut/VArt*CArt |
CLmetabolism = 9.5 first_order_rate_constant; Kliver2plasma = 1.0 dimensionless; Fraction_unbound_plasma = 0.8 dimensionless | Reaction: CLiver => CMetabolized, Rate Law: CLmetabolism*CLiver/(Kliver2plasma*Fraction_unbound_plasma) |
Ratioblood2plasma = 1.09 dimensionless; Fraction_unbound_plasma = 0.8 dimensionless; KRest2plasma = 1.6 dimensionless; QRest = 188.8 Volumetric_Flow | Reaction: CRest => CVen, Rate Law: QRest/VRest*CRest*Ratioblood2plasma/(KRest2plasma*Fraction_unbound_plasma) |
QGut = 74.42 Volumetric_Flow; Kliver2plasma = 1.0 dimensionless; QLiver = 19.42 Volumetric_Flow; Ratioblood2plasma = 1.09 dimensionless; Fraction_unbound_plasma = 0.8 dimensionless | Reaction: CLiver => CVen, Rate Law: (QLiver+QGut)/VLiver*CLiver*Ratioblood2plasma/(Kliver2plasma*Fraction_unbound_plasma) |
QCardiac = 363.0 Volumetric_Flow | Reaction: CLung => CArt, Rate Law: QCardiac/VLung*CLung |
QLiver = 19.42 Volumetric_Flow | Reaction: CArt => CLiver, Rate Law: QLiver/VArt*CArt |
QKidney = 80.37 Volumetric_Flow | Reaction: CArt => CKidney, Rate Law: QKidney/VArt*CArt |
Kkidney2plasma = 1.0 dimensionless; QKidney = 80.37 Volumetric_Flow; Ratioblood2plasma = 1.09 dimensionless; Fraction_unbound_plasma = 0.8 dimensionless | Reaction: CKidney => CVen, Rate Law: QKidney/VKidney*CKidney*Ratioblood2plasma/(Kkidney2plasma*Fraction_unbound_plasma) |
States:
Name | Description |
---|---|
CVen | CVen |
CArt | CArt |
CKidney | CKidney |
CMetabolized | CMetabolized |
CGut | CGut |
AGutlumen | AGutlumen |
CTubules | CTubules |
CLung | CLung |
CLiver | CLiver |
CRest | CRest |
BIOMD0000000624
— v0.0.1Sluka2016 - Acetaminophen metabolism**Liver metabolism of Acetaminophen:** Acetaminophen (APAP) is metabolized in the li…
Details
We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics. link: http://identifiers.org/pubmed/27636091
Parameters:
Name | Description |
---|---|
Vmax_2E1_APAP = 2.0E-5 flux; Km_2E1_APAP = 1.29 millimolar | Reaction: APAP => NAPQI, Rate Law: Vmax_2E1_APAP*APAP/(Km_2E1_APAP+APAP) |
kGsh = 1.0E-4 first_order_rate_constant; GSHmax = 10.0 millimolar | Reaction: X1 => GSH, Rate Law: kGsh*(GSHmax-GSH)*compartment |
Km_PhaseIIEnzGlu_APAP = 1.0 millimolar; Vmax_PhaseIIEnzGlu_APAP = 0.001 flux | Reaction: APAP => APAPconj_Glu, Rate Law: Vmax_PhaseIIEnzGlu_APAP*APAP/(Km_PhaseIIEnzGlu_APAP+APAP) |
kNapqiGsh = 0.1 second_order_rate_constant | Reaction: GSH + NAPQI => NAPQIGSH, Rate Law: kNapqiGsh*NAPQI*GSH*compartment*compartment |
Km_PhaseIIEnzSul_APAP = 0.2 millimolar; Vmax_PhaseIIEnzSul_APAP = 1.75E-4 flux | Reaction: APAP => APAPconj_Sul, Rate Law: Vmax_PhaseIIEnzSul_APAP*APAP/(Km_PhaseIIEnzSul_APAP+APAP) |
States:
Name | Description |
---|---|
APAPconj Sul | [paracetamol sulfate] |
APAP | [paracetamol] |
NAPQI | [N-acetyl-1,4-benzoquinone imine] |
NAPQIGSH | [acetaminophen glutathione conjugate] |
APAPconj Glu | [acetaminophen O-beta-D-glucosiduronic acid] |
X1 | X1 |
GSH | [glutathione] |
MODEL1105180000
— v0.0.1This model is from the article: Flux balance analysis: a geometric perspective. Smallbone K, Simeonidis E. J Theor B…
Details
Advances in the field of bioinformatics have led to reconstruction of genome-scale networks for a number of key organisms. The application of physicochemical constraints to these stoichiometric networks allows researchers, through methods such as flux balance analysis, to highlight key sets of reactions necessary to achieve particular objectives. The key benefits of constraint-based analysis lie in the minimal knowledge required to infer systemic properties. However, network degeneracy leads to a large number of flux distributions that satisfy any objective; moreover, these distributions may be dominated by biologically irrelevant internal cycles. By examining the geometry underlying the problem, we define two methods for finding a unique solution within the space of all possible flux distributions; such a solution contains no internal cycles, and is representative of the space as a whole. The first method draws on typical geometric knowledge, but cannot be applied to large networks because of the high computational complexity of the problem. Thus a second method, an iteration of linear programs which scales easily to the genome scale, is defined. The algorithm is run on four recent genome-scale models, and unique flux solutions are found. The algorithm set out here will allow researchers in flux balance analysis to exchange typical solutions to their models in a reproducible format. Moreover, having found a single solution, statistical analyses such as correlations may be performed. link: http://identifiers.org/pubmed/19490860
MODEL1001200000
— v0.0.1This is the model described in the article: Towards a genome-scale kinetic model of cellular metabolism Smallbone K,…
Details
Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system.Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at http://www.mcisb.org/resources/genomescale/. link: http://identifiers.org/pubmed/20109182
BIOMD0000000380
— v0.0.1This model is from the article: Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae. Smal…
Details
In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed. link: http://identifiers.org/pubmed/21943906
Parameters:
Name | Description |
---|---|
Keq=0.3 dimensionless; Kf6p=0.29 mM; shock=1.0 dimensionless; heat = 0.0 dimensionless; Kg6p=1.4 mM; Vmax=1071.0 mM per min | Reaction: g6p => f6p, Rate Law: cell*shock^heat*Vmax/Kg6p*(g6p-f6p/Keq)/(1+g6p/Kg6p+f6p/Kf6p) |
Kudg=0.886 mM; activity=1.0 dimensionless; heat = 0.0 dimensionless; Vmax=1.371 mM per min; shock=12.0 dimensionless; Kg6p=3.8 mM | Reaction: g6p + udg => t6p + udp + h, Rate Law: cell*activity*shock^heat*Vmax*g6p*udg/(Kg6p*Kudg)/((1+g6p/Kg6p)*(1+udg/Kudg)) |
Kg1p=0.023 mM; heat = 0.0 dimensionless; Vmax=0.3545 mM per min; Kg6p=0.05 mM; shock=16.0 dimensionless; Keq=0.1667 dimensionless | Reaction: g6p => g1p, Rate Law: cell*shock^heat*Vmax/Kg6p*(g6p-g1p/Keq)/(1+g6p/Kg6p+g1p/Kg1p) |
Kt6p=0.5 mM; heat = 0.0 dimensionless; Vmax=6.5 mM per min; shock=18.0 dimensionless | Reaction: t6p + h2o => trh + pho, Rate Law: cell*shock^heat*Vmax*t6p/Kt6p/(1+t6p/Kt6p) |
Kg6p=30.0 mM; Kglc=0.08 mM; Katp=0.15 mM; heat = 0.0 dimensionless; Keq=2000.0 dimensionless; shock=8.0 dimensionless; Vmax=289.6 mM per min; Kadp=0.23 mM; Kit6p=0.04 mM | Reaction: glc + atp => g6p + adp + h; t6p, Rate Law: cell*shock^heat*Vmax/(Kglc*Katp)*(glc*atp-g6p*adp/Keq)/((1+glc/Kglc+g6p/Kg6p+t6p/Kit6p)*(1+atp/Katp+adp/Kadp)) |
Vmax=15.2 mM per min; heat = 0.0 dimensionless; shock=6.0 dimensionless; Ktrh=2.99 mM | Reaction: trh + h2o => glc, Rate Law: cell*shock^heat*Vmax*trh/Ktrh/(1+trh/Ktrh) |
heat = 0.0 dimensionless; Vmax=97.24 mM per min; shock=8.0 dimensionless; Ki=0.91 dimensionless; Kglc=1.1918 mM | Reaction: glx => glc, Rate Law: cell*shock^heat*Vmax*(glx-glc)/Kglc/(1+(glx+glc)/Kglc+Ki*glx*glc/Kglc^2) |
Kiutp=0.11 mM; heat = 0.0 dimensionless; Kg1p=0.32 mM; Kutp=0.11 mM; Vmax=36.82 mM per min; shock=16.0 dimensionless; Kiudg=0.0035 mM | Reaction: g1p + utp + h => udg + ppi, Rate Law: cell*shock^heat*Vmax*utp*g1p/(Kutp*Kg1p)/(Kiutp/Kutp+utp/Kutp+g1p/Kg1p+utp*g1p/(Kutp*Kg1p)+Kiutp/Kutp*udg/Kiudg+g1p*udg/(Kg1p*Kiudg)) |
States:
Name | Description |
---|---|
ppi | [diphosphate(4-)] |
glx | [alpha-D-glucose] |
trh | [alpha,alpha-trehalose] |
pho | [hydrogenphosphate] |
glc | [alpha-D-glucose] |
h2o | [water] |
h | [proton] |
udp | [UDP] |
atp | [ATP] |
utp | [UTP(4-)] |
g6p | [alpha-D-glucose 6-phosphate] |
adp | [ADP] |
t6p | [alpha,alpha-trehalose 6-phosphate] |
f6p | [beta-D-fructofuranose 6-phosphate] |
udg | [UDP-D-glucose] |
g1p | [D-glucopyranose 1-phosphate] |
BIOMD0000000520
— v0.0.1Smallbone2013 - Colon Crypt cycle - Version 0This model is described in the article: [A mathematical model of the colon…
Details
Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models. link: http://identifiers.org/pubmed/24354351
Parameters:
Name | Description |
---|---|
d2 = 1.83 per day | Reaction: N2 => ; N2, Rate Law: d2*N2 |
d1 = 0.263 per day | Reaction: N1 => ; N1, Rate Law: d1*N1 |
b1 = 0.547 per day; m1 = 29.2408052354609 cell; c1 = 1.0 per day | Reaction: N1 => N1 + N2; N1, Rate Law: (b1+c1*N1/(N1+m1))*N1 |
a0 = 0.0999999999999998 per day | Reaction: N0 => N0; N0, Rate Law: a0*N0 |
b0 = 0.218 per day; c0 = 1.0 per day; m0 = 2.92408052354609 cell | Reaction: N0 => N0 + N1; N0, Rate Law: (b0+c0*N0/(N0+m0))*N0 |
a1 = 0.239254806051979 per day | Reaction: N1 => N1; N1, Rate Law: a1*N1 |
d0 = 0.1 per day | Reaction: N0 => ; N0, Rate Law: d0*N0 |
States:
Name | Description |
---|---|
N1 | [stem cell] |
N0 | [stem cell] |
N2 | [stem cell] |
BIOMD0000000519
— v0.0.1Smallbone2013 - Colon Crypt cycle - Version 1This model is described in the article: [A mathematical model of the colon…
Details
Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models. link: http://identifiers.org/pubmed/24354351
Parameters:
Name | Description |
---|---|
p01 = 0.855699855699856 dimensionless; f0 = NaN cell per_day | Reaction: N0 => N0 + N1, Rate Law: p01*f0 |
d1 = 0.420467092599869 per day | Reaction: N1 => ; N1, Rate Law: d1*N1 |
p12 = 0.827377484810943 dimensionless; f1 = NaN cell per_day | Reaction: N1 => N1 + N2, Rate Law: p12*f1 |
d2 = 1.10138534772246 per day | Reaction: N2 => ; N2, Rate Law: d2*N2 |
d0 = 0.1 per day | Reaction: N0 => ; N0, Rate Law: d0*N0 |
f0 = NaN cell per_day; p00 = NaN dimensionless | Reaction: N0 => N0, Rate Law: p00*f0 |
f1 = NaN cell per_day; p11 = NaN dimensionless | Reaction: N1 => N1, Rate Law: p11*f1 |
States:
Name | Description |
---|---|
N1 | [stem cell] |
N0 | [stem cell] |
N2 | [stem cell] |
BIOMD0000000518
— v0.0.1Smallbone2013 - Colon Crypt cycle - Version 2This model is described in the article: [A mathematical model of the colon…
Details
Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models. link: http://identifiers.org/pubmed/24354351
Parameters:
Name | Description |
---|---|
p01 = 0.815689334807208 dimensionless; f0 = NaN cell per_day | Reaction: N0 => N0 + N1, Rate Law: p01*f0 |
p12 = 0.827377484810943 dimensionless; f1 = NaN cell per_day | Reaction: N1 => N1 + N2, Rate Law: p12*f1 |
f0 = NaN cell per_day; p03 = NaN dimensionless | Reaction: N0 => N0 + N3, Rate Law: p03*f0 |
d2 = 2.20277069544492 per day; K2X = 1.5709821429 cell | Reaction: N2 => ; N3, N2, N3, Rate Law: d2*N2*K2X/(N3+K2X) |
K1X = 1.5709821429 cell; d1 = 0.840934185199738 per day | Reaction: N1 => ; N3, N1, N3, Rate Law: d1*N1*K1X/(N3+K1X) |
d3 = 0.0379622536021846 per day | Reaction: N3 => ; N3, Rate Law: d3*N3 |
d0 = 0.2 per day; K0X = 1.5709821429 cell | Reaction: N0 => ; N3, N0, N3, Rate Law: d0*N0*K0X/(N3+K0X) |
f1 = NaN cell per_day; p11 = NaN dimensionless | Reaction: N1 => N1, Rate Law: p11*f1 |
f0 = NaN cell per_day; p00 = NaN dimensionless | Reaction: N0 => N0, Rate Law: p00*f0 |
States:
Name | Description |
---|---|
N2 | [stem cell] |
N1 | [stem cell] |
N0 | [stem cell] |
N3 | N3 |
BIOMD0000000517
— v0.0.1Smallbone2013 - Colon Crypt cycle - Version 3This model is described in the article: [A mathematical model of the colon…
Details
Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models. link: http://identifiers.org/pubmed/24354351
Parameters:
Name | Description |
---|---|
d2 = 1.888676618 per day; K2X = 2.70405837954268 cell | Reaction: N2 => ; N3, N2, N3, Rate Law: d2*N2*K2X/(N3+K2X) |
f0 = NaN cell per_day; p03 = NaN dimensionless | Reaction: N0 => N0 + N3, Rate Law: p03*f0 |
d3 = 0.1677359306 per day | Reaction: N3 => ; N3, Rate Law: d3*N3 |
d0 = 0.02 per day; K0X = 0.153646265911768 cell | Reaction: N0 => ; N3, N0, N3, Rate Law: d0*N0*K0X/(N3+K0X) |
p12 = 0.8050459589 dimensionless; f1 = NaN cell per_day | Reaction: N1 => N1 + N2, Rate Law: p12*f1 |
K1X = 15.3645644864404 cell; d1 = 0.5480597115 per day | Reaction: N1 => ; N3, N1, N3, Rate Law: d1*N1*K1X/(N3+K1X) |
p01 = 0.6313780928 dimensionless; f0 = NaN cell per_day | Reaction: N0 => N0 + N1, Rate Law: p01*f0 |
f0 = NaN cell per_day; p00 = NaN dimensionless | Reaction: N0 => N0, Rate Law: p00*f0 |
f1 = NaN cell per_day; p11 = NaN dimensionless | Reaction: N1 => N1, Rate Law: p11*f1 |
States:
Name | Description |
---|---|
N3 | N3 |
N1 | [stem cell] |
N0 | [stem cell] |
N2 | [stem cell] |
MODEL1303260000
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 00This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260001
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 01This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260002
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 02This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260003
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 03This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260004
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 04This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260005
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 05This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260006
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 06This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260007
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 07This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260008
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 08This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260009
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 09This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260010
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 10This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260011
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 11This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260012
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 12This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260013
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 13This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260014
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 14This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260015
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 15This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260016
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 16This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260017
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 17This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1303260018
— v0.0.1Smallbone2013 - Glycolysis in S.cerevisiae - Iteration 18This model is described in the article: [A model of yeast glyc…
Details
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought. link: http://identifiers.org/pubmed/23831062
MODEL1311110000
— v0.0.1Smallbone2013 - Human metabolism global reconstruction (recon 2.1)Recon 2.1. This model is described in the article: […
Details
Recon 2 is a highly curated reconstruction of the human metabolic network. Whilst the network is state of the art, it has shortcomings, including the presence of unbalanced reactions involving generic metabolites. By replacing these generic molecules with each of their specific instances, we can ensure full elemental balancing, in turn allowing constraint-based analyses to be performed. The resultant model, called Recon 2.1, is an order of magnitude larger than the original. link: http://arxiv.org/abs/1311.5696
MODEL1311110001
— v0.0.1Smallbone2013 - Human metabolism global reconstruction (recon 2.1x)Recon 2.1x. This model is described in the article:…
Details
Recon 2 is a highly curated reconstruction of the human metabolic network. Whilst the network is state of the art, it has shortcomings, including the presence of unbalanced reactions involving generic metabolites. By replacing these generic molecules with each of their specific instances, we can ensure full elemental balancing, in turn allowing constraint-based analyses to be performed. The resultant model, called Recon 2.1, is an order of magnitude larger than the original. link: http://arxiv.org/abs/1311.5696
BIOMD0000000454
— v0.0.1Smallbone2013 - Metabolic Control Analysis - Example 1Metabolic control analysis (MCA) is a biochemical formalism, defin…
Details
Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models. link: http://arxiv.org/pdf/1305.6449v1.pdf
Parameters:
Name | Description |
---|---|
p1=10.0 dimensionless; e1=1.0 dimensionless | Reaction: y1 + x2 => x1 + x3; y1, x2, x1, x3, Rate Law: e1*(p1*y1*x2-x1*x3)/(1+y1+x2+x1+x3+y1*x2+x1*x3) |
e3=1.0 dimensionless; p3=50.0 dimensionless | Reaction: x1 => y2; x1, y2, Rate Law: e3*(p3*x1-y2)/(1+x1+y2) |
e2=1.0 dimensionless; p2=10.0 dimensionless | Reaction: y4 + x3 => y5 + x2; y4, x3, y5, x2, Rate Law: e2*(p2*y4*x3-y5*x2)/(1+x3+x2+y4+y5+x3*y4+x2*y5) |
p4=10.0 dimensionless; e4=1.0 dimensionless | Reaction: x1 => y3; x1, y3, Rate Law: e4*(p4*x1-y3)/(1+x1+y3) |
States:
Name | Description |
---|---|
y3 | y3 |
x1 | x1 |
y4 | y4 |
y1 | y1 |
x2 | x2 |
y2 | y2 |
x3 | x3 |
y5 | y5 |
BIOMD0000000455
— v0.0.1Smallbone2013 - Metabolic Control Analysis - Example 2Metabolic control analysis (MCA) is a biochemical formalism, defin…
Details
Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models. link: http://arxiv.org/pdf/1305.6449v1.pdf
Parameters:
Name | Description |
---|---|
p1=10.0 dimensionless; e1=1.0 dimensionless | Reaction: y1 + x2 => x1 + x3; y1, x2, x1, x3, Rate Law: e1*(p1*y1*x2-x1*x3)/(1+y1+x2+x1+x3+y1*x2+x1*x3) |
e3=1.0 dimensionless; p3=50.0 dimensionless | Reaction: x1 => y2; x1, y2, Rate Law: e3*(p3*x1-y2)/(1+x1+y2) |
p4=10.0 dimensionless; e4=1.0 dimensionless | Reaction: x1 => y3; x1, y3, Rate Law: e4*(p4*x1-y3)/(1+x1+y3) |
e2=1.0 dimensionless; p2=10.0 dimensionless | Reaction: y4 + x3 => y5 + x2; y4, x3, y5, x2, Rate Law: e2*(p2*y4*x3-y5*x2)/(1+x3+x2+y4+y5+x3*y4+x2*y5) |
e5=1.0 dimensionless; p5=0.0 dimensionless | Reaction: x3 => y6; x3, Rate Law: e5*p5*x3 |
States:
Name | Description |
---|---|
y3 | y3 |
x1 | x1 |
y1 | y1 |
y4 | y4 |
x2 | x2 |
y2 | y2 |
y6 | y6 |
x3 | x3 |
y5 | y5 |
BIOMD0000000456
— v0.0.1Smallbone2013 - Metabolic Control Analysis - Example 3Metabolic control analysis (MCA) is a biochemical formalism, defin…
Details
Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models. link: http://arxiv.org/pdf/1305.6449v1.pdf
Parameters:
Name | Description |
---|---|
e6=1.0 dimensionless; p6=1.0 dimensionless | Reaction: y7 => x4; y7, Rate Law: e6*p6*y7 |
p1=10.0 dimensionless; e1=1.0 dimensionless | Reaction: y1 + x2 => x1 + x3; y1, x2, x1, x3, Rate Law: e1*(p1*y1*x2-x1*x3)/(1+y1+x2+x1+x3+y1*x2+x1*x3) |
e3=1.0 dimensionless; p3=50.0 dimensionless | Reaction: x1 => y2; x1, y2, Rate Law: e3*(p3*x1-y2)/(1+x1+y2) |
e2=1.0 dimensionless; p2=10.0 dimensionless | Reaction: y4 + x3 => y5 + x2; y4, x3, y5, x2, Rate Law: e2*(p2*y4*x3-y5*x2)/(1+x3+x2+y4+y5+x3*y4+x2*y5) |
p4=10.0 dimensionless; e4=1.0 dimensionless | Reaction: x1 => y3; x1, y3, Rate Law: e4*(p4*x1-y3)/(1+x1+y3) |
p7=1.0 dimensionless; e7=1.0 dimensionless | Reaction: x4 => y8, Rate Law: e7*p7 |
States:
Name | Description |
---|---|
y3 | y3 |
x4 | x4 |
x2 | x2 |
x3 | x3 |
y8 | y8 |
x1 | x1 |
y4 | y4 |
y1 | y1 |
y7 | y7 |
y2 | y2 |
y5 | y5 |
BIOMD0000000458
— v0.0.1Smallbone2013 - Serine biosynthesisKinetic modelling of metabolic pathways in application to Serine biosynthesis. This…
Details
In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway using kinetic data from both experimental measurements and literature review. Our methodology is presented by using the example of serine biosynthesis in E. coli. link: http://identifiers.org/pubmed/23417802
Parameters:
Name | Description |
---|---|
KAphp=0.0032 mM; KiAser=0.0038 mM; kcatA=0.55 per s; KAp3g=1.2 mM | Reaction: p3g => php; serA, ser, serA, p3g, php, ser, Rate Law: cell*serA*kcatA*p3g/KAp3g/(1+p3g/KAp3g+php/KAphp)/(1+ser/KiAser) |
kcatC=1.75 per s; KCpser=0.0017 mM; KCphp=0.0015 mM | Reaction: php => pser; serC, serC, php, pser, Rate Law: cell*serC*kcatC*php/KCphp/(1+php/KCphp+pser/KCpser) |
KBpser=0.0015 mM; KBser=0.15 mM; kcatB=1.43 per s | Reaction: pser => ser; serB, serB, pser, ser, Rate Law: cell*serB*kcatB*pser/KBpser/(1+pser/KBpser+ser/KBser) |
States:
Name | Description |
---|---|
p3g | [3-phosphonato-D-glycerate(3-)] |
ser | [L-serine] |
php | [3-phosphonatooxypyruvate(3-)] |
pser | [O-phosphonato-L-serine(2-)] |
BIOMD0000000831
— v0.0.1This a model from the article: Hypothalamic regulation of pituitary secretion of luteinizing hormone.II. Feedback cont…
Details
A general mathematical model describing the biochemical interactions of the hormones luteinizing hormone releasing hormone (LHRH), luteinizing hormone (LH) and testosterone (T) in the male is presented. The model structure consists of a negative feedback system of three ordinary differential equations, in which the quali]ative behavior is either a stable constant equilibrium solution or oscillatory solutions. A specific realization of the model is used to describe the experimental observations of pulsatile hormone release, its experimental suppression, the onset of puberty, the effects of castration, and several other qualitative and quantitative results. This model is presented as a first step in understanding the physi- cochemical interactions of the hypothalamic pituitary gonadal axis. link: http://identifiers.org/pubmed/6986927
Parameters:
Name | Description |
---|---|
g1 = 10.0 1/h | Reaction: => L; R, Rate Law: Compartment*g1*R |
h = 12.0 1/h; H = 1.0 1; c = 100.0 ng/(l*h) | Reaction: => R; T, Rate Law: Compartment*(c-h*T)*(1-H) |
b1 = 1.29 1/h | Reaction: R =>, Rate Law: Compartment*b1*R |
g2 = 0.7 1/h | Reaction: => T; L, Rate Law: Compartment*g2*L |
b3 = 1.39 1/h | Reaction: T =>, Rate Law: Compartment*b3*T |
b2 = 0.97 1/h | Reaction: L =>, Rate Law: Compartment*b2*L |
States:
Name | Description |
---|---|
T | [Thyroxine 5-deiodinase] |
L | [Luteinizing hormone] |
R | [Luteinizing hormone receptor] |
MODEL1006230000
— v0.0.1This a model from the article: Minimal haemodynamic system model including ventricular interaction and valve dynamics.…
Details
Characterising circulatory dysfunction and choosing a suitable treatment is often difficult and time consuming, and can result in a deterioration in patient condition, or unsuitable therapy choices. A stable minimal model of the human cardiovascular system (CVS) is developed with the ultimate specific aim of assisting medical staff for rapid, on site modelling to assist in diagnosis and treatment. Models found in the literature simulate specific areas of the CVS with limited direct usefulness to medical staff. Others model the full CVS as a closed loop system, but they were found to be very complex, difficult to solve, or unstable. This paper develops a model that uses a minimal number of governing equations with the primary goal of accurately capturing trends in the CVS dynamics in a simple, easily solved, robust model. The model is shown to have long term stability and consistency with non-specific initial conditions as a result. An "open on pressure close on flow" valve law is created to capture the effects of inertia and the resulting dynamics of blood flow through the cardiac valves. An accurate, stable solution is performed using a method that varies the number of states in the model depending on the specific phase of the cardiac cycle, better matching the real physiological conditions. Examples of results include a 9% drop in cardiac output when increasing the thoracic pressure from -4 to 0 mmHg, and an increase in blood pressure from 120/80 to 165/130 mmHg when the systemic resistance is doubled. These results show that the model adequately provides appropriate magnitudes and trends that are in agreement with existing data for a variety of physiologically verified test cases simulating human CVS function. link: http://identifiers.org/pubmed/15036180
BIOMD0000000439
— v0.0.1Smith2009 - RGS mediated GTP hydrolysisThis model is described in the article: [Dual positive and negative regulation o…
Details
G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increases the intrinsic GTPase activity of Galpha-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where Galpha(GTP) enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the Galpha produces this unimodal relationship. link: http://identifiers.org/pubmed/19285552
Parameters:
Name | Description |
---|---|
k1=0.0025 1/(nM*hr) | Reaction: R + L => RL; R, L, Rate Law: compartment*R*L*k1 |
k8=2.5 1/hr | Reaction: RGSGaGTP => GaGDPP + RGS; RGSGaGTP, Rate Law: compartment*RGSGaGTP*k8 |
k7=500.0 1/(nM*hr) | Reaction: GaGTP + RGS => RGSGaGTP; GaGTP, RGS, Rate Law: compartment*GaGTP*RGS*k7 |
k9=0.005 1/hr | Reaction: GaGTP => GaGDPP; GaGTP, Rate Law: compartment*GaGTP*k9 |
k16=1000.0 1/(nM*hr) | Reaction: GaGDP + Gbg => Gabg; GaGDP, Gbg, Rate Law: compartment*GaGDP*Gbg*k16 |
k15=1000.0 1/hr | Reaction: GaGDPP => GaGDP + P; GaGDPP, Rate Law: compartment*GaGDPP*k15 |
k4=0.005 1/(nM*hr) | Reaction: RGabg + L => RGabgL; RGabg, L, Rate Law: compartment*RGabg*L*k4 |
k11=1.0 1/hr | Reaction: GaGTPEffector => inertGaGTP + Effector; GaGTPEffector, Rate Law: compartment*GaGTPEffector*k11 |
k5=50.0 1/hr | Reaction: RGabgL => RL + GaGTP + Gbg; RGabgL, Rate Law: compartment*RGabgL*k5 |
k17=10.0 1/hr | Reaction: P => ; P, Rate Law: compartment*P*k17 |
k13=0.3 1/hr | Reaction: RGSinertGaGTP => GaGDPP + RGS; RGSinertGaGTP, Rate Law: compartment*RGSinertGaGTP*k13 |
k6=0.2 1/hr | Reaction: Gabg => GaGTP + Gbg; Gabg, Rate Law: compartment*Gabg*k6 |
k12=50.0 1/(nM*hr) | Reaction: inertGaGTP + RGS => RGSinertGaGTP; inertGaGTP, RGS, Rate Law: compartment*inertGaGTP*RGS*k12 |
k14=0.005 1/hr | Reaction: inertGaGTP => GaGDPP; inertGaGTP, Rate Law: compartment*inertGaGTP*k14 |
k3=0.02 1/(nM*hr) | Reaction: RL + Gabg => RGabgL; RL, Gabg, Rate Law: compartment*RL*Gabg*k3 |
k2=0.005 1/(nM*hr) | Reaction: R + Gabg => RGabg; R, Gabg, Rate Law: compartment*R*Gabg*k2 |
k10=10.0 1/(nM*hr) | Reaction: Effector + GaGTP => GaGTPEffector; Effector, GaGTP, Rate Law: compartment*Effector*GaGTP*k10 |
ka = 1.5 1/hr | Reaction: => z1; GaGTPEffector, GaGTPEffector, Rate Law: compartment*GaGTPEffector*ka |
States:
Name | Description |
---|---|
RGabg | [IPR000276; heterotrimeric G-protein complex] |
GaGTP | [GTP; Guanine nucleotide-binding protein G(t) subunit alpha-1] |
GaGDPP | [GDP; Guanine nucleotide-binding protein G(t) subunit alpha-1] |
inertGaGTP | [GTP; Guanine nucleotide-binding protein G(t) subunit alpha-1; inactive] |
RGS | [IPR000342] |
P | [phosphate(3-)] |
z1 | [SBO:0000347] |
RL | [receptor complex] |
L | L |
Gabg | [heterotrimeric G-protein complex] |
Gbg | [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(T) subunit gamma-T1] |
z3 | [SBO:0000347] |
GaGDP | [GDP; Guanine nucleotide-binding protein G(t) subunit alpha-1] |
GaGTPEffector | [GTP; Guanine nucleotide-binding protein G(t) subunit alpha-1; SBO:0000459] |
RGSGaGTP | [GTP; Guanine nucleotide-binding protein G(t) subunit alpha-1; IPR000342] |
RGSinertGaGTP | [GTP; Guanine nucleotide-binding protein G(t) subunit alpha-1; IPR000342; inactive] |
Effector | [SBO:0000459] |
RGabgL | [IPR000276; heterotrimeric G-protein complex; SBO:0000280] |
z2 | [SBO:0000347] |
R | [IPR000276] |
MODEL1112260002
— v0.0.1This a model from the article: Modelling the response of FOXO transcription factors to multiple post-translational mod…
Details
FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. The use of the ancillary program to generate the SBML is necessary because the possibility that many regulatory PTMs may be added, each independently of the others, means that a large number of chemically distinct forms of the FOXO molecule must be taken into account, and the program is used to generate them. Although the model does not yet include detailed representations of events upstream and downstream of FOXO, we show how it can qualitatively, and in some cases quantitatively, reproduce the known effects of certain treatments that induce various single and multiple PTMs, and allows for a complex spatiotemporal interplay of effects due to the activation of multiple PTM-inducing treatments. Thus, it provides an important framework to integrate current knowledge about the behaviour of FOXO. The approach should be generally applicable to other proteins experiencing multiple regulations. link: http://identifiers.org/pubmed/20567500
BIOMD0000000924
— v0.0.1Pneumococcal pneumonia is a leading cause of death and a major source of human morbidity. The initial immune response pl…
Details
Pneumococcal pneumonia is a leading cause of death and a major source of human morbidity. The initial immune response plays a central role in determining the course and outcome of pneumococcal disease. We combine bacterial titer measurements from mice infected with Streptococcus pneumoniae with mathematical modeling to investigate the coordination of immune responses and the effects of initial inoculum on outcome. To evaluate the contributions of individual components, we systematically build a mathematical model from three subsystems that describe the succession of defensive cells in the lung: resident alveolar macrophages, neutrophils and monocyte-derived macrophages. The alveolar macrophage response, which can be modeled by a single differential equation, can by itself rapidly clear small initial numbers of pneumococci. Extending the model to include the neutrophil response required additional equations for recruitment cytokines and host cell status and damage. With these dynamics, two outcomes can be predicted: bacterial clearance or sustained bacterial growth. Finally, a model including monocyte-derived macrophage recruitment by neutrophils suggests that sustained bacterial growth is possible even in their presence. Our model quantifies the contributions of cytotoxicity and immune-mediated damage in pneumococcal pathogenesis. link: http://identifiers.org/pubmed/21300073
Parameters:
Name | Description |
---|---|
eta = 1.33; N_max = 180000.0 | Reaction: => Neutrophils__N; proinflammatory_cytokine__C, Rate Law: compartment*eta*proinflammatory_cytokine__C*(1-Neutrophils__N/N_max) |
theta_M = 4.2E-8; d = 0.001; k_n = 1.4E-5; M_Astar = 1000000.0; alpha = 0.021; v = 0.029; kappa = 0.042 | Reaction: => proinflammatory_cytokine__C; Epithelial_cells_with_bacteria_attached__Ea, Neutrophils__N, Pneumococci___P, Rate Law: compartment*(alpha*Epithelial_cells_with_bacteria_attached__Ea/(1+k_n*Neutrophils__N)+v*theta_M*Pneumococci___P*M_Astar/(d+kappa+theta_M*Pneumococci___P*(1+k_n*Neutrophils__N))) |
d_E = 0.167 | Reaction: Epithelial_cells_with_bacteria_attached__Ea =>, Rate Law: compartment*d_E*Epithelial_cells_with_bacteria_attached__Ea |
d_C = 0.83 | Reaction: proinflammatory_cytokine__C =>, Rate Law: compartment*d_C*proinflammatory_cytokine__C |
d_NP = 1.76E-7; d_E = 0.167; rho1 = 0.15; d_N = 0.063; rho2 = 0.001; rho3 = 1.0E-5 | Reaction: => Debris__D; Neutrophils__N, Pneumococci___P, Epithelial_cells_with_bacteria_attached__Ea, Rate Law: compartment*(rho1*d_NP*Neutrophils__N*Pneumococci___P+rho2*d_N*Neutrophils__N+rho3*d_E*Epithelial_cells_with_bacteria_attached__Ea) |
d_D = 1.4E-7; M_Astar = 1000000.0 | Reaction: Debris__D =>, Rate Law: compartment*d_D*Debris__D*M_Astar |
f_P_M_A = 0.00249376558603491; gamma_N = 1.0E-5; k_d = 5.0E-9; M_Astar = 1000000.0; gamma_M_A = 5.6E-6 | Reaction: Pneumococci___P => ; Debris__D, Neutrophils__N, Rate Law: compartment*(gamma_M_A*f_P_M_A/(1+k_d*Debris__D*M_Astar)*M_Astar*Pneumococci___P+gamma_N*Neutrophils__N*Pneumococci___P) |
K_P = 3.41765197726012E8; r = 1.13 | Reaction: => Pneumococci___P, Rate Law: compartment*r*Pneumococci___P*(1-Pneumococci___P/K_P) |
omega = 2.1E-8 | Reaction: Susceptible_epithelial_cells__EU => ; Pneumococci___P, Rate Law: compartment*omega*Pneumococci___P*Susceptible_epithelial_cells__EU |
d_NP = 1.76E-7; d_N = 0.063 | Reaction: Neutrophils__N => ; Pneumococci___P, Rate Law: compartment*(d_NP*Neutrophils__N*Pneumococci___P+d_N*Neutrophils__N) |
States:
Name | Description |
---|---|
Pneumococci P | [C76384] |
Epithelial cells with bacteria attached Ea | [infected cell] |
Susceptible epithelial cells EU | [0006083] |
Neutrophils N | [0010527] |
proinflammatory cytokine C | [Cytokine] |
Debris D | [C120869] |
MODEL1106160000
— v0.0.1This model is from the article: A metabolic model of the mitochondrion and its use in modelling diseases of the tricar…
Details
Mitochondria are a vital component of eukaryotic cells and their dysfunction is implicated in a large number of metabolic, degenerative and age-related human diseases. The mechanism or these disorders can be difficult to elucidate due to the inherent complexity of mitochondrial metabolism. To understand how mitochondrial metabolic dysfunction contributes to these diseases, a metabolic model of a human heart mitochondrion was created.A new model of mitochondrial metabolism was built on the principle of metabolite availability using MitoMiner, a mitochondrial proteomics database, to evaluate the subcellular localisation of reactions that have evidence for mitochondrial localisation. Extensive curation and manual refinement was used to create a model called iAS253, containing 253 reactions, 245 metabolites and 89 transport steps across the inner mitochondrial membrane. To demonstrate the predictive abilities of the model, flux balance analysis was used to calculate metabolite fluxes under normal conditions and to simulate three metabolic disorders that affect the TCA cycle: fumarase deficiency, succinate dehydrogenase deficiency and α-ketoglutarate dehydrogenase deficiency.The results of simulations using the new model corresponded closely with phenotypic data under normal conditions and provided insight into the complicated and unintuitive phenotypes of the three disorders, including the effect of interventions that may be of therapeutic benefit, such as low glucose diets or amino acid supplements. The model offers the ability to investigate other mitochondrial disorders and can provide the framework for the integration of experimental data in future studies. link: http://identifiers.org/pubmed/21714867
BIOMD0000000474
— v0.0.1Smith2013 - Regulation of Insulin Signalling by Oxidative StressThe model describes insulin signalling (in rodent adipoc…
Details
Existing models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO.Insulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear.Our model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level.A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000.Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012insulinsignalling. link: http://identifiers.org/pubmed/23705851
Parameters:
Name | Description |
---|---|
k1 = 2.0E-5 | Reaction: Ins + InR => Ins_InR; InR, Ins, Rate Law: k1*Ins*extracellular*InR*cellsurface |
kminusr16a = 1.0E-6 | Reaction: AS160_P => AS160; PP2A, AS160_P, PP2A, Rate Law: cytoplasm*kminusr16a*PP2A*cytoplasm*AS160_P*cytoplasm/cytoplasm |
kminus9 = 0.0014; kminus9_basal = 2.7 | Reaction: PI345P3 => PIP2; PTEN, PI345P3, PTEN, Rate Law: cytoplasm*(kminus9_basal+kminus9*PTEN*cytoplasm)*PI345P3*cytoplasm/cytoplasm |
ktr=0.125 | Reaction: nucleus_Foxo1_Pa1_Pd0_Pe1_pUb0 => dnabound_Foxo1_Pa1_Pd0_Pe1_pUb0; nucleus_Foxo1_Pa1_Pd0_Pe1_pUb0, Rate Law: nucleus_Foxo1_Pa1_Pd0_Pe1_pUb0*nucleus*ktr |
kdeg=1.0E-4 | Reaction: cytoplasm_Foxo1_Pa1_Pd1_Pe1_pUb1 => degr_Foxo1; Proteasome, Proteasome, cytoplasm_Foxo1_Pa1_Pd1_Pe1_pUb1, Rate Law: cytoplasm*cytoplasm_Foxo1_Pa1_Pd1_Pe1_pUb1*cytoplasm*Proteasome*cytoplasm*kdeg/cytoplasm |
kkin=3.0E-4 | Reaction: cytoplasm_Foxo1_Pa0_Pd1_Pe1_pUb1 => cytoplasm_Foxo1_Pa1_Pd1_Pe1_pUb1; SGK, SGK, cytoplasm_Foxo1_Pa0_Pd1_Pe1_pUb1, Rate Law: cytoplasm*cytoplasm_Foxo1_Pa0_Pd1_Pe1_pUb1*cytoplasm*SGK*cytoplasm*kkin/cytoplasm |
k36f = 180.0 | Reaction: Mt => Mt + ROS; Mt, Rate Law: cytoplasm*k36f*Mt*cytoplasm/cytoplasm |
kub=6.6E-5 | Reaction: dnabound_Foxo1_Pa1_Pd1_Pe0_pUb0 => dnabound_Foxo1_Pa1_Pd1_Pe0_pUb1; SCF, SCF, dnabound_Foxo1_Pa1_Pd1_Pe0_pUb0, Rate Law: dnabound*dnabound_Foxo1_Pa1_Pd1_Pe0_pUb0*dnabound*SCF*cytoplasm*kub/dnabound |
ktr=0.055 | Reaction: nucleus_Foxo1_Pa0_Pd1_Pe1_pUb1 => cytoplasm_Foxo1_Pa0_Pd1_Pe1_pUb1; nucleus_Foxo1_Pa0_Pd1_Pe1_pUb1, Rate Law: nucleus_Foxo1_Pa0_Pd1_Pe1_pUb1*nucleus*ktr |
kub=1.0E-6 | Reaction: dnabound_Foxo1_Pa0_Pd0_Pe1_pUb0 => dnabound_Foxo1_Pa0_Pd0_Pe1_pUb1; SCF, SCF, dnabound_Foxo1_Pa0_Pd0_Pe1_pUb0, Rate Law: dnabound*dnabound_Foxo1_Pa0_Pd0_Pe1_pUb0*dnabound*SCF*cytoplasm*kub/dnabound |
k35f = 450.0 | Reaction: NOX => ROS + NOX; NOX, Rate Law: cytoplasm*k35f*NOX*cytoplasm/cytoplasm |
kub=3.0E-6 | Reaction: nucleus_Foxo1_Pa1_Pd0_Pe0_pUb0 => nucleus_Foxo1_Pa1_Pd0_Pe0_pUb1; SCF, SCF, nucleus_Foxo1_Pa1_Pd0_Pe0_pUb0, Rate Law: nucleus*nucleus_Foxo1_Pa1_Pd0_Pe0_pUb0*nucleus*SCF*cytoplasm*kub/nucleus |
kpdeg=0.0044 | Reaction: cytoplasm_InR => null; cytoplasm_InR, Rate Law: cytoplasm*cytoplasm_InR*cytoplasm*kpdeg/cytoplasm |
kminus12 = 1.25E-6 | Reaction: PKC_P => PKC; PP2A, PKC_P, PP2A, Rate Law: cytoplasm*kminus12*PP2A*cytoplasm*PKC_P*cytoplasm/cytoplasm |
kminus13 = 0.167 | Reaction: cellsurface_GLUT4 => cytoplasm_GLUT4; cellsurface_GLUT4, Rate Law: kminus13*cellsurface_GLUT4*cellsurface |
k_ros_perm = 4.81 | Reaction: ROS => extracellular_ROS; ROS, Rate Law: k_ros_perm*extracellular/cytoplasm*ROS*cytoplasm |
ktr=0.55 | Reaction: nucleus_Foxo1_Pa0_Pd1_Pe0_pUb0 => cytoplasm_Foxo1_Pa0_Pd1_Pe0_pUb0; nucleus_Foxo1_Pa0_Pd1_Pe0_pUb0, Rate Law: nucleus_Foxo1_Pa0_Pd1_Pe0_pUb0*nucleus*ktr |
ktr=0.0909090909091 | Reaction: cytoplasm_Foxo1_Pa0_Pd1_Pe0_pUb0 => nucleus_Foxo1_Pa0_Pd1_Pe0_pUb0; cytoplasm_Foxo1_Pa0_Pd1_Pe0_pUb0, Rate Law: cytoplasm_Foxo1_Pa0_Pd1_Pe0_pUb0*cytoplasm*ktr |
k30r = 0.005 | Reaction: PTP1B_ox + GSH => PTP1B + GSH; GSH, PTP1B_ox, Rate Law: cytoplasm*k30r*PTP1B_ox*cytoplasm*GSH*cytoplasm/cytoplasm |
k13 = 7.5E-6; k13_basal = 0.015 | Reaction: cytoplasm_GLUT4 => cellsurface_GLUT4; AS160_P, AS160_P, cytoplasm_GLUT4, Rate Law: (k13_basal+k13*AS160_P*cytoplasm)*cytoplasm_GLUT4*cytoplasm |
k32f = 6.0E-4 | Reaction: DUSP + ROS => DUSP_ox + ROS; DUSP, ROS, Rate Law: cytoplasm*k32f*DUSP*cytoplasm*ROS*cytoplasm/cytoplasm |
k31r = 0.002 | Reaction: PTEN_ox + GSH => PTEN + GSH; GSH, PTEN_ox, Rate Law: cytoplasm*k31r*PTEN_ox*cytoplasm*GSH*cytoplasm/cytoplasm |
by_jnk_phos_factor = 2.0; kkin=5.0E-5 | Reaction: dnabound_Foxo1_Pa0_Pd0_Pe0_pUb1 => dnabound_Foxo1_Pa0_Pd0_Pe1_pUb1; JNK_P, JNK_P, dnabound_Foxo1_Pa0_Pd0_Pe0_pUb1, Rate Law: dnabound*dnabound_Foxo1_Pa0_Pd0_Pe0_pUb1*dnabound*JNK_P*cytoplasm*by_jnk_phos_factor*kkin/dnabound |
kminus11 = 1.1878E-6 | Reaction: Akt_P2 => Akt; PP2A, Akt_P2, PP2A, Rate Law: cytoplasm*kminus11*PP2A*cytoplasm*Akt_P2*cytoplasm/cytoplasm |
k9 = 0.0055; k9_basal = 0.13145 | Reaction: PIP2 => PI345P3; IRS1_TyrP_PI3K, IRS1_TyrP_PI3K, PIP2, Rate Law: cytoplasm*(k9_basal+k9*IRS1_TyrP_PI3K*cytoplasm)*PIP2*cytoplasm/cytoplasm |
by_ikk_phos_factor = 3.0; kkin=5.0E-5 | Reaction: dnabound_Foxo1_Pa0_Pd0_Pe1_pUb1 => dnabound_Foxo1_Pa0_Pd1_Pe1_pUb1; IKK_P, IKK_P, dnabound_Foxo1_Pa0_Pd0_Pe1_pUb1, Rate Law: dnabound*dnabound_Foxo1_Pa0_Pd0_Pe1_pUb1*dnabound*IKK_P*cytoplasm*by_ikk_phos_factor*kkin/dnabound |
ktr=0.181818181818 | Reaction: cytoplasm_Foxo1_Pa0_Pd0_Pe0_pUb1 => nucleus_Foxo1_Pa0_Pd0_Pe0_pUb1; cytoplasm_Foxo1_Pa0_Pd0_Pe0_pUb1, Rate Law: cytoplasm_Foxo1_Pa0_Pd0_Pe0_pUb1*cytoplasm*ktr |
pip3_basal = 200.0; k12 = 3.5E-5 | Reaction: PKC => PKC_P; PI345P3, PI345P3, PKC, Rate Law: cytoplasm*k12*PKC*cytoplasm*piecewise(PI345P3*cytoplasm-pip3_basal, (PI345P3*cytoplasm) > pip3_basal, 0)/cytoplasm |
k31f = 2.7E-4 | Reaction: PTEN + ROS => PTEN_ox + ROS; PTEN, ROS, Rate Law: cytoplasm*k31f*PTEN*cytoplasm*ROS*cytoplasm/cytoplasm |
pip3_basal = 200.0; k11 = 2.5E-5 | Reaction: Akt => Akt_P2; PI345P3, Akt, PI345P3, Rate Law: cytoplasm*k11*Akt*cytoplasm*piecewise(PI345P3*cytoplasm-pip3_basal, (PI345P3*cytoplasm) > pip3_basal, 0)/cytoplasm |
ktranscr=0.24 | Reaction: null => nucleus_RNA_InR; dnabound_Foxo1_Pa1_Pd0_Pe0_pUb0, dnabound_Foxo1_Pa1_Pd0_Pe0_pUb0, Rate Law: dnabound_Foxo1_Pa1_Pd0_Pe0_pUb0*dnabound*ktranscr |
k_irs1_basal_degr = 0.001 | Reaction: IRS1 => NULL; IRS1, Rate Law: cytoplasm*IRS1*cytoplasm*k_irs1_basal_degr/cytoplasm |
kdephos=1.0E-6 | Reaction: dnabound_Foxo1_Pa1_Pd0_Pe1_pUb1 => dnabound_Foxo1_Pa0_Pd0_Pe1_pUb1; PP2A, PP2A, dnabound_Foxo1_Pa1_Pd0_Pe1_pUb1, Rate Law: dnabound*dnabound_Foxo1_Pa1_Pd0_Pe1_pUb1*dnabound*PP2A*cytoplasm*kdephos/dnabound |
States:
Name | Description |
---|---|
dnabound Foxo1 Pa1 Pd1 Pe1 pUb1 | [double-stranded DNA; Forkhead box protein O1] |
dnabound Foxo1 Pa1 Pd1 Pe1 pUb0 | [double-stranded DNA; Forkhead box protein O1] |
PIP2 | [phosphatidylinositol bisphosphate] |
PKC P | [phosphorylated; Atypical protein kinase C] |
PTEN | [Phosphatase and tensin homologPhosphatase and tensin homolog, isoform CRA_aProtein tyrosine phosphatase and tensin homolog/mutated in multiple advanced cancers proteinProtein tyrosine phosphatase and tensin-like protein] |
cytoplasm Foxo1 Pa0 Pd1 Pe0 pUb0 | [Forkhead box protein O1] |
Akt | [RAC-alpha serine/threonine-protein kinase] |
cytoplasm Foxo1 Pa1 Pd0 Pe1 pUb1 | [Forkhead box protein O1] |
cytoplasm Foxo1 Pa0 Pd0 Pe0 pUb1 | [Forkhead box protein O1] |
nucleus Foxo1 Pa1 Pd1 Pe1 pUb0 | [Forkhead box protein O1] |
nucleus Foxo1 Pa1 Pd0 Pe0 pUb0 | [Forkhead box protein O1] |
dnabound Foxo1 Pa1 Pd0 Pe1 pUb0 | [double-stranded DNA; Forkhead box protein O1] |
PTP1B ox | PTP1B_ox |
nucleus Foxo1 Pa1 Pd1 Pe1 pUb1 | [Forkhead box protein O1] |
cytoplasm InR | [Insulin receptor] |
cellsurface GLUT4 | [Solute carrier family 2, facilitated glucose transporter member 4] |
dnabound Foxo1 Pa0 Pd0 Pe1 pUb1 | [double-stranded DNA; Forkhead box protein O1] |
ROS | [reactive oxygen species] |
cytoplasm Foxo1 Pa0 Pd1 Pe1 pUb1 | [Forkhead box protein O1] |
NULL | NULL |
dnabound Foxo1 Pa0 Pd0 Pe0 pUb0 | [double-stranded DNA; Forkhead box protein O1] |
nucleus Foxo1 Pa1 Pd0 Pe1 pUb0 | [Forkhead box protein O1] |
PTEN ox | [oxidized; Phosphatase and tensin homologPhosphatase and tensin homolog, isoform CRA_aProtein tyrosine phosphatase and tensin homolog/mutated in multiple advanced cancers proteinProtein tyrosine phosphatase and tensin-like protein] |
nucleus RNA InR | [ribonucleic acid; Insulin receptor] |
dnabound Foxo1 Pa1 Pd1 Pe0 pUb0 | [double-stranded DNA; Forkhead box protein O1] |
Akt P2 | [phosphorylated; RAC-alpha serine/threonine-protein kinase] |
AS160 | [TBC1 domain family member 4] |
cytoplasm GLUT4 | [Solute carrier family 2, facilitated glucose transporter member 4] |
Ins | [Insulin-1] |
dnabound Foxo1 Pa1 Pd0 Pe0 pUb0 | [double-stranded DNA; Forkhead box protein O1] |
cytoplasm Foxo1 Pa1 Pd1 Pe0 pUb1 | [Forkhead box protein O1] |
cytoplasm Foxo1 Pa1 Pd1 Pe1 pUb1 | [Forkhead box protein O1] |
PKC | [Atypical protein kinase C] |
extracellular ROS | [extracellular region; reactive oxygen species] |
MODEL1812040005
— v0.0.1Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can re…
Details
Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can reduce the severity of SBIs, but many have limited efficacy or cause adverse effects. Thus, new treatment strategies are needed. Kinetic models describing the infection process can help determine optimal therapeutic targets, the time scale on which a drug will be most effective, and how infection dynamics will change under therapy. To understand how different therapies perturb the dynamics of influenza infection and bacterial coinfection and to quantify the benefit of increasing a drug's efficacy or targeting a different infection process, I analyzed data from mice treated with an antiviral, an antibiotic, or an immune modulatory agent with kinetic models. The results suggest that antivirals targeting the viral life cycle are most efficacious in the first 2 days of infection, potentially because of an improved immune response, and that increasing the clearance of infected cells is important for treatment later in the infection. For a coinfection, immunotherapy could control low bacterial loads with as little as 20 % efficacy, but more effective drugs would be necessary for high bacterial loads. Antibiotics targeting bacterial replication and administered 10 h after infection would require 100 % efficacy, which could be reduced to 40 % with prophylaxis. Combining immunotherapy with antibiotics could substantially increase treatment success. Taken together, the results suggest when and why some therapies fail, determine the efficacy needed for successful treatment, identify potential immune effects, and show how the regulation of underlying mechanisms can be used to design new therapeutic strategies. link: http://identifiers.org/pubmed/27679506
BIOMD0000000164
— v0.0.1The model reproduces the compartmental model for Ran transport as depicted in Fig 3 of the paper. Model reproduced using…
Details
The separate components of nucleocytoplasmic transport have been well characterized, including the key regulatory role of Ran, a guanine nucleotide triphosphatase. However, the overall system behavior in intact cells is difficult to analyze because the dynamics of these components are interdependent. We used a combined experimental and computational approach to study Ran transport in vivo. The resulting model provides the first quantitative picture of Ran flux between the nuclear and cytoplasmic compartments in eukaryotic cells. The model predicts that the Ran exchange factor RCC1, and not the flux capacity of the nuclear pore complex (NPC), is the crucial regulator of steady-state flux across the NPC. Moreover, it provides the first estimate of the total in vivo flux (520 molecules per NPC per second and predicts that the transport system is robust. link: http://identifiers.org/pubmed/11799242
Parameters:
Name | Description |
---|---|
RCC1Kcat=8.5 s^(-1); RCC1Km=1.1 0.001*dimensionless*m^(-3)*mol | Reaction: RanGDP_Nucleus => RanGTP_Nucleus; RCC1_Nucleus, NTF2_RanGDP_Nucleus, Rate Law: 0.75*RCC1Kcat*RCC1_Nucleus*RanGDP_Nucleus*1/(RCC1Km+RanGDP_Nucleus+NTF2_RanGDP_Nucleus)*Nucleus |
I=0.0 dimensionless*A*m^(-2); NTF2_RanGDP_Kperm=3.73333 1E-6*dimensionless*m*s^(-1) | Reaction: FNTF2_RanGDP_Cytosol => FNTF2_RanGDP_Nucleus, Rate Law: NTF2_RanGDP_Kperm*(FNTF2_RanGDP_Cytosol+(-FNTF2_RanGDP_Nucleus))*Nuc_membrane |
Koff_RanBP1_binding=0.5 s^(-1); Kon_RanBP1_binding=100.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: FCarrier_RanGTP_Cytosol + RanBP1_Cytosol => FRanBP1_Carrier_RanGTP_Cytosol, Rate Law: (Kon_RanBP1_binding*FCarrier_RanGTP_Cytosol*RanBP1_Cytosol+(-Koff_RanBP1_binding*FRanBP1_Carrier_RanGTP_Cytosol))*Cytosol |
I=0.0 dimensionless*A*m^(-2); Carrier_RanGTP_Kperm=0.173333 1E-6*dimensionless*m*s^(-1) | Reaction: Carrier_RanGTP_Cytosol => Carrier_RanGTP_Nucleus, Rate Law: Carrier_RanGTP_Kperm*(Carrier_RanGTP_Cytosol+(-Carrier_RanGTP_Nucleus))*Nuc_membrane |
Kon_RanGTP_Carrier_binding=100.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Koff_RanGTP_Carrier_binding=1.0 s^(-1) | Reaction: Carrier_Nucleus + FRanGTP_Nucleus => FCarrier_RanGTP_Nucleus, Rate Law: (Kon_RanGTP_Carrier_binding*Carrier_Nucleus*FRanGTP_Nucleus+(-Koff_RanGTP_Carrier_binding*FCarrier_RanGTP_Nucleus))*Nucleus |
Vmax_RanGTP_dephosphorylation_RanGTP_dephosphorylation = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km_RanGTP_dephosphorylation=0.43 0.001*dimensionless*m^(-3)*mol | Reaction: RanGTP_Cytosol => RanGDP_Cytosol; RanGAP_Cytosol, Rate Law: Vmax_RanGTP_dephosphorylation_RanGTP_dephosphorylation*RanGTP_Cytosol*1/(Km_RanGTP_dephosphorylation+RanGTP_Cytosol)*Cytosol |
Km_dephosphorylation=0.43 0.001*dimensionless*m^(-3)*mol; Vmax_dephosphorylation_dephosphorylationF = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: FRanBP1_Carrier_RanGTP_Cytosol => FRanGDP_Cytosol + RanBP1_Cytosol + Carrier_Cytosol; RanGAP_Cytosol, Rate Law: Vmax_dephosphorylation_dephosphorylationF*FRanBP1_Carrier_RanGTP_Cytosol*1/(Km_dephosphorylation+FRanBP1_Carrier_RanGTP_Cytosol)*Cytosol |
Koff_NTF2_RanGDP_unbinding=2.5 s^(-1); Kon_NTF2_RanGDP_unbinding=100.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: NTF2_RanGDP_Nucleus => RanGDP_Nucleus + NTF2_Nucleus, Rate Law: (Koff_NTF2_RanGDP_unbinding*NTF2_RanGDP_Nucleus+(-Kon_NTF2_RanGDP_unbinding*RanGDP_Nucleus*NTF2_Nucleus))*Nucleus |
I=0.0 dimensionless*A*m^(-2); Carrier_Kperm=1.86667 1E-6*dimensionless*m*s^(-1) | Reaction: Carrier_Cytosol => Carrier_Nucleus, Rate Law: Carrier_Kperm*(Carrier_Cytosol+(-Carrier_Nucleus))*Nuc_membrane |
RanGDP_Kperm=0.0 1E-6*dimensionless*m*s^(-1); I=0.0 dimensionless*A*m^(-2) | Reaction: FRanGDP_Cytosol => FRanGDP_Nucleus, Rate Law: RanGDP_Kperm*(FRanGDP_Cytosol+(-FRanGDP_Nucleus))*Nuc_membrane |
I=0.0 dimensionless*A*m^(-2); NTF2_Kperm=3.73333 1E-6*dimensionless*m*s^(-1) | Reaction: NTF2_Cytosol => NTF2_Nucleus, Rate Law: NTF2_Kperm*(NTF2_Cytosol+(-NTF2_Nucleus))*Nuc_membrane |
Koff_NTF2_RanGDP_binding=2.5 s^(-1); Kon_NTF2_RanGDP_binding=100.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: NTF2_Cytosol + FRanGDP_Cytosol => FNTF2_RanGDP_Cytosol, Rate Law: (Kon_NTF2_RanGDP_binding*NTF2_Cytosol*FRanGDP_Cytosol+(-Koff_NTF2_RanGDP_binding*FNTF2_RanGDP_Cytosol))*Cytosol |
RanGTP_Kperm=0.0 1E-6*dimensionless*m*s^(-1); I=0.0 dimensionless*A*m^(-2) | Reaction: RanGTP_Cytosol => RanGTP_Nucleus, Rate Law: RanGTP_Kperm*(RanGTP_Cytosol+(-RanGTP_Nucleus))*Nuc_membrane |
Vmax_RanGTP_dephosphorylation_FRanGTP_dephosphorylation = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km_RanGTP_dephosphorylation=0.43 0.001*dimensionless*m^(-3)*mol | Reaction: FRanGTP_Cytosol => FRanGDP_Cytosol; RanGAP_Cytosol, Rate Law: Vmax_RanGTP_dephosphorylation_FRanGTP_dephosphorylation*FRanGTP_Cytosol*1/(Km_RanGTP_dephosphorylation+FRanGTP_Cytosol)*Cytosol |
ar_for_Microinj = 0.0 | Reaction: => FRanGDP_Cytosol; Pipet_Cytosol, Rate Law: ar_for_Microinj*Cytosol*1 |
Koff_Carrier_RanGTP_binding=0.0 s^(-1); Kon_Carrier_RanGTP_binding=0.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: Carrier_Cytosol + FRanGTP_Cytosol => FCarrier_RanGTP_Cytosol, Rate Law: (Kon_Carrier_RanGTP_binding*Carrier_Cytosol*FRanGTP_Cytosol+(-Koff_Carrier_RanGTP_binding*FCarrier_RanGTP_Cytosol))*Cytosol |
Km_dephosphorylation=0.43 0.001*dimensionless*m^(-3)*mol; Vmax_dephosphorylation_dephosphorylation = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: RanBP1_Carrier_RanGTP_Cytosol => RanGDP_Cytosol + Carrier_Cytosol + RanBP1_Cytosol; RanGAP_Cytosol, Rate Law: Vmax_dephosphorylation_dephosphorylation*RanBP1_Carrier_RanGTP_Cytosol*1/(Km_dephosphorylation+RanBP1_Carrier_RanGTP_Cytosol)*Cytosol |
States:
Name | Description |
---|---|
Carrier Cytosol | Carrier_Cytosol |
FNTF2 RanGDP Cytosol | [Nuclear transport factor 2; GTP-binding nuclear protein Ran; GDP; GDP] |
RanGTP Nucleus | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
FRanGDP Cytosol | [GTP-binding nuclear protein Ran; GDP; GDP] |
Carrier RanGTP Nucleus | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
RanGDP Nucleus | [GTP-binding nuclear protein Ran; GDP; GDP] |
FRanGDP Nucleus | [GTP-binding nuclear protein Ran; GDP; GDP] |
NTF2 Nucleus | [Nuclear transport factor 2] |
NTF2 Cytosol | [Nuclear transport factor 2] |
Carrier RanGTP Cytosol | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
RanGTP Cytosol | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
NTF2 RanGDP Nucleus | [GTP-binding nuclear protein Ran; Nuclear transport factor 2; GDP; GDP] |
RanBP1 Carrier RanGTP Cytosol | [GTP; GTP-binding nuclear protein Ran; GTP] |
FNTF2 RanGDP Nucleus | [Nuclear transport factor 2; GTP-binding nuclear protein Ran; GDP; GDP] |
RanGDP Cytosol | [GTP-binding nuclear protein Ran; GDP; GDP] |
NTF2 RanGDP Cytosol | [Nuclear transport factor 2; GTP-binding nuclear protein Ran; GDP; GDP] |
FCarrier RanGTP Cytosol | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
FRanGTP Cytosol | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
FCarrier RanGTP Nucleus | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
FRanBP1 Carrier RanGTP Cytosol | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
FRanGTP Nucleus | [GTP; GTP-binding nuclear protein Ran; GTP-binding nuclear protein Ran; GTP; GTP] |
RanBP1 Cytosol | RanBP1_Cytosol |
Carrier Nucleus | Carrier_Nucleus |
BIOMD0000000025
— v0.0.1This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 T…
Details
Although several detailed models of molecular processes essential for circadian oscillations have been developed, their complexity makes intuitive understanding of the oscillation mechanism difficult. The goal of the present study was to reduce a previously developed, detailed model to a minimal representation of the transcriptional regulation essential for circadian rhythmicity in Drosophila. The reduced model contains only two differential equations, each with time delays. A negative feedback loop is included, in which PER protein represses per transcription by binding the dCLOCK transcription factor. A positive feedback loop is also included, in which dCLOCK indirectly enhances its own formation. The model simulated circadian oscillations, light entrainment, and a phase-response curve with qualitative similarities to experiment. Time delays were found to be essential for simulation of circadian oscillations with this model. To examine the robustness of the simplified model to fluctuations in molecule numbers, a stochastic variant was constructed. Robust circadian oscillations and entrainment to light pulses were simulated with fewer than 80 molecules of each gene product present on average. Circadian oscillations persisted when the positive feedback loop was removed. Moreover, elimination of positive feedback did not decrease the robustness of oscillations to stochastic fluctuations or to variations in parameter values. Such reduced models can aid understanding of the oscillation mechanisms in Drosophila and in other organisms in which feedback regulation of transcription may play an important role. link: http://identifiers.org/pubmed/12414672
Parameters:
Name | Description |
---|---|
kdp = 0.5 per_hr | Reaction: dClk => EmptySet, Rate Law: kdp*dClk*CELL |
K1 = 0.3 nM; dClkF_tau1 = NaN nM; Vsp = 0.5 nM_per_hr | Reaction: EmptySet => Per; dClkF, Rate Law: Vsp*dClkF_tau1/(K1+dClkF_tau1)*CELL |
kdc = 0.5 per_hr | Reaction: Per => EmptySet, Rate Law: kdc*Per*CELL |
K2 = 0.1 nM; Vsc = 0.25 nM_per_hr; dClkF_tau2 = NaN nM | Reaction: EmptySet => dClk; dClkF, Rate Law: CELL*Vsc*K2/(K2+dClkF_tau2) |
States:
Name | Description |
---|---|
dClkF | [Circadian locomoter output cycles protein kaput] |
dClk | [Period circadian protein; Circadian locomoter output cycles protein kaput] |
Per | [Period circadian protein] |
BIOMD0000000034
— v0.0.1No inititial conditions are specified in the paper. Because there is a basal rate of transcription for each gene, it doe…
Details
A model of Drosophila circadian rhythm generation was developed to represent feedback loops based on transcriptional regulation of per, Clk (dclock), Pdp-1, and vri (vrille). The model postulates that histone acetylation kinetics make transcriptional activation a nonlinear function of [CLK]. Such a nonlinearity is essential to simulate robust circadian oscillations of transcription in our model and in previous models. Simulations suggest that two positive feedback loops involving Clk are not essential for oscillations, because oscillations of [PER] were preserved when Clk, vri, or Pdp-1 expression was fixed. However, eliminating positive feedback by fixing vri expression altered the oscillation period. Eliminating the negative feedback loop in which PER represses per expression abolished oscillations. Simulations of per or Clk null mutations, of per overexpression, and of vri, Clk, or Pdp-1 heterozygous null mutations altered model behavior in ways similar to experimental data. The model simulated a photic phase-response curve resembling experimental curves, and oscillations entrained to simulated light-dark cycles. Temperature compensation of oscillation period could be simulated if temperature elevation slowed PER nuclear entry or PER phosphorylation. The model makes experimental predictions, some of which could be tested in transgenic Drosophila. link: http://identifiers.org/pubmed/15111397
Parameters:
Name | Description |
---|---|
parameter_0000048 = 0.00531 | Reaction: species_0000001 =>, Rate Law: compartment_0000001*parameter_0000048*species_0000001 |
parameter_0000043 = 0.001; parameter_0000042 = 0.3186 | Reaction: species_0000001 => species_0000002, Rate Law: compartment_0000001*parameter_0000042*species_0000001/(parameter_0000043+species_0000001) |
parameter_0000010 = 0.54; parameter_0000008 = 0.54; parameter_0000030 = 1.062; parameter_0000033 = 0.001062 | Reaction: => species_0000008; species_0000009, species_0000007, Rate Law: compartment_0000001*(parameter_0000030*species_0000009^2/(species_0000009^2+parameter_0000010^2)*parameter_0000008^2/(species_0000007^2+parameter_0000008^2)+parameter_0000033) |
parameter_0000040 = 1.6992; parameter_0000041 = 0.25 | Reaction: species_0000004 => species_0000005, Rate Law: compartment_0000002*parameter_0000040*species_0000004/(parameter_0000041+species_0000004) |
parameter_0000027 = 10.62; parameter_0000031 = 0.02124; parameter_0000021 = NaN | Reaction: => species_0000004, Rate Law: compartment_0000002*(parameter_0000027*parameter_0000021+parameter_0000031) |
parameter_0000037 = 0.7434 | Reaction: species_0000007 =>, Rate Law: compartment_0000001*parameter_0000037*species_0000007 |
parameter_0000038 = 0.6903 | Reaction: species_0000009 =>, Rate Law: compartment_0000001*parameter_0000038*species_0000009 |
parameter_0000046 = 5.31; parameter_0000047 = 0.01 | Reaction: species_0000003 =>, Rate Law: compartment_0000001*parameter_0000046*species_0000003/(parameter_0000047+species_0000003) |
parameter_0000044 = 1.6992; parameter_0000045 = 0.25 | Reaction: species_0000006 => species_0000001, Rate Law: compartment_0000002*parameter_0000044*species_0000006/(parameter_0000045+species_0000006) |
parameter_0000020 = NaN; parameter_0000028 = 76.464; parameter_0000032 = 0.19116 | Reaction: => species_0000007, Rate Law: compartment_0000001*(parameter_0000028*parameter_0000020+parameter_0000032) |
parameter_0000036 = 0.2124 | Reaction: species_0000008 =>, Rate Law: compartment_0000001*parameter_0000036*species_0000008 |
parameter_0000029 = 344.09; parameter_0000022 = NaN; parameter_0000034 = 0.38232; parameter_0000039 = 2.8249 | Reaction: => species_0000009, Rate Law: compartment_0000001*delay(parameter_0000029*parameter_0000022+parameter_0000034, parameter_0000039) |
States:
Name | Description |
---|---|
species 0000008 | [Circadian locomoter output cycles protein kaput; Period circadian protein] |
species 0000005 | [Period circadian protein] |
species 0000002 | [Period circadian protein] |
species 0000003 | [Period circadian protein] |
species 0000001 | [Period circadian protein] |
species 0000007 | [BZIP transcription factor] |
species 0000004 | [Period circadian protein] |
species 0000009 | [PAR domain protein 1-epislonPAR domain protein 1-epsilon] |
species 0000006 | [Period circadian protein] |
BIOMD0000000853
— v0.0.1This is a mathematical model describing the formation of long-term potentiation (LTP) at the Schaffer collateral of CA1…
Details
The transition from early long-term potentiation (E-LTP) to late long-term potentiation (L-LTP) is a multistep process that involves both protein synthesis and degradation. The ways in which these two opposing processes interact to establish L-LTP are not well understood, however. For example, L-LTP is attenuated by inhibiting either protein synthesis or proteasome-dependent degradation prior to and during a tetanic stimulus (e.g., Huang et al., 1996; Karpova et al., 2006), but paradoxically, L-LTP is not attenuated when synthesis and degradation are inhibited simultaneously (Fonseca et al., 2006). These paradoxical results suggest that counter-acting 'positive' and 'negative' proteins regulate L-LTP. To investigate the basis of this paradox, we developed a model of LTP at the Schaffer collateral to CA1 pyramidal cell synapse. The model consists of nine ordinary differential equations that describe the levels of both positive- and negative-regulator proteins (PP and NP, respectively) and the transitions among five discrete synaptic states, including a basal state (BAS), three states corresponding to E-LTP (EP1, EP2, and ED), and a L-LTP state (LP). An LTP-inducing stimulus: 1) initiates the transition from BAS to EP1 and from EP1 to EP2; 2) initiates the synthesis of PP and NP; and finally; 3) activates the ubiquitin-proteasome system (UPS), which in turn, mediates transitions of EP1 and EP2 to ED and the degradation of NP. The conversion of E-LTP to L-LTP is mediated by the PP-dependent transition from ED to LP, whereas NP mediates reversal of EP2 to BAS. We found that the inclusion of the five discrete synaptic states was necessary to simulate key empirical observations: 1) normal L-LTP, 2) block of L-LTP by either proteasome inhibitor or protein synthesis inhibitor alone, and 3) preservation of L-LTP when both inhibitors are applied together. Although our model is abstract, elements of the model can be correlated with specific molecular processes. Moreover, the model correctly captures the dynamics of protein synthesis- and degradation-dependent phases of LTP, and it makes testable predictions, such as a unique synaptic state (ED) that precedes the transition from E-LTP to L-LTP, and a well-defined time window for the action of the UPS (i.e., during the transitions from EP1 and EP2 to ED). Tests of these predictions will provide new insights into the processes and dynamics of long-term synaptic plasticity. link: http://identifiers.org/pubmed/30138630
Parameters:
Name | Description |
---|---|
kdeg2 = 0.01; LAC = 0.0 | Reaction: NP => ; UPS, Rate Law: compartment*kdeg2*UPS*NP*(1-LAC) |
PSI = 0.0; STIM = 1.0; ksyn2 = 2.0; ksyn2bas = 0.0014 | Reaction: => NP, Rate Law: compartment*(1-PSI)*(ksyn2*STIM+ksyn2bas) |
kdeg3 = 0.02 | Reaction: STAB =>, Rate Law: compartment*kdeg3*STAB |
kb3 = 0.02 | Reaction: ED => BAS, Rate Law: compartment*kb3*ED |
kf4 = 0.02; LAC = 0.0 | Reaction: EP2 => ED; UPS, Rate Law: compartment*kf4*UPS*(1-LAC)*EP2 |
ksyn1 = 1.0; PSI = 0.0; ksyn1bas = 0.0035; STIM = 1.0 | Reaction: => PP, Rate Law: compartment*(1-PSI)*(ksyn1*STIM+ksyn1bas) |
ksyn3 = 1.0; STIM = 1.0 | Reaction: => STAB, Rate Law: compartment*ksyn3*STIM |
kf1bas = 0.0; STIM = 1.0 | Reaction: BAS => EP1, Rate Law: compartment*kf1bas*(1-STIM)*BAS |
kb1 = 0.005 | Reaction: EP1 => BAS, Rate Law: compartment*kb1*EP1 |
kdeact = 0.0143 | Reaction: UPS =>, Rate Law: compartment*kdeact*UPS |
kactbas = 0.00214 | Reaction: => UPS, Rate Law: compartment*kactbas |
kf3 = 0.01 | Reaction: EP1 => EP2; STAB, Rate Law: compartment*kf3*STAB*EP1 |
kact = 0.2; STIM = 1.0 | Reaction: => UPS, Rate Law: compartment*kact*STIM |
kf2 = 0.02; LAC = 0.0 | Reaction: EP1 => ED; UPS, Rate Law: compartment*kf2*UPS*(1-LAC)*EP1 |
STIM = 1.0; kf1 = 0.1 | Reaction: BAS => EP1, Rate Law: compartment*kf1*STIM*BAS |
ksyn3bas = 0.008 | Reaction: => STAB, Rate Law: compartment*ksyn3bas |
kb4 = 0.001 | Reaction: LP => BAS, Rate Law: compartment*kb4*LP |
kf5 = 5.0E-4 | Reaction: ED => LP; PP, Rate Law: compartment*kf5*PP^2*ED |
kb2 = 7.0E-4 | Reaction: EP2 => BAS; NP, Rate Law: compartment*kb2*EP2*NP |
kdeg1 = 0.005 | Reaction: PP =>, Rate Law: compartment*kdeg1*PP |
kdeg2bas = 0.002 | Reaction: NP =>, Rate Law: compartment*kdeg2bas*NP |
States:
Name | Description |
---|---|
ED | [C13281; C61589] |
NP | [Protein; Inhibitor] |
EP1 | [C13281; C61589] |
PP | [Protein; SBO:0000459] |
UPS | [PW:0000144] |
LP | [C13281; C25322] |
BAS | [C13281; C90067] |
EP2 | [C13281; C61589] |
STAB | [PR:000009238] |
BIOMD0000000548
— v0.0.1Sneppen2009 - Modeling proteasome dynamics in Parkinson's diseaseThis model is described in the article: [Modeling prot…
Details
In Parkinson's disease (PD), there is evidence that alpha-synuclein (alphaSN) aggregation is coupled to dysfunctional or overburdened protein quality control systems, in particular the ubiquitin-proteasome system. Here, we develop a simple dynamical model for the on-going conflict between alphaSN aggregation and the maintenance of a functional proteasome in the healthy cell, based on the premise that proteasomal activity can be titrated out by mature alphaSN fibrils and their protofilament precursors. In the presence of excess proteasomes the cell easily maintains homeostasis. However, when the ratio between the available proteasome and the alphaSN protofilaments is reduced below a threshold level, we predict a collapse of homeostasis and onset of oscillations in the proteasome concentration. Depleted proteasome opens for accumulation of oligomers. Our analysis suggests that the onset of PD is associated with a proteasome population that becomes occupied in periodic degradation of aggregates. This behavior is found to be the general state of a proteasome/chaperone system under pressure, and suggests new interpretations of other diseases where protein aggregation could stress elements of the protein quality control system. link: http://identifiers.org/pubmed/19411740
Parameters:
Name | Description |
---|---|
m = 25.0; gamma = 1.0 | Reaction: F = m/(1+P)-gamma*F*P, Rate Law: m/(1+P)-gamma*F*P |
sigma = 1.0; nu = 1.0; gamma = 1.0 | Reaction: P = ((sigma-P)-gamma*F*P)+nu*C, Rate Law: ((sigma-P)-gamma*F*P)+nu*C |
nu = 1.0; gamma = 1.0 | Reaction: C = gamma*F*P-nu*C, Rate Law: gamma*F*P-nu*C |
States:
Name | Description |
---|---|
P | [proteasome complex] |
C | [Alpha-synuclein; proteasome complex; supramolecular fiber] |
F | [supramolecular fiber; Alpha-synuclein] |
MODEL1006230107
— v0.0.1This a model from the article: Intercellular calcium waves mediated by diffusion of inositol trisphosphate: a two-dime…
Details
In response to mechanical stimulation of a single cell, airway epithelial cells in culture exhibit a wave of increased intracellular free Ca2+ concentration that spreads from cell to cell over a limited distance through the culture. We present a detailed analysis of the intercellular wave in a two-dimensional sheet of cells. The model is based on the hypothesis that the wave is the result of diffusion of inositol trisphosphate (IP3) from the stimulated cell. The two-dimensional model agrees well with experimental data and makes the following quantitative predictions: as the distance from the stimulated cells increases, 1) the intercellular delay increases exponentially, 2) the intracellular wave speed decreases exponentially, and 3) the arrival time increases exponentially. Furthermore, 4) a proportion of the cells at the periphery of the response will exhibit waves of decreased amplitude, 5) the intercellular membrane permeability to IP3 must be approximately 2 microns/s or greater, and 6) the ratio of the maximum concentration of IP3 in the stimulated cell to the Km of the IP3 receptor (with respect to IP3) must be approximately 300 or greater. These predictions constitute a rigorous test of the hypothesis that the intercellular Ca2+ waves are mediated by IP3 diffusion. link: http://identifiers.org/pubmed/7611375
BIOMD0000000057
— v0.0.1This model was successfully tested on Jarnac and MathSBML. The model reproduces the time profile of "Open Probability" o…
Details
The dynamic properties of the inositol (1,4,5)-trisphosphate (IP(3)) receptor are crucial for the control of intracellular Ca(2+), including the generation of Ca(2+) oscillations and waves. However, many models of this receptor do not agree with recent experimental data on the dynamic responses of the receptor. We construct a model of the IP(3) receptor and fit the model to dynamic and steady-state experimental data from type-2 IP(3) receptors. Our results indicate that, (i) Ca(2+) binds to the receptor using saturating, not mass-action, kinetics; (ii) Ca(2+) decreases the rate of IP(3) binding while simultaneously increasing the steady-state sensitivity of the receptor to IP(3); (iii) the rate of Ca(2+)-induced receptor activation increases with Ca(2+) and is faster than Ca(2+)-induced receptor inactivation; and (iv) IP(3) receptors are sequentially activated and inactivated by Ca(2+) even when IP(3) is bound. Our results emphasize that measurement of steady-state properties alone is insufficient to characterize the functional properties of the receptor. link: http://identifiers.org/pubmed/11842185
Parameters:
Name | Description |
---|---|
lminus2 = 0.8; kminus1 = 0.04; lminus2=0.8; kminus1=0.04; Phi5 = 0.0 | Reaction: A => I2, Rate Law: compartment*(Phi5*A-(kminus1+lminus2)*I2) |
kminus3 = 29.8; kminus3=29.8; Phi3 = 0.0 | Reaction: O => S, Rate Law: compartment*(Phi3*O-kminus3*S) |
Phi4 = 0.0; Phi_minus4 = 0.0 | Reaction: O => A, Rate Law: compartment*(Phi4*O-Phi_minus4*A) |
lminus2 = 0.8; kminus1 = 0.04; Phi1 = 0.0; lminus2=0.8; kminus1=0.04 | Reaction: R => I1, Rate Law: compartment*(Phi1*R-(kminus1+lminus2)*I1) |
IP3=10.0; IP3 = 10.0; Phi_minus2 = 0.0; Phi2 = 0.0 | Reaction: R => O, Rate Law: compartment*(Phi2*IP3*R-Phi_minus2*O) |
States:
Name | Description |
---|---|
I1 | [IPR000493] |
I2 | [IPR000493] |
S | [IPR000493] |
A | [IPR000493] |
R | [IPR000493] |
O | [IPR000493] |
MODEL7896869925
— v0.0.1This a model from the article: Mathematical modelling of prolactin-receptor interaction and the corollary for prolacti…
Details
A mathematical model of prolactin regulating its own receptors was developed, and compared with experimental data on a qualitative level. The model incorporates the kinetics of prolactin-receptor interactions and subsequent signalling by prolactin-receptor dimers to regulate the production of receptor mRNA and hence the receptor population. The model relates changes in plasma prolactin concentration to prolactin receptor (PRLR) gene expression, and can be used for predictive purposes. The cell signalling that leads to the activation of target genes, and the mechanisms for regulation of transcription, were treated empirically in the model. The model's parameters were adjusted so that model simulations agreed with experimentally observed responses to administration of prolactin in sheep. In particular, the model correctly predicts insensitivity of receptor mRNA regulation to a series of subcutaneous injections of prolactin, versus sensitivity to prolonged infusion of prolactin. In the latter case, response was an acute down-regulation followed by a prolonged up-regulation of mRNA, with the magnitude of the up-regulation increasing with the duration of infusion period. The model demonstrates the feasibility of predicting the in vivo response of prolactin target genes to external manipulation of plasma prolactin, and could provide a useful tool for identifying optimal prolactin treatments for desirable outcomes. link: http://identifiers.org/pubmed/15757685
MODEL1507180050
— v0.0.1Sohn2010 - Genome-scale metabolic network of Pichia pastoris (PpaMBEL1254)This model is described in the article: [Geno…
Details
The methylotrophic yeast Pichia pastoris has gained much attention during the last decade as a platform for producing heterologous recombinant proteins of pharmaceutical importance, due to its ability to reproduce post-translational modification similar to higher eukaryotes. With the recent release of the full genome sequence for P. pastoris, in-depth study of its functions has become feasible. Here we present the first reconstruction of the genome-scale metabolic model of the eukaryote P. pastoris type strain DSMZ 70382, PpaMBEL1254, consisting of 1254 metabolic reactions and 1147 metabolites compartmentalized into eight different regions to represent organelles. Additionally, equations describing the production of two heterologous proteins, human serum albumin and human superoxide dismutase, were incorporated. The protein-producing model versions of PpaMBEL1254 were then analyzed to examine the impact on oxygen limitation on protein production. link: http://identifiers.org/pubmed/20503221
MODEL1507180043
— v0.0.1Sohn2010 - Genome-scale metabolic network of Pseudomonas putida (PpuMBEL1071)This model is described in the article: [I…
Details
Genome-scale metabolic models have been appearing with increasing frequency and have been employed in a wide range of biotechnological applications as well as in biological studies. With the metabolic model as a platform, engineering strategies have become more systematic and focused, unlike the random shotgun approach used in the past. Here we present the genome-scale metabolic model of the versatile Gram-negative bacterium Pseudomonas putida, which has gained widespread interest for various biotechnological applications. With the construction of the genome-scale metabolic model of P. putida KT2440, PpuMBEL1071, we investigated various characteristics of P. putida, such as its capacity for synthesizing polyhydroxyalkanoates (PHA) and degrading aromatics. Although P. putida has been characterized as a strict aerobic bacterium, the physiological characteristics required to achieve anaerobic survival were investigated. Through analysis of PpuMBEL1071, extended survival of P. putida under anaerobic stress was achieved by introducing the ackA gene from Pseudomonas aeruginosa and Escherichia coli. link: http://identifiers.org/pubmed/20540110
MODEL1507180061
— v0.0.1Sohn2012 - Genome-scale metabolic network of Schizosaccharomyces pombe (SpoMBEL1693)This model is described in the artic…
Details
BACKGROUND: Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes. RESULTS: Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype). CONCLUSION: This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model's predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data. link: http://identifiers.org/pubmed/22631437
BIOMD0000000903
— v0.0.1A fractional mathematical model of breast cancer competition model Author links open overlay panelJ.E.Solís-PérezaJ.F.Gó…
Details
In this paper, a mathematical model which considers population dynamics among cancer stem cells, tumor cells, healthy cells, the effects of excess estrogen and the body’s natural immune response on the cell populations was considered. Fractional derivatives with power law and exponential decay law in Liouville–Caputo sense were considered. Special solutions using an iterative scheme via Laplace transform were obtained. Furthermore, numerical simulations of the model considering both derivatives were obtained using the Atangana–Toufik numerical method. Also, random model described by a system of random differential equations was presented. The use of fractional derivatives provides more useful information about the complexity of the dynamics of the breast cancer competition model.
Volume 127, October 2019, Pages 38-54 link: http://identifiers.org/doi/10.1016/j.chaos.2019.06.027
Parameters:
Name | Description |
---|---|
a3 = 1250000.0; p3 = 100.0; delta = 6.0E-8 | Reaction: H => ; T, E, Rate Law: compartment*(delta*H*T+p3*H*E/(a3+H)) |
gamma1 = 3.0E-7 | Reaction: C => ; I, Rate Law: compartment*gamma1*I*C |
tau = 2700.0 | Reaction: => E, Rate Law: compartment*tau |
q = 0.7; M3 = 2.5E7 | Reaction: => H, Rate Law: compartment*q*H*(1-H/M3) |
a2 = 1.135E7; mu = 0.97; a3 = 1250000.0; a1 = 1135000.0; d1 = 0.01; d2 = 0.01; d3 = 0.01 | Reaction: E => ; C, T, H, Rate Law: compartment*(mu+d1*C/(a1+C)+d2*T/(a2+T)+d3*H/(a3+H))*E |
gamma2 = 3.0E-6; n1 = 0.01 | Reaction: T => ; I, Rate Law: compartment*(n1*T+gamma2*I*T) |
s = 13000.0; w = 300000.0; p = 0.2 | Reaction: => I; T, Rate Law: compartment*(s+p*I*T/(w+T)) |
p1 = 600.0; M1 = 2270000.0; k1 = 0.75; a1 = 1135000.0 | Reaction: => C; E, Rate Law: compartment*(k1*C*(1-C/M1)+p1*C*E/(a1+C)) |
p2 = 0.0; M2 = 2.27E7; a2 = 1.135E7; M1 = 2270000.0; k2 = 0.514 | Reaction: => T; C, E, Rate Law: compartment*(k2*C*C/M1*(1-T/M2)+p2*T*E/(a2+T)) |
gamma3 = 1.0E-7; v = 400.0; u = 0.2; n2 = 0.29 | Reaction: I => ; T, E, Rate Law: compartment*(gamma3*I*T+n2*I+u*I*E/(v+E)) |
States:
Name | Description |
---|---|
I | [Immune Cell] |
T | [Neoplastic Cell] |
C | [BTO:0006293] |
E | E |
H | [Healthy] |
BIOMD0000000114
— v0.0.1This model encoded according to the paper *Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Sim…
Details
Hormone-induced oscillations of the free intracellular calcium concentration are thought to be relevant for frequency encoding of hormone signals. In liver cells, such Ca2+ oscillations occur in response to stimulation by hormones acting via phosphoinositide breakdown. This observation may be explained by cooperative, positive feedback of Ca2+ on its own release from one inositol 1,4,5-trisphosphate-sensitive pool, obviating oscillations of inositol 1,4,5-trisphosphate. The kinetic rate laws of the associated model have a mathematical structure reminiscent of the Brusselator, a hypothetical chemical model involving a rather improbable trimolecular reaction step, thus giving a realistic biological interpretation to this hallmark of dissipative structures. We propose that calmodulin is involved in mediating this cooperativity and positive feedback, as suggested by the presented experiments. For one, hormone-induced calcium oscillations can be inhibited by the (nonphenothiazine) calmodulin antagonists calmidazolium or CGS 9343 B. Alternatively, in cells overstimulated by hormone, as characterized by a non-oscillatory elevated Ca2+ concentration, these antagonists could again restore sustained calcium oscillations. The experimental observations, including modulation of the oscillations by extracellular calcium, were in qualitative agreement with the predictions of our mathematical model. link: http://identifiers.org/pubmed/1904060
Parameters:
Name | Description |
---|---|
alpha = 5.0; fy = NaN | Reaction: x => y, Rate Law: alpha*fy*x*cytoplasm |
k = 0.01; k1 = 2.0 | Reaction: x => y, Rate Law: k*x*cytoplasm-k1*y*ER |
beta = 1.0 | Reaction: y =>, Rate Law: beta*y*extracellular |
gamma = 1.0 | Reaction: => y, Rate Law: gamma*cytoplasm |
States:
Name | Description |
---|---|
x | [calcium(2+); Calcium cation] |
y | [calcium(2+); Calcium cation] |
BIOMD0000000115
— v0.0.1Another model from *Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simply One Pool Model.* A…
Details
Hormone-induced oscillations of the free intracellular calcium concentration are thought to be relevant for frequency encoding of hormone signals. In liver cells, such Ca2+ oscillations occur in response to stimulation by hormones acting via phosphoinositide breakdown. This observation may be explained by cooperative, positive feedback of Ca2+ on its own release from one inositol 1,4,5-trisphosphate-sensitive pool, obviating oscillations of inositol 1,4,5-trisphosphate. The kinetic rate laws of the associated model have a mathematical structure reminiscent of the Brusselator, a hypothetical chemical model involving a rather improbable trimolecular reaction step, thus giving a realistic biological interpretation to this hallmark of dissipative structures. We propose that calmodulin is involved in mediating this cooperativity and positive feedback, as suggested by the presented experiments. For one, hormone-induced calcium oscillations can be inhibited by the (nonphenothiazine) calmodulin antagonists calmidazolium or CGS 9343 B. Alternatively, in cells overstimulated by hormone, as characterized by a non-oscillatory elevated Ca2+ concentration, these antagonists could again restore sustained calcium oscillations. The experimental observations, including modulation of the oscillations by extracellular calcium, were in qualitative agreement with the predictions of our mathematical model. link: http://identifiers.org/pubmed/1904060
Parameters:
Name | Description |
---|---|
alpha = 10.0; fy = NaN | Reaction: x => y, Rate Law: alpha*fy*(x-y)*Cytosol |
k = 0.01 | Reaction: x => y, Rate Law: Cytosol*k*(x-y) |
k1 = 2.0 | Reaction: y => x, Rate Law: k1*y*ER |
beta = 1.0 | Reaction: y =>, Rate Law: beta*y*Extracellular |
gamma = 1.0 | Reaction: => y, Rate Law: gamma*Cytosol |
States:
Name | Description |
---|---|
x | [calcium(2+); Calcium cation] |
y | [calcium(2+); Calcium cation] |
BIOMD0000000873
— v0.0.1IL-6 has been proposed to favor the development of Th2 responses and play an important role in the communication between…
Details
IL-6 has been proposed to favor the development of Th2 responses and play an important role in the communication between cells of multicellular organisms. They are involved in the regulation of complex cellular processes such as proliferation, differentiation and act as key player during inflammation and immune response. Th2 cytokines play an immunoregulatory role in early infection. Literature says in mice infected with L. major, IL-6 may promote the development of both Th1 and Th2 responses. IL-4 is also considered to be the signature cytokine of Th-2 response. IL-10 was initially characterized as a Th2 cytokine but later on it was proved to be a pleiotropic cytokine, secreted from different cell types including the macrophages. A major challenge is to understand how these complex non-linear processes are connected and regulated. Systems biology approaches may be used to tackle this challenge in an iterative process of quantitative mathematical analysis. In this study, we created an in silico model of IL6 mediated macrophage activation which suffers from an excessive impact of the negative feedback loop involving SOCS3. The strategy adopted in this framework may help to reduce the complexity of the leishmanial IL6 model analysis and also laydown various physiological or pathological conditions of IL6 signaling in future. link: http://identifiers.org/pubmed/29128405
Parameters:
Name | Description |
---|---|
mwce7a28b5_2f60_4228_a88f_ccd3a6213c3e=0.23 | Reaction: mw6339814d_af4c_4eee_9455_7e20795f6aeb => mwc42127ea_2b78_4381_b230_30e95cd5a9d6, Rate Law: mwce7a28b5_2f60_4228_a88f_ccd3a6213c3e*mw6339814d_af4c_4eee_9455_7e20795f6aeb*mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mw8d03d46b_3832_4fe6_8a6b_467ef38af206=10.0; mw658feef9_67a1_431f_95fa_7358c6b294d2=1.0E-15; mwf50b6519_aaf9_4cb0_a651_d5a3159d7390=10.0 | Reaction: mwacd53f34_4935_4b6a_8267_024f0d966c8c + mw57b4236e_c789_4799_a4f9_a03437a5593a => mw57b4236e_c789_4799_a4f9_a03437a5593a + mwacd53f34_4935_4b6a_8267_024f0d966c8c, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw658feef9_67a1_431f_95fa_7358c6b294d2*mw57b4236e_c789_4799_a4f9_a03437a5593a*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mw8d03d46b_3832_4fe6_8a6b_467ef38af206*(1+mwacd53f34_4935_4b6a_8267_024f0d966c8c*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwf50b6519_aaf9_4cb0_a651_d5a3159d7390)+mw57b4236e_c789_4799_a4f9_a03437a5593a*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwe479dc91_2774_4239_8212_60a330a84a76=0.86 | Reaction: mw869055b5_5d27_4f4a_a390_b3fa48d6780e => mwc84af692_e3fc_4ede_99b6_b0cce3729bf7, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mwe479dc91_2774_4239_8212_60a330a84a76*mw869055b5_5d27_4f4a_a390_b3fa48d6780e*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw1aca690a_9ee1_460f_bc28_780fb04c1a32=1.0; mwf604d18f_ae32_4d08_bfef_f778aae8f24b=1.0 | Reaction: mw6f8ce639_1c28_444f_b6e6_30ff06ab0d6e + mwb9a40fab_a7c9_4984_805f_045fefc4ff32 => mwd5f166e6_df0a_45bf_b662_e87b91b79a27, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mwf604d18f_ae32_4d08_bfef_f778aae8f24b*mwb9a40fab_a7c9_4984_805f_045fefc4ff32*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mw1aca690a_9ee1_460f_bc28_780fb04c1a32+mwb9a40fab_a7c9_4984_805f_045fefc4ff32*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw0930c7cf_99a6_4a20_a33d_02fd90e424bb=0.24 | Reaction: mwc42127ea_2b78_4381_b230_30e95cd5a9d6 => mw4fd8b902_e2a2_4910_899c_2a7d3425e0e0, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw0930c7cf_99a6_4a20_a33d_02fd90e424bb*mwc42127ea_2b78_4381_b230_30e95cd5a9d6*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwe250484c_acc9_4453_9eb6_49cda54b2f1e=0.3 | Reaction: mw10b15557_55f5_4525_b19d_161b056f5791 => mw6f8ce639_1c28_444f_b6e6_30ff06ab0d6e, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mwe250484c_acc9_4453_9eb6_49cda54b2f1e*mw10b15557_55f5_4525_b19d_161b056f5791*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwd84cf612_edaf_4dd9_9abd_003cc0569864=0.0 | Reaction: mw9275f30d_c42b_459c_91c5_67b7e08b6486 => mwbe46ba92_97de_4cc4_970d_2dec54671573, Rate Law: mwd84cf612_edaf_4dd9_9abd_003cc0569864*mw9275f30d_c42b_459c_91c5_67b7e08b6486*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw3c07a768_8e7a_49bb_8a1d_fdc92192f92b=1.0E-15; mw2a60d98a_bc06_4e05_b965_c920da8987dc=10.0; mw9704b67a_2c16_4ac7_a311_2096ab426758=10.0 | Reaction: mwacd53f34_4935_4b6a_8267_024f0d966c8c + mw7f261959_39d2_4e8b_92b6_4466c2504544 => mw7f261959_39d2_4e8b_92b6_4466c2504544 + mwacd53f34_4935_4b6a_8267_024f0d966c8c, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw3c07a768_8e7a_49bb_8a1d_fdc92192f92b*mw7f261959_39d2_4e8b_92b6_4466c2504544*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mw9704b67a_2c16_4ac7_a311_2096ab426758*(1+mwacd53f34_4935_4b6a_8267_024f0d966c8c*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mw2a60d98a_bc06_4e05_b965_c920da8987dc)+mw7f261959_39d2_4e8b_92b6_4466c2504544*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw4594ab28_d538_4a72_875a_d878e907020d=1.0E-15 | Reaction: mwb167e768_b778_4072_8798_3cf19e96d1d7 + mw5b252d78_9ab9_438c_8b81_2189b1f76357 => mwf219928e_abba_4c09_9597_5d6910f7e4d9, Rate Law: mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw4594ab28_d538_4a72_875a_d878e907020d*mwb167e768_b778_4072_8798_3cf19e96d1d7*mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw5b252d78_9ab9_438c_8b81_2189b1f76357*mw664a2e7f_0c35_423c_ac5d_34090e629a69/mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mw9c4a9937_faff_4853_8a76_d17a084948d9=0.16 | Reaction: mwbba20281_3d8b_48c3_8e13_dee78e87dfb8 => mw78198c86_4b16_4117_8592_6a95c3953126, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw9c4a9937_faff_4853_8a76_d17a084948d9*mwbba20281_3d8b_48c3_8e13_dee78e87dfb8*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwd01d5494_fdcd_494e_8652_87201fa5b291=0.002 | Reaction: mwf219928e_abba_4c09_9597_5d6910f7e4d9 => mw57b4236e_c789_4799_a4f9_a03437a5593a, Rate Law: mwd01d5494_fdcd_494e_8652_87201fa5b291*mwf219928e_abba_4c09_9597_5d6910f7e4d9*mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mw7b7a1d20_f258_4e3d_9234_62db60126326=6.0E-5 | Reaction: mw9b410665_6c5e_4f37_a7f2_0cb0963b98b1 + mw56c26af9_9f4a_4f13_936a_94ae6364342b => mwb167e768_b778_4072_8798_3cf19e96d1d7, Rate Law: mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw7b7a1d20_f258_4e3d_9234_62db60126326*mw9b410665_6c5e_4f37_a7f2_0cb0963b98b1*mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw56c26af9_9f4a_4f13_936a_94ae6364342b*mw664a2e7f_0c35_423c_ac5d_34090e629a69/mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mw87e9394c_7eef_4271_85bc_d1b0a96db1f2=0.0 | Reaction: mwd5f166e6_df0a_45bf_b662_e87b91b79a27 => mw9275f30d_c42b_459c_91c5_67b7e08b6486, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw87e9394c_7eef_4271_85bc_d1b0a96db1f2*mwd5f166e6_df0a_45bf_b662_e87b91b79a27*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw8f586bdf_8d42_49c1_807a_670a1c49cbd9=1.0; mw2ba0a425_a224_4fa1_affc_bcc206027a3e=1.0 | Reaction: mw6f8ce639_1c28_444f_b6e6_30ff06ab0d6e + mw58370246_a992_4253_8029_12fbb07a417d => mw869055b5_5d27_4f4a_a390_b3fa48d6780e, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw8f586bdf_8d42_49c1_807a_670a1c49cbd9*mw58370246_a992_4253_8029_12fbb07a417d*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mw2ba0a425_a224_4fa1_affc_bcc206027a3e+mw58370246_a992_4253_8029_12fbb07a417d*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw58dfe861_b815_48b4_89e9_6f71cccb561e=0.095 | Reaction: mw5607cee0_ee75_4065_9368_d07c3abbe18b => mw8675a533_92fb_4fbf_b747_7bca05c5841c, Rate Law: mw58dfe861_b815_48b4_89e9_6f71cccb561e*mw5607cee0_ee75_4065_9368_d07c3abbe18b*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw106608bc_52fb_40e5_babb_cbb58aaaeed4=0.24 | Reaction: mwf219928e_abba_4c09_9597_5d6910f7e4d9 => mwce6e4efd_3187_4379_ad47_104c95e0eb3b, Rate Law: mw106608bc_52fb_40e5_babb_cbb58aaaeed4*mwf219928e_abba_4c09_9597_5d6910f7e4d9*mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mwd7b0f9fb_1180_47bb_b8c0_be2f89aa1c59=1.0; mw91a51f27_e4be_41a6_a967_b6a63c909652=1.0 | Reaction: mw7f261959_39d2_4e8b_92b6_4466c2504544 => mwd5f166e6_df0a_45bf_b662_e87b91b79a27, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw91a51f27_e4be_41a6_a967_b6a63c909652*mw7f261959_39d2_4e8b_92b6_4466c2504544*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mwd7b0f9fb_1180_47bb_b8c0_be2f89aa1c59+mw7f261959_39d2_4e8b_92b6_4466c2504544*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwf8062aeb_69d6_416b_b877_9687ef6fbc80=1.0; mw7ff2e94a_ac19_47ed_9127_14d385a1e544=1.0 | Reaction: mw57b4236e_c789_4799_a4f9_a03437a5593a + mwb9a40fab_a7c9_4984_805f_045fefc4ff32 => mwd5f166e6_df0a_45bf_b662_e87b91b79a27, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw7ff2e94a_ac19_47ed_9127_14d385a1e544*mwb9a40fab_a7c9_4984_805f_045fefc4ff32*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mwf8062aeb_69d6_416b_b877_9687ef6fbc80+mwb9a40fab_a7c9_4984_805f_045fefc4ff32*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwdf0efe39_4f88_4fc1_930b_55865b8d52f0=0.08 | Reaction: mw8675a533_92fb_4fbf_b747_7bca05c5841c => mw56c26af9_9f4a_4f13_936a_94ae6364342b, Rate Law: mwdf0efe39_4f88_4fc1_930b_55865b8d52f0*mw8675a533_92fb_4fbf_b747_7bca05c5841c*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw5ea6c681_bf85_4ac5_9aac_536087a97950=1.0; mw65a608c7_1026_4108_bb3d_f3358430aaf2=1.0 | Reaction: mwce6e4efd_3187_4379_ad47_104c95e0eb3b + mw58370246_a992_4253_8029_12fbb07a417d => mw869055b5_5d27_4f4a_a390_b3fa48d6780e, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw65a608c7_1026_4108_bb3d_f3358430aaf2*mw58370246_a992_4253_8029_12fbb07a417d*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/(mw5ea6c681_bf85_4ac5_9aac_536087a97950+mw58370246_a992_4253_8029_12fbb07a417d*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22)/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw1981fa4d_c122_41a6_8c85_55a49d408c00=400.0; mw340bc412_34ab_498a_bd42_47dd6cf025bd=1.0; mw6cf7ac60_5bab_4b73_bb47_dc064486f000=2000.0 | Reaction: mwba110304_bd9a_4fd0_9b4c_b8bfc975e30b => mw5607cee0_ee75_4065_9368_d07c3abbe18b + mw364ca1a4_fc56_4138_8031_16341ac865de + mw4fc13b75_10cc_41fb_b9f8_1ce95fccae73, Rate Law: mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9*mw1981fa4d_c122_41a6_8c85_55a49d408c00*(mwba110304_bd9a_4fd0_9b4c_b8bfc975e30b*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mw340bc412_34ab_498a_bd42_47dd6cf025bd/(mw6cf7ac60_5bab_4b73_bb47_dc064486f000+(mwba110304_bd9a_4fd0_9b4c_b8bfc975e30b*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mw340bc412_34ab_498a_bd42_47dd6cf025bd)/mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw2081c907_9d37_48a0_8581_8b8f3d5f7148=0.1 | Reaction: mwd7270399_0429_4c85_920a_de2e0ae74440 => mw7f261959_39d2_4e8b_92b6_4466c2504544, Rate Law: mw2081c907_9d37_48a0_8581_8b8f3d5f7148*mwd7270399_0429_4c85_920a_de2e0ae74440*mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mw3bf6abba_fc8d_4254_bc9c_037ec64d3e12=1000.0; mw4c1174b0_1cba_4d7c_bedb_c29d9f65c10c=1.0; mw85474b5d_1b19_4388_bcee_098fbb23f2e0=700.0 | Reaction: mw55b6f083_2b28_4a1a_ab90_82a751525d72 => mwcbadb505_1cfb_4903_9975_0e53de2ba877, Rate Law: mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9*mw3bf6abba_fc8d_4254_bc9c_037ec64d3e12*(mw55b6f083_2b28_4a1a_ab90_82a751525d72*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mw4c1174b0_1cba_4d7c_bedb_c29d9f65c10c/(mw85474b5d_1b19_4388_bcee_098fbb23f2e0+(mw55b6f083_2b28_4a1a_ab90_82a751525d72*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mw4c1174b0_1cba_4d7c_bedb_c29d9f65c10c)/mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw202bb744_d70a_4cf2_b2db_0d9d360bdfb5=0.1 | Reaction: mw78198c86_4b16_4117_8592_6a95c3953126 => mwf26f605f_29e9_4454_834c_7b3edab4bbc2, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw202bb744_d70a_4cf2_b2db_0d9d360bdfb5*mw78198c86_4b16_4117_8592_6a95c3953126*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw3288f227_0208_435a_a41c_b1778f50008c=0.09 | Reaction: mwf26f605f_29e9_4454_834c_7b3edab4bbc2 => mw10b15557_55f5_4525_b19d_161b056f5791, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw3288f227_0208_435a_a41c_b1778f50008c*mwf26f605f_29e9_4454_834c_7b3edab4bbc2*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwf5ba0e23_9a0b_4bf6_b0ea_9fbc91843b5c=0.1 | Reaction: mwc84af692_e3fc_4ede_99b6_b0cce3729bf7 => mw55b6f083_2b28_4a1a_ab90_82a751525d72, Rate Law: mwf5ba0e23_9a0b_4bf6_b0ea_9fbc91843b5c*mwc84af692_e3fc_4ede_99b6_b0cce3729bf7*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw5b67154e_1851_4ba0_9491_7a14b2064c42=0.06 | Reaction: mw364ca1a4_fc56_4138_8031_16341ac865de => mwb9a40fab_a7c9_4984_805f_045fefc4ff32, Rate Law: mw5b67154e_1851_4ba0_9491_7a14b2064c42*mw364ca1a4_fc56_4138_8031_16341ac865de*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mwb8d480f9_5783_4feb_aaf1_2f861b09e009=0.14 | Reaction: mw78198c86_4b16_4117_8592_6a95c3953126 => mw7938fab7_d0c6_497b_8fb6_75922fcc19d5, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mwb8d480f9_5783_4feb_aaf1_2f861b09e009*mw78198c86_4b16_4117_8592_6a95c3953126*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw7246cef3_7a78_43b0_acb7_21533c394daa=0.001 | Reaction: mw85ae78ed_34f7_460c_b906_1f512a83810c + mw49322d55_ad63_4e7c_b1eb_42835c9b577a => mwd7270399_0429_4c85_920a_de2e0ae74440, Rate Law: mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw7246cef3_7a78_43b0_acb7_21533c394daa*mw85ae78ed_34f7_460c_b906_1f512a83810c*mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw49322d55_ad63_4e7c_b1eb_42835c9b577a*mw664a2e7f_0c35_423c_ac5d_34090e629a69/mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mwe808b2da_9ed2_421e_a8d9_3c3534d42ee0=0.2 | Reaction: mw4fc13b75_10cc_41fb_b9f8_1ce95fccae73 => mw58370246_a992_4253_8029_12fbb07a417d, Rate Law: mwe808b2da_9ed2_421e_a8d9_3c3534d42ee0*mw4fc13b75_10cc_41fb_b9f8_1ce95fccae73*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw322ca6ab_81cf_4f6c_835e_48bb5f8b11de=0.0; mw051e0a03_1d45_448b_be4a_9d489e1b3ff9=0.0; mwf9b630f8_c8a5_4fd0_bef1_92819b1257d2=0.0 | Reaction: mwbe46ba92_97de_4cc4_970d_2dec54671573 => mw3d58864c_79c7_4ff2_98fe_1a85b2ccc43d, Rate Law: mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9*mw051e0a03_1d45_448b_be4a_9d489e1b3ff9*(mwbe46ba92_97de_4cc4_970d_2dec54671573*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mwf9b630f8_c8a5_4fd0_bef1_92819b1257d2/(mw322ca6ab_81cf_4f6c_835e_48bb5f8b11de+(mwbe46ba92_97de_4cc4_970d_2dec54671573*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mwf9b630f8_c8a5_4fd0_bef1_92819b1257d2)/mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw5805aaa6_5a0a_495b_a21b_16e6ba6f39bc=0.3 | Reaction: mw3d58864c_79c7_4ff2_98fe_1a85b2ccc43d => mwacd53f34_4935_4b6a_8267_024f0d966c8c, Rate Law: mw5805aaa6_5a0a_495b_a21b_16e6ba6f39bc*mw3d58864c_79c7_4ff2_98fe_1a85b2ccc43d*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw5b78650d_d92f_4602_bbe9_66d900ff312e=0.22 | Reaction: mw4fd8b902_e2a2_4910_899c_2a7d3425e0e0 => mwf06c0537_577b_4f13_a9a3_521f9d2217fb, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mw5b78650d_d92f_4602_bbe9_66d900ff312e*mw4fd8b902_e2a2_4910_899c_2a7d3425e0e0*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mw9ca6d980_6fbd_4207_9f2c_c05e2b5b0502=0.0; mwf32cef57_32ba_4c0f_bbaf_bbc90af4a8aa=0.0; mwcffa9e49_67c6_4908_9314_6c16030c8989=0.0 | Reaction: mwbe46ba92_97de_4cc4_970d_2dec54671573 => mw6949b379_107d_4822_b13e_680a491d2425, Rate Law: mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9*mwf32cef57_32ba_4c0f_bbaf_bbc90af4a8aa*(mwbe46ba92_97de_4cc4_970d_2dec54671573*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mwcffa9e49_67c6_4908_9314_6c16030c8989/(mw9ca6d980_6fbd_4207_9f2c_c05e2b5b0502+(mwbe46ba92_97de_4cc4_970d_2dec54671573*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mwcffa9e49_67c6_4908_9314_6c16030c8989)/mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mw2db26dbd_dda3_4770_b923_d2725a80ccba=1.0; mwb86b112e_674a_4e98_8ca4_3d0273a134d4=10.0; mw287bd1ba_fbc1_43a0_94ab_38a6336bbebb=10.0 | Reaction: mw55b6f083_2b28_4a1a_ab90_82a751525d72 => mwe3af2471_5087_4796_982a_56fad9a9a972, Rate Law: mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9*mwb86b112e_674a_4e98_8ca4_3d0273a134d4*(mw55b6f083_2b28_4a1a_ab90_82a751525d72*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mw2db26dbd_dda3_4770_b923_d2725a80ccba/(mw287bd1ba_fbc1_43a0_94ab_38a6336bbebb+(mw55b6f083_2b28_4a1a_ab90_82a751525d72*mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9)^mw2db26dbd_dda3_4770_b923_d2725a80ccba)/mwdfcbcdb1_3058_4a8b_9166_5b5e144c52c9 |
mwf80a94ba_03ba_44f0_b793_4f60c20fd074=8.0E-9 | Reaction: mwfaacd7f7_6aa9_4e6c_a7d8_281f2022ba2f + mwa244ea2a_3e41_473b_9ae7_d0db512fc366 + mw763cffc8_c121_4004_afdb_97e9de9f0081 => mw6339814d_af4c_4eee_9455_7e20795f6aeb, Rate Law: mw664a2e7f_0c35_423c_ac5d_34090e629a69*mwf80a94ba_03ba_44f0_b793_4f60c20fd074*mwfaacd7f7_6aa9_4e6c_a7d8_281f2022ba2f*mw664a2e7f_0c35_423c_ac5d_34090e629a69*mwa244ea2a_3e41_473b_9ae7_d0db512fc366*mw664a2e7f_0c35_423c_ac5d_34090e629a69*mw763cffc8_c121_4004_afdb_97e9de9f0081*mw664a2e7f_0c35_423c_ac5d_34090e629a69/mw664a2e7f_0c35_423c_ac5d_34090e629a69 |
mwec4950e3_ecf8_4077_9cd1_b70818e911b9=0.19 | Reaction: mwf06c0537_577b_4f13_a9a3_521f9d2217fb => mwbba20281_3d8b_48c3_8e13_dee78e87dfb8, Rate Law: mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22*mwec4950e3_ecf8_4077_9cd1_b70818e911b9*mwf06c0537_577b_4f13_a9a3_521f9d2217fb*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22/mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
mwe0f7b31e_2efc_481e_90d0_e86c3b8d0be4=0.125 | Reaction: mw7938fab7_d0c6_497b_8fb6_75922fcc19d5 => mwba110304_bd9a_4fd0_9b4c_b8bfc975e30b, Rate Law: mwe0f7b31e_2efc_481e_90d0_e86c3b8d0be4*mw7938fab7_d0c6_497b_8fb6_75922fcc19d5*mwfef402b9_4b7e_4fbd_bba2_ff8998ab0b22 |
States:
Name | Description |
---|---|
mw763cffc8 c121 4004 afdb 97e9de9f0081 | LPG |
mw56c26af9 9f4a 4f13 936a 94ae6364342b | [Interleukin-6 receptor subunit alpha] |
mw6f8ce639 1c28 444f b6e6 30ff06ab0d6e | [Q4QAU2] |
mw9b410665 6c5e 4f37 a7f2 0cb0963b98b1 | [Interleukin-6 receptor subunit alpha] |
mwacd53f34 4935 4b6a 8267 024f0d966c8c | [O64645] |
mwa244ea2a 3e41 473b 9ae7 d0db512fc366 | [Q9Y2C9] |
mw9275f30d c42b 459c 91c5 67b7e08b6486 | [Signal transducer and activator of transcription 1-alpha/beta] |
mw7938fab7 d0c6 497b 8fb6 75922fcc19d5 | [O64645] |
mw6949b379 107d 4822 b13e 680a491d2425 | [Nitric oxide synthase, inducible] |
mw5b252d78 9ab9 438c 8b81 2189b1f76357 | [Q87041] |
mwc42127ea 2b78 4381 b230 30e95cd5a9d6 | [Myeloid differentiation primary response protein MyD88] |
mwf219928e abba 4c09 9597 5d6910f7e4d9 | [Interleukin-6 receptor subunit alpha] |
mw8675a533 92fb 4fbf b747 7bca05c5841c | [Interleukin-6 receptor subunit alpha] |
mw7f261959 39d2 4e8b 92b6 4466c2504544 | [Tyrosine-protein kinase JAK1] |
mw5607cee0 ee75 4065 9368 d07c3abbe18b | [Interleukin-6 receptor subunit alpha] |
mw3d58864c 79c7 4ff2 98fe 1a85b2ccc43d | [O64645] |
mwbe46ba92 97de 4cc4 970d 2dec54671573 | [Signal transducer and activator of transcription 1-alpha/beta] |
mw4fd8b902 e2a2 4910 899c 2a7d3425e0e0 | [Interleukin-1 receptor-associated kinase 1] |
mwce6e4efd 3187 4379 ad47 104c95e0eb3b | [Tyrosine-protein kinase JAK1] |
mw55b6f083 2b28 4a1a ab90 82a751525d72 | [Signal transducer and activator of transcription 1-alpha/beta] |
mwd5f166e6 df0a 45bf b662 e87b91b79a27 | [5416611] |
mwba110304 bd9a 4fd0 9b4c b8bfc975e30b | [O64645] |
mw78198c86 4b16 4117 8592 6a95c3953126 | [Q9VEZ5] |
mw10b15557 55f5 4525 b19d 161b056f5791 | [A0A061RQ89] |
mw6339814d af4c 4eee 9455 7e20795f6aeb | [Q9Y2C9] |
mwb9a40fab a7c9 4984 805f 045fefc4ff32 | [5416611] |
mwf26f605f 29e9 4454 834c 7b3edab4bbc2 | [B3EWE5] |
mwb167e768 b778 4072 8798 3cf19e96d1d7 | [Interleukin-6 receptor subunit alpha] |
mwc84af692 e3fc 4ede 99b6 b0cce3729bf7 | [Signal transducer and activator of transcription 1-alpha/beta] |
mwfaacd7f7 6aa9 4e6c a7d8 281f2022ba2f | [Toll-like receptor 2] |
mw58370246 a992 4253 8029 12fbb07a417d | [5416611] |
mw57b4236e c789 4799 a4f9 a03437a5593a | [Tyrosine-protein kinase JAK1] |
mw869055b5 5d27 4f4a a390 b3fa48d6780e | [5416611] |
mwe3af2471 5087 4796 982a 56fad9a9a972 | [O64645] |
mw364ca1a4 fc56 4138 8031 16341ac865de | [5416611] |
mw4fc13b75 10cc 41fb b9f8 1ce95fccae73 | [5416611] |
mw49322d55 ad63 4e7c b1eb 42835c9b577a | [Interferon gamma] |
mwd7270399 0429 4c85 920a de2e0ae74440 | [Interferon gamma] |
mwf06c0537 577b 4f13 a9a3 521f9d2217fb | [TNF receptor-associated factor 2] |
mwcbadb505 1cfb 4903 9975 0e53de2ba877 | [Q9BRQ8] |
mw85ae78ed 34f7 460c b906 1f512a83810c | [Interferon gamma] |
mwbba20281 3d8b 48c3 8e13 dee78e87dfb8 | [Q9V3Q6] |
BIOMD0000000580
— v0.0.1Sonntag2012 - mTOR model - IRS dependent regulation of AMPK by insulinTSC1-TSC2 complex has two states: 1) active (TSC1_…
Details
Mammalian target of rapamycin (mTOR) kinase responds to growth factors, nutrients and cellular energy status and is a central controller of cellular growth. mTOR exists in two multiprotein complexes that are embedded into a complex signalling network. Adenosine monophosphate-dependent kinase (AMPK) is activated by energy deprivation and shuts off adenosine 5'-triphosphate (ATP)-consuming anabolic processes, in part via the inactivation of mTORC1. Surprisingly, we observed that AMPK not only responds to energy deprivation but can also be activated by insulin, and is further induced in mTORC1-deficient cells. We have recently modelled the mTOR network, covering both mTOR complexes and their insulin and nutrient inputs. In the present study we extended the network by an AMPK module to generate the to date most comprehensive data-driven dynamic AMPK-mTOR network model. In order to define the intersection via which AMPK is activated by the insulin network, we compared simulations for six different hypothetical model structures to our observed AMPK dynamics. Hypotheses ranking suggested that the most probable intersection between insulin and AMPK was the insulin receptor substrate (IRS) and that the effects of canonical IRS downstream cues on AMPK would be mediated via an mTORC1-driven negative-feedback loop. We tested these predictions experimentally in multiple set-ups, where we inhibited or induced players along the insulin-mTORC1 signalling axis and observed AMPK induction or inhibition. We confirmed the identified model and therefore report a novel connection within the insulin-mTOR-AMPK network: we conclude that AMPK is positively regulated by IRS and can be inhibited via the negative-feedback loop. link: http://identifiers.org/pubmed/22452783
Parameters:
Name | Description |
---|---|
scale_PRAS40_pS183_obs = 1.0 | Reaction: PRAS40_pS183_obs = scale_PRAS40_pS183_obs*PRAS40_pS183, Rate Law: missing |
AMPK_pT172_dephosphorylation = 0.0107214736590526 | Reaction: AMPK_pT172 => AMPK; AMPK_pT172, Rate Law: Cell*AMPK_pT172_dephosphorylation*AMPK_pT172 |
Akt_T308_phosphorylation_by_IRS1_p = 6.91810637938108 | Reaction: Akt_T308 => Akt_pT308; IRS1_p, Akt_T308, IRS1_p, Rate Law: Cell*Akt_T308_phosphorylation_by_IRS1_p*Akt_T308*IRS1_p |
IRS1_pS636_dephosphorylation = 0.0130499987407289 | Reaction: IRS1_pS636 => IRS1; IRS1_pS636, Rate Law: Cell*IRS1_pS636_dephosphorylation*IRS1_pS636 |
PRAS40_pS183_dephosphorylation = 2.33014390064544 | Reaction: PRAS40_pS183 => PRAS40_S183; PRAS40_pS183, Rate Law: Cell*PRAS40_pS183_dephosphorylation*PRAS40_pS183 |
scale_Akt_pS473_obs = 1.0 | Reaction: Akt_pS473_obs = scale_Akt_pS473_obs*Akt_pS473, Rate Law: missing |
scale_TSC1_TSC2_pS1387_obs = 1.0 | Reaction: TSC1_TSC2_pS1387_obs = scale_TSC1_TSC2_pS1387_obs*TSC1_TSC2_pS1387, Rate Law: missing |
scale_AMPK_pT172_obs = 1.0 | Reaction: AMPK_pT172_obs = scale_AMPK_pT172_obs*AMPK_pT172, Rate Law: missing |
mTORC1_S2448_activation_by_Amino_Acids = 0.00438915524637669 | Reaction: mTORC1 => mTORC1_pS2448; Amino_Acids, Amino_Acids, mTORC1, Rate Law: Cell*mTORC1_S2448_activation_by_Amino_Acids*mTORC1*Amino_Acids |
scale_mTOR_pS2448_obs = 1.0 | Reaction: mTOR_pS2448_obs = scale_mTOR_pS2448_obs*mTORC1_pS2448, Rate Law: missing |
IR_beta_ready = 0.0532769862975496 | Reaction: IR_beta_refractory => IR_beta; IR_beta_refractory, Rate Law: Cell*IR_beta_ready*IR_beta_refractory |
PI3K_variant_phosphorylation_by_IR_beta_pY1146 = 0.01 | Reaction: PI3K_variant => PI3K_variant_p; IR_beta_pY1146, IR_beta_pY1146, PI3K_variant, Rate Law: Cell*PI3K_variant_phosphorylation_by_IR_beta_pY1146*PI3K_variant*IR_beta_pY1146 |
PI3K_variant_p_dephosphorylation = 10.0 | Reaction: PI3K_variant_p => PI3K_variant; PI3K_variant_p, Rate Law: Cell*PI3K_variant_p_dephosphorylation*PI3K_variant_p |
TSC1_TSC2_T1462_phosphorylation_by_Akt_pT308 = 0.0177561800881718 | Reaction: TSC1_TSC2_pS1387 => TSC1_TSC2_pT1462; Akt_pT308, Akt_pT308, TSC1_TSC2_pS1387, Rate Law: Cell*TSC1_TSC2_T1462_phosphorylation_by_Akt_pT308*TSC1_TSC2_pS1387*Akt_pT308 |
PRAS40_S183_phosphorylation_by_mTORC1_pS2448 = 0.187621138099883 | Reaction: PRAS40_S183 => PRAS40_pS183; mTORC1_pS2448, PRAS40_S183, mTORC1_pS2448, Rate Law: Cell*PRAS40_S183_phosphorylation_by_mTORC1_pS2448*PRAS40_S183*mTORC1_pS2448 |
TSC1_TSC2_S1387_phosphorylation_by_AMPK_pT172 = 0.036558856656738 | Reaction: TSC1_TSC2_pT1462 => TSC1_TSC2_pS1387; AMPK_pT172, AMPK_pT172, TSC1_TSC2_pT1462, Rate Law: Cell*TSC1_TSC2_S1387_phosphorylation_by_AMPK_pT172*TSC1_TSC2_pT1462*AMPK_pT172 |
mTORC2_pS2481_dephosphorylation = 0.0183734532316308 | Reaction: mTORC2_pS2481 => mTORC2; mTORC2_pS2481, Rate Law: Cell*mTORC2_pS2481_dephosphorylation*mTORC2_pS2481 |
IR_beta_phosphorylation_by_Insulin = 0.124273166818913 | Reaction: IR_beta => IR_beta_pY1146; Insulin, IR_beta, Insulin, Rate Law: Cell*IR_beta_phosphorylation_by_Insulin*IR_beta*Insulin |
IRS1_phosphorylation_by_IR_beta_pY1146 = 0.00491598674814158 | Reaction: IRS1 => IRS1_p; IR_beta_pY1146, IRS1, IR_beta_pY1146, Rate Law: Cell*IRS1_phosphorylation_by_IR_beta_pY1146*IRS1*IR_beta_pY1146 |
AMPK_T172_phosphorylation = 9.79765849087796 | Reaction: AMPK => AMPK_pT172; IRS1_p, AMPK, IRS1_p, Rate Law: Cell*AMPK_T172_phosphorylation*AMPK*IRS1_p |
Akt_pS473_dephosphorylation = 0.00640215551178824 | Reaction: Akt_pS473 => Akt_S473; Akt_pS473, Rate Law: Cell*Akt_pS473_dephosphorylation*Akt_pS473 |
mTORC1_pS2448_dephosphorylation_by_TSC1_TSC2_pS1387 = 0.0106651971237991 | Reaction: mTORC1_pS2448 => mTORC1; TSC1_TSC2_pS1387, TSC1_TSC2_pS1387, mTORC1_pS2448, Rate Law: Cell*mTORC1_pS2448_dephosphorylation_by_TSC1_TSC2_pS1387*mTORC1_pS2448*TSC1_TSC2_pS1387 |
scale_mTOR_pS2481_obs = 1.0 | Reaction: mTOR_pS2481_obs = scale_mTOR_pS2481_obs*mTORC2_pS2481, Rate Law: missing |
scale_p70S6K_pT389_obs = 1.0 | Reaction: p70S6K_pT389_obs = scale_p70S6K_pT389_obs*p70S6K_pT389, Rate Law: missing |
PRAS40_T246_phosphorylation_by_Akt_pT308 = 0.137729484208433 | Reaction: PRAS40_T246 => PRAS40_pT246; Akt_pT308, Akt_pT308, PRAS40_T246, Rate Law: Cell*PRAS40_T246_phosphorylation_by_Akt_pT308*PRAS40_T246*Akt_pT308 |
mTORC2_S2481_phosphorylation_by_PI3K_variant_p = 0.37535264623552 | Reaction: mTORC2 => mTORC2_pS2481; PI3K_variant_p, PI3K_variant_p, mTORC2, Rate Law: Cell*mTORC2_S2481_phosphorylation_by_PI3K_variant_p*mTORC2*PI3K_variant_p |
PRAS40_pT246_dephosphorylation = 1.60512543108081 | Reaction: PRAS40_pT246 => PRAS40_T246; PRAS40_pT246, Rate Law: Cell*PRAS40_pT246_dephosphorylation*PRAS40_pT246 |
Akt_S473_phosphorylation_by_mTORC2_pS2481_n_IRS1_p = 13.1441708920036 | Reaction: Akt_S473 => Akt_pS473; mTORC2_pS2481, IRS1_p, Akt_S473, IRS1_p, mTORC2_pS2481, Rate Law: Cell*Akt_S473_phosphorylation_by_mTORC2_pS2481_n_IRS1_p*Akt_S473*mTORC2_pS2481*IRS1_p |
p70S6K_pT389_dephosphorylation = 0.0113511588360422 | Reaction: p70S6K_pT389 => p70S6K; p70S6K_pT389, Rate Law: Cell*p70S6K_pT389_dephosphorylation*p70S6K_pT389 |
p70S6K_T389_phosphorylation_by_mTORC1_pS2448 = 0.00184042775983938 | Reaction: p70S6K => p70S6K_pT389; mTORC1_pS2448, mTORC1_pS2448, p70S6K, Rate Law: Cell*p70S6K_T389_phosphorylation_by_mTORC1_pS2448*p70S6K*mTORC1_pS2448 |
scale_IR_beta_pY1146_obs = 1.0 | Reaction: IR_beta_pY1146_obs = scale_IR_beta_pY1146_obs*IR_beta_pY1146, Rate Law: missing |
scale_Akt_pT308_obs = 1.0 | Reaction: Akt_pT308_obs = scale_Akt_pT308_obs*Akt_pT308, Rate Law: missing |
IRS1_p_phosphorylation_by_p70S6K_pT389 = 1682.74838380846 | Reaction: IRS1_p => IRS1_pS636; p70S6K_pT389, IRS1_p, p70S6K_pT389, Rate Law: Cell*IRS1_p_phosphorylation_by_p70S6K_pT389*IRS1_p*p70S6K_pT389 |
Akt_pT308_dephosphorylation = 0.00335544587646129 | Reaction: Akt_pT308 => Akt_T308; Akt_pT308, Rate Law: Cell*Akt_pT308_dephosphorylation*Akt_pT308 |
IR_beta_pY1146_dephosphorylation = 0.396235078561935 | Reaction: IR_beta_pY1146 => IR_beta_refractory; IR_beta_pY1146, Rate Law: Cell*IR_beta_pY1146_dephosphorylation*IR_beta_pY1146 |
scale_PRAS40_pT246_obs = 1.0 | Reaction: PRAS40_pT246_obs = scale_PRAS40_pT246_obs*PRAS40_pT246, Rate Law: missing |
scale_IRS1_pS636_obs = 1.0 | Reaction: IRS1_pS636_obs = scale_IRS1_pS636_obs*IRS1_pS636, Rate Law: missing |
States:
Name | Description |
---|---|
mTORC1 pS2448 | [Serine/threonine-protein kinase mTOR] |
IRS1 pS636 | [Insulin receptor substrate 1; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform] |
mTOR pS2448 obs | [Serine/threonine-protein kinase mTOR] |
AMPK pT172 obs | [5'-AMP-activated protein kinase subunit gamma-1; 5'-AMP-activated protein kinase catalytic subunit alpha-1; 5'-AMP-activated protein kinase subunit beta-1] |
Akt S473 | [RAC-alpha serine/threonine-protein kinase] |
IRS1 pS636 obs | [Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform; Insulin receptor substrate 1] |
IRS1 p | [Insulin receptor substrate 1; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform] |
IR beta | [Insulin receptor] |
AMPK pT172 | [5'-AMP-activated protein kinase subunit gamma-1; 5'-AMP-activated protein kinase subunit beta-1; 5'-AMP-activated protein kinase catalytic subunit alpha-1] |
PRAS40 S183 | [Proline-rich AKT1 substrate 1] |
PRAS40 pT246 | [Proline-rich AKT1 substrate 1] |
PI3K variant | PI3K_variant |
PI3K variant p | PI3K_variant_p |
PRAS40 pT246 obs | [Proline-rich AKT1 substrate 1] |
PRAS40 pS183 | [Proline-rich AKT1 substrate 1] |
mTOR pS2481 obs | [Serine/threonine-protein kinase mTOR] |
Akt pS473 | [RAC-alpha serine/threonine-protein kinase] |
p70S6K pT389 obs | [Ribosomal protein S6 kinase beta-1] |
PRAS40 T246 | [Proline-rich AKT1 substrate 1] |
mTORC2 | [Serine/threonine-protein kinase mTOR] |
Akt pS473 obs | [RAC-alpha serine/threonine-protein kinase] |
TSC1 TSC2 pT1462 | [Hamartin; Tuberin] |
IR beta refractory | [Insulin receptor] |
Akt pT308 | [RAC-alpha serine/threonine-protein kinase] |
PRAS40 pS183 obs | [Proline-rich AKT1 substrate 1] |
Akt T308 | [RAC-alpha serine/threonine-protein kinase] |
Insulin | [Insulin] |
mTORC1 | [Serine/threonine-protein kinase mTOR] |
AMPK | [5'-AMP-activated protein kinase subunit beta-1; 5'-AMP-activated protein kinase subunit gamma-1; 5'-AMP-activated protein kinase catalytic subunit alpha-1] |
Amino Acids | Amino_Acids |
IRS1 | [Insulin receptor substrate 1; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform] |
IR beta pY1146 | [Insulin receptor] |
TSC1 TSC2 pS1387 | [Tuberin; Hamartin] |
p70S6K pT389 | [Ribosomal protein S6 kinase beta-1] |
IR beta pY1146 obs | [Insulin receptor] |
mTORC2 pS2481 | [Serine/threonine-protein kinase mTOR] |
TSC1 TSC2 pS1387 obs | [Tuberin; Hamartin] |
p70S6K | [Ribosomal protein S6 kinase beta-1] |
Akt pT308 obs | [RAC-alpha serine/threonine-protein kinase] |
MODEL0911120000
— v0.0.1Final version obtained by merging of the PhyA_FHL model with On_OFF 9h long experiment This model originates from BioMod…
Details
Advances in synthetic biology will require spatio-temporal regulation of biological processes in heterologous host cells. We develop a light-switchable, two-hybrid interaction in yeast, based upon the Arabidopsis proteins PHYTOCHROME A and FAR-RED ELONGATED HYPOCOTYL 1-LIKE. Light input to this regulatory module allows dynamic control of a light-emitting LUCIFERASE reporter gene, which we detect by real-time imaging of yeast colonies on solid media.The reversible activation of the phytochrome by red light, and its inactivation by far-red light, is retained. We use this quantitative readout to construct a mathematical model that matches the system's behaviour and predicts the molecular targets for future manipulation.Our model, methods and materials together constitute a novel system for a eukaryotic host with the potential to convert a dynamic pattern of light input into a predictable gene expression response. This system could be applied for the regulation of genetic networks - both known and synthetic. link: http://identifiers.org/pubmed/19761615
MODEL1204240000
— v0.0.1This model is from the article: Microarray data can predict diurnal changes of starch content in the picoalga Ostreoco…
Details
The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation.We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga Ostreococcus tauri. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in O. tauri in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by in silico analysis.A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in O. tauri directly from time-series gene expression data. link: http://identifiers.org/pubmed/21352558
BIOMD0000000785
— v0.0.1This model describes the interaction dynamics of a lymphocyte-tumor cell population.
Details
We present a model for the interaction dynamics of lymphocytes-tumor cells population. This model reproduces all known states for the tumor. Further, we develop it taking into account periodical immunotherapy treatment with cytokines alone. A detailed analysis for the evolution of tumor cells as a function of frequency and therapy burden applied for the periodical treatment is carried out. Certain threshold values for the frequency and applied doses are derived from this analysis. So it seems possible to control and reduce the growth of the tumor. Also, constant values for cytokines doses seems to be a successful treatment. link: http://identifiers.org/doi/10.1016/S0167-2789(03)00005-8
Parameters:
Name | Description |
---|---|
alpha = 2.0 | Reaction: y_Lymphocytes =>, Rate Law: compartment*y_Lymphocytes/alpha |
k = 0.2 | Reaction: y_Lymphocytes => ; x_Malignant_Cells, Rate Law: compartment*k*x_Malignant_Cells |
sigma = 0.25 | Reaction: => y_Lymphocytes, Rate Law: compartment*sigma |
States:
Name | Description |
---|---|
x Malignant Cells | [neoplastic cell] |
y Lymphocytes | [lymphocyte] |
BIOMD0000000181
— v0.0.1The model reproduces the time profile of species depicted in Figure 12a and 12 b. The authors communicated to the curato…
Details
A novel topology of regulatory networks abstracted from the budding yeast cell cycle is studied by constructing a simple nonlinear model. A ternary positive feedback loop with only positive regulations is constructed with elements that activates the subsequent element in a clockwise fashion. A ternary negative feedback loop with only negative regulations is constructed with the elements that inhibit the subsequent element in an anticlockwise fashion. Positive feedback loop exhibits bistability, whereas the negative feedback loop exhibits limit cycle oscillations. The novelty of the topology is that the corresponding elements in these two homogeneous feedback loops are linked by the binary positive feedback loops with only positive regulations. This results in the emergence of mixed feedback loops in the network that displays complex behaviour like the coexistence of multiple steady states, relaxation oscillations and chaos. Importantly, the arrangement of the feedback loops brings in the notion of checkpoint in the model. The model also exhibits domino-like behaviour, where the limit cycle oscillations take place in a stepwise fashion. As the aforementioned topology is abstracted from the budding yeast cell cycle, the events that govern the cell cycle are considered for the present study. In budding yeast, the sequential activation of the transcription factors, cyclins and their inhibitors form mixed feedback loops. The transcription factors that involve in the positive regulation in a clockwise orientation generates ternary positive feedback loop, while the cyclins and their inhibitors that involve in the negative regulation in an anticlockwise orientation generates ternary negative feedback loop. The mutual regulation between the corresponding elements in the transcription factors and the cyclins and their inhibitors generates binary positive feedback loops. The bifurcation diagram constructed for the whole system can be related to the different events of the cell cycle in terms of dynamical system theory. The checkpoint mechanism that plays an important role in different phases of the cell cycle are accounted for by silencing appropriate feedback loops in the model. link: http://identifiers.org/pubmed/18203579
Parameters:
Name | Description |
---|---|
kc2 = 0.22 min_1 | Reaction: => T2; C2, Rate Law: compartment*kc2*C2 |
j1 = 0.9 nM_min_1 | Reaction: => T1, Rate Law: compartment*j1 |
vd1 = 6.0 nM_min_1; km1 = 5.0 nM; n = 2.0 dimensionless | Reaction: => T1; T3, Rate Law: compartment*vd1*T3^n/(km1^n+T3^n) |
k110 = 10.0 nM; n = 2.0 dimensionless; v11 = 15.0 nM_min_1 | Reaction: => C2; T2, C3, Rate Law: compartment*v11*T2^n/(k110^n+T2^n+C3^n) |
kd4 = 0.16 min_1 | Reaction: C1 =>, Rate Law: compartment*kd4*C1 |
kc1 = 0.2 min_1 | Reaction: => T1; C1, Rate Law: compartment*kc1*C1 |
j2 = 0.5 nM_min_1 | Reaction: => T2, Rate Law: compartment*j2 |
kc3 = 0.6 min_1 | Reaction: => T3; C3, Rate Law: compartment*kc3*C3 |
v12 = 15.0 nM_min_1; n = 2.0 dimensionless; k120 = 10.0 nM | Reaction: => C1; T1, C2, Rate Law: compartment*v12*T1^n/(k120^n+T1^n+C2^n) |
vd3 = 3.0 nM_min_1; km3 = 5.0 nM; n = 2.0 dimensionless | Reaction: => T3; T2, Rate Law: compartment*vd3*T2^n/(km3^n+T2^n) |
kd2 = 0.9 min_1 | Reaction: T2 =>, Rate Law: compartment*kd2*T2 |
v10 = 15.0 nM_min_1; k100 = 10.0 nM; n = 2.0 dimensionless | Reaction: => C3; T3, C1, Rate Law: compartment*v10*T3^n/(k100^n+T3^n+C1^n) |
vd2 = 1.052 nM_min_1; n = 2.0 dimensionless; km2 = 5.0 nM | Reaction: => T2; T1, Rate Law: compartment*vd2*T1^n/(km2^n+T1^n) |
kd6 = 0.16 min_1 | Reaction: C3 =>, Rate Law: compartment*kd6*C3 |
kd5 = 0.16 min_1 | Reaction: C2 =>, Rate Law: compartment*kd5*C2 |
kd3 = 0.8 min_1 | Reaction: T3 =>, Rate Law: compartment*kd3*T3 |
j3 = 0.2 nM_min_1 | Reaction: => T3, Rate Law: compartment*j3 |
kd1 = 0.8 min_1 | Reaction: T1 =>, Rate Law: compartment*kd1*T1 |
States:
Name | Description |
---|---|
T3 | [Transcriptional factor SWI5] |
C1 | [G1/S-specific cyclin CLN1; Cyclin-dependent kinase 1] |
C2 | [G2/mitotic-specific cyclin-1; Cyclin-dependent kinase 1] |
C3 | [Protein SIC1] |
T1 | [Regulatory protein SWI4] |
T2 | [Pheromone receptor transcription factor] |
BIOMD0000000196
— v0.0.1In this model the values of "free CDK" (Id: x2), "cdc25_P" (x4) "Wee1_P" (Id: y5) and "APC" (Id: y6) are assigned using…
Details
We propose a seven variable model with time delay in one of the variables for the cell cycle in higher eukaryotes. The model consists of four important phosphorylation-dephosphorylation (P-D) cycles that govern the cell cycle, namely Pre-MPF-MPF, Cdc25P-Cdc25, Wee1P-Wee1 and APCP-APC. Other variables are cyclin, free cyclin dependent kinase (Cdk) and mass. The mass acts as a G2/M checkpoint and the checkpoint is represented by a saddle node loop bifurcation. The key feature of the model is that a time lag has been introduced in the activation of anaphase promoting complex (APC) by maturation promoting factor (MPF). This is effected by treating MPF as a time-delayed variable in the activation step of APC. The time lag acts as a spindle checkpoint. Absence of time delay induces a bistability in our model. Time delay also brings about variability in G1 phase timings. The model also reproduces the mutant phenotype experiments on wee1 cells. Stochasticity has been introduced in the model to simulate the dependence of the cycle time on cell birth length. Mutant phenotypes in the stochastic model reproduce the experimental observations better than the deterministic model. link: http://identifiers.org/pubmed/16473373
Parameters:
Name | Description |
---|---|
Ka = 0.5; j1_2 = 0.01; vM1_2 = 0.55; B1 = 5.0 | Reaction: x3 => Pre_MPF; x5, Rate Law: vM1_2*(1+B1*x5/(Ka+x5))*x3/(j1_2+x3) |
Ka = 0.5; j1 = 0.01; a1 = 1.2; vM1 = 0.7 | Reaction: Pre_MPF => x3; x4, Rate Law: vM1*(1+a1*x4/(Ka+x4))*Pre_MPF/(j1+Pre_MPF) |
totwee1 = 1.0 | Reaction: y5 = totwee1-x5, Rate Law: missing |
kf = 1.0 | Reaction: x1 + x2 => Pre_MPF, Rate Law: kf*x1*x2 |
vM3 = 1.0; j3_2 = 0.01 | Reaction: y5 => x5, Rate Law: vM3*(1-x5)/((j3_2+1)-x5) |
B3 = 1.0; Ka = 0.5; j3 = 0.01; vM3_2 = 1.0 | Reaction: x5 => y5; x3, m, Rate Law: vM3_2*(1+B3*m*x3/(Ka+m*x3))*x5/(j3+x5) |
totcdc25 = 1.0 | Reaction: x4 = totcdc25-y4, Rate Law: missing |
kd = 0.2; B2 = 3.3 | Reaction: x3 => x2; x6, Rate Law: kd*(1+B2*x6)*x3 |
c = 1.1 | Reaction: x2 = (c-Pre_MPF)-x3, Rate Law: missing |
a = 10.0; mu = 0.01 | Reaction: => m, Rate Law: mu*m*(1-m/a) |
vM2_2 = 1.0; j2_2 = 0.01 | Reaction: x4 => y4, Rate Law: vM2_2*x4/(j2_2+x4) |
Ka = 0.5; vM2 = 0.41; j2 = 0.01; a2 = 1.0 | Reaction: y4 => x4; m, x3, Rate Law: vM2*(1+a2*m*x3/(Ka+m*x3))*(1-x4)/((j2+1)-x4) |
vf = 0.215 | Reaction: => x1, Rate Law: vf |
Bc = 3.5; kc = 0.05 | Reaction: x1 => ; x6, Rate Law: x1*(kc+Bc*x6) |
j4_2 = 0.01; vM4_2 = 1.0 | Reaction: x6 => y6, Rate Law: vM4_2*x6/(j4_2+x6) |
Ka = 0.5; j4 = 0.01; a4 = 2.0; vM4 = 0.7; tau = 5.0 | Reaction: y6 => x6; m, x3, Rate Law: vM4*(1+a4*m*delay(x3, tau)/(Ka+m*delay(x3, tau)))*(1-x6)/(j4+(1-x6)) |
totAPC = 1.0 | Reaction: y6 = totAPC-x6, Rate Law: missing |
States:
Name | Description |
---|---|
x5 | [Wee1-like protein kinase] |
Pre MPF | [Cyclin-dependent kinase 1; IPR015454; MPF complex; cyclin-dependent protein kinase holoenzyme complex] |
x4 | [Cell division control protein 25; M-phase inducer phosphatase 1; Phosphoprotein] |
x6 | [Phosphoprotein; anaphase-promoting complex] |
x2 | [Cyclin-dependent kinase 1; protein kinase activity] |
x3 | [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin-B1; MPF complex] |
x1 | [G2/mitotic-specific cyclin-B1; cyclin-dependent protein serine/threonine kinase regulator activity] |
y4 | [Cell division control protein 25; M-phase inducer phosphatase 1] |
m | [cell] |
y6 | [anaphase-promoting complex] |
y5 | [Wee1-like protein kinase; Phosphoprotein] |
BIOMD0000000497
— v0.0.1Stanford2013 - Kinetic model of yeast metabolic network (standard)Large-scale model construction based on a logical laye…
Details
The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. link: http://identifiers.org/pubmed/24324546
Parameters:
Name | Description |
---|---|
Vmax_r_0529=4.51989; kmp_s_0735r_0529=0.601873; kms_s_1315r_0529=12.8511; Keq_r_0529=0.0515178; kms_s_0657r_0529=0.549; kmp_s_0659r_0529=0.549 | Reaction: s_0657 + s_1315 => s_0659 + s_0735; s_0657, s_0659, s_0735, s_1315, Rate Law: intracellular*Vmax_r_0529*(1/kms_s_0657r_0529)^1*(1/kms_s_1315r_0529)^1*(s_0657^1*s_1315^1-s_0659^1*s_0735^1/Keq_r_0529)/(((1+s_0657/kms_s_0657r_0529)*(1+s_1315/kms_s_1315r_0529)+(1+s_0659/kmp_s_0659r_0529)*(1+s_0735/kmp_s_0735r_0529))-1)/intracellular |
kmp_s_0692r_0171=0.549; Keq_r_0171=1.38552; kmp_s_0434r_0171=1.25956; Vmax_r_0171=0.395998; kms_s_1053r_0171=0.549 | Reaction: s_1053 => s_0434 + s_0692; s_0434, s_0692, s_1053, Rate Law: intracellular*Vmax_r_0171*(1/kms_s_1053r_0171)^1*(s_1053^1-s_0434^1*s_0692^1/Keq_r_0171)/((1+s_1053/kms_s_1053r_0171+(1+s_0434/kmp_s_0434r_0171)*(1+s_0692/kmp_s_0692r_0171))-1)/intracellular |
Vmax_r_1036=0.14014; kms_s_0731r_1036=0.0436363; kmp_s_0561r_1036=0.549; Keq_r_1036=13.8394; kmp_s_0427r_1036=0.549; kms_s_1304r_1036=0.549 | Reaction: s_0731 + s_1304 => s_0427 + s_0561; s_0427, s_0561, s_0731, s_1304, Rate Law: intracellular*Vmax_r_1036*(1/kms_s_0731r_1036)^1*(1/kms_s_1304r_1036)^1*(s_0731^1*s_1304^1-s_0427^1*s_0561^1/Keq_r_1036)/(((1+s_0731/kms_s_0731r_1036)*(1+s_1304/kms_s_1304r_1036)+(1+s_0427/kmp_s_0427r_1036)*(1+s_0561/kmp_s_0561r_1036))-1)/intracellular |
kms_s_1096r_0467=0.549; kmp_s_0514r_0467=0.549; kms_s_0763_br_0467=0.549; kms_s_1187r_0467=0.549; Vmax_r_0467=0.00599719; Keq_r_0467=3.64962; kmp_s_1091r_0467=0.549; kms_s_1005r_0467=0.549; kmp_s_1334r_0467=0.549; kmp_s_0470r_0467=1.0; kmp_s_1434_br_0467=0.549 | Reaction: s_0763_b + s_1005 + s_1096 + s_1187 => s_0470 + s_0514 + s_1091 + s_1334 + s_1434_b; s_0470, s_0514, s_0763_b, s_1005, s_1091, s_1096, s_1187, s_1334, s_1434_b, Rate Law: intracellular*Vmax_r_0467*(1/kms_s_0763_br_0467)^3*(1/kms_s_1005r_0467)^1*(1/kms_s_1096r_0467)^2*(1/kms_s_1187r_0467)^1*(s_0763_b^3*s_1005^1*s_1096^2*s_1187^1-s_0470^1*s_0514^1*s_1091^2*s_1334^1*s_1434_b^1/Keq_r_0467)/(((1+s_0763_b/kms_s_0763_br_0467)*(1+s_1005/kms_s_1005r_0467)*(1+s_1096/kms_s_1096r_0467)*(1+s_1187/kms_s_1187r_0467)+(1+s_0470/kmp_s_0470r_0467)*(1+s_0514/kmp_s_0514r_0467)*(1+s_1091/kmp_s_1091r_0467)*(1+s_1334/kmp_s_1334r_0467)*(1+s_1434_b/kmp_s_1434_br_0467))-1)/intracellular |
Vmax_r_1038=0.1001; kms_s_1434_br_1038=0.549; Keq_r_1038=1.1; kms_s_0419r_1038=0.549; kmp_s_0416r_1038=0.549; kmp_s_1207r_1038=0.549 | Reaction: s_0419 + s_1434_b => s_0416 + s_1207; s_0416, s_0419, s_1207, s_1434_b, Rate Law: intracellular*Vmax_r_1038*(1/kms_s_0419r_1038)^1*(1/kms_s_1434_br_1038)^1*(s_0419^1*s_1434_b^1-s_0416^1*s_1207^1/Keq_r_1038)/(((1+s_0419/kms_s_0419r_1038)*(1+s_1434_b/kms_s_1434_br_1038)+(1+s_0416/kmp_s_0416r_1038)*(1+s_1207/kmp_s_1207r_1038))-1)/intracellular |
Keq_r_0886=0.950614; kmp_s_0009r_0886=0.549; kms_s_0446r_0886=1.09208; kmp_s_1207r_0886=0.549; kmp_s_0763_br_0886=0.549; kms_s_0318r_0886=0.549; Vmax_r_0886=1.53571; kms_s_0881r_0886=0.549; kmp_s_0400r_0886=1.71907 | Reaction: s_0318 + s_0446 + s_0881 => s_0009 + s_0400 + s_0763_b + s_1207; s_0009, s_0318, s_0400, s_0446, s_0763_b, s_0881, s_1207, Rate Law: intracellular*Vmax_r_0886*(1/kms_s_0318r_0886)^1*(1/kms_s_0446r_0886)^1*(1/kms_s_0881r_0886)^1*(s_0318^1*s_0446^1*s_0881^1-s_0009^1*s_0400^1*s_0763_b^1*s_1207^1/Keq_r_0886)/(((1+s_0318/kms_s_0318r_0886)*(1+s_0446/kms_s_0446r_0886)*(1+s_0881/kms_s_0881r_0886)+(1+s_0009/kmp_s_0009r_0886)*(1+s_0400/kmp_s_0400r_0886)*(1+s_0763_b/kmp_s_0763_br_0886)*(1+s_1207/kmp_s_1207r_0886))-1)/intracellular |
kmp_s_0514r_0442=0.549; kmp_s_1132r_0442=0.549; Vmax_r_0442=0.001914; kms_s_0605r_0442=0.549; kmp_s_0446r_0442=1.09208; kms_s_0434r_0442=1.25956; kms_s_1140r_0442=0.549; Keq_r_0442=0.953736 | Reaction: s_0434 + s_0605 + s_1140 => s_0446 + s_0514 + s_1132; s_0434, s_0446, s_0514, s_0605, s_1132, s_1140, Rate Law: intracellular*Vmax_r_0442*(1/kms_s_0434r_0442)^1*(1/kms_s_0605r_0442)^1*(1/kms_s_1140r_0442)^1*(s_0434^1*s_0605^1*s_1140^1-s_0446^1*s_0514^1*s_1132^1/Keq_r_0442)/(((1+s_0434/kms_s_0434r_0442)*(1+s_0605/kms_s_0605r_0442)*(1+s_1140/kms_s_1140r_0442)+(1+s_0446/kmp_s_0446r_0442)*(1+s_0514/kmp_s_0514r_0442)*(1+s_1132/kmp_s_1132r_0442))-1)/intracellular |
Keq_r_0528=0.0128394; Vmax_r_0528=3.48809; kmp_s_0732r_0528=0.15; kms_s_1315r_0528=12.8511; kms_s_1434_br_0528=0.549; kmp_s_1207r_0528=0.549 | Reaction: s_1315 + s_1434_b => s_0732 + s_1207; s_0732, s_1207, s_1315, s_1434_b, Rate Law: intracellular*Vmax_r_0528*(1/kms_s_1315r_0528)^1*(1/kms_s_1434_br_0528)^1*(s_1315^1*s_1434_b^1-s_0732^1*s_1207^1/Keq_r_0528)/(((1+s_1315/kms_s_1315r_0528)*(1+s_1434_b/kms_s_1434_br_0528)+(1+s_0732/kmp_s_0732r_0528)*(1+s_1207/kmp_s_1207r_0528))-1)/intracellular |
Vmax_r_0021=1.60931; Keq_r_0021=40.5765; kms_s_1243r_0021=0.0271093; kms_s_1434_br_0021=0.549; kmp_s_0356r_0021=0.549; kmp_s_1207r_0021=0.549; kms_s_0533r_0021=0.549 | Reaction: s_0533 + s_1243 + s_1434_b => s_0356 + s_1207; s_0356, s_0533, s_1207, s_1243, s_1434_b, Rate Law: intracellular*Vmax_r_0021*(1/kms_s_0533r_0021)^1*(1/kms_s_1243r_0021)^1*(1/kms_s_1434_br_0021)^1*(s_0533^1*s_1243^1*s_1434_b^1-s_0356^1*s_1207^1/Keq_r_0021)/(((1+s_0533/kms_s_0533r_0021)*(1+s_1243/kms_s_1243r_0021)*(1+s_1434_b/kms_s_1434_br_0021)+(1+s_0356/kmp_s_0356r_0021)*(1+s_1207/kmp_s_1207r_0021))-1)/intracellular |
kms_s_0366r_0183=0.120104; kmp_s_1082r_0183=1.50326; kms_s_0763_br_0183=0.549; kms_s_1087r_0183=0.0867353; Keq_r_0183=14456.7; Vmax_r_0183=99.1; kmp_s_0650r_0183=50.0 | Reaction: s_0366 + s_0763_b + s_1087 => s_0650 + s_1082; s_0366, s_0650, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0183*(1/kms_s_0366r_0183)^1*(1/kms_s_0763_br_0183)^1*(1/kms_s_1087r_0183)^1*(s_0366^1*s_0763_b^1*s_1087^1-s_0650^1*s_1082^1/Keq_r_0183)/(((1+s_0366/kms_s_0366r_0183)*(1+s_0763_b/kms_s_0763_br_0183)*(1+s_1087/kms_s_1087r_0183)+(1+s_0650/kmp_s_0650r_0183)*(1+s_1082/kmp_s_1082r_0183))-1)/intracellular |
Keq_r_0465=3.64962; kmp_s_0470r_0465=1.0; kmp_s_1044r_0465=0.549; kms_s_0763_br_0465=0.549; kmp_s_1091r_0465=0.549; kms_s_0977r_0465=0.549; Vmax_r_0465=0.0179399; kms_s_1005r_0465=0.549; kmp_s_0514r_0465=0.549; kmp_s_1434_br_0465=0.549; kms_s_1096r_0465=0.549 | Reaction: s_0763_b + s_0977 + s_1005 + s_1096 => s_0470 + s_0514 + s_1044 + s_1091 + s_1434_b; s_0470, s_0514, s_0763_b, s_0977, s_1005, s_1044, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0465*(1/kms_s_0763_br_0465)^3*(1/kms_s_0977r_0465)^1*(1/kms_s_1005r_0465)^1*(1/kms_s_1096r_0465)^2*(s_0763_b^3*s_0977^1*s_1005^1*s_1096^2-s_0470^1*s_0514^1*s_1044^1*s_1091^2*s_1434_b^1/Keq_r_0465)/(((1+s_0763_b/kms_s_0763_br_0465)*(1+s_0977/kms_s_0977r_0465)*(1+s_1005/kms_s_1005r_0465)*(1+s_1096/kms_s_1096r_0465)+(1+s_0470/kmp_s_0470r_0465)*(1+s_0514/kmp_s_0514r_0465)*(1+s_1044/kmp_s_1044r_0465)*(1+s_1091/kmp_s_1091r_0465)*(1+s_1434_b/kmp_s_1434_br_0465))-1)/intracellular |
kms_s_0069r_0485=0.549; Keq_r_0485=0.6039; Vmax_r_0485=2.08449; kmp_s_0692r_0485=0.549; kmp_s_1434_br_0485=0.549 | Reaction: s_0069 => s_0692 + s_1434_b; s_0069, s_0692, s_1434_b, Rate Law: intracellular*Vmax_r_0485*(1/kms_s_0069r_0485)^1*(s_0069^1-s_0692^1*s_1434_b^1/Keq_r_0485)/((1+s_0069/kms_s_0069r_0485+(1+s_0692/kmp_s_0692r_0485)*(1+s_1434_b/kmp_s_1434_br_0485))-1)/intracellular |
Vmax_r_0509=38.2031; kmp_s_0899r_0509=0.549; kms_s_0763_br_0509=0.549; kms_s_1096r_0509=0.549; kmp_s_1091r_0509=0.549; kms_s_0185r_0509=0.549; Keq_r_0509=2.00364; kmp_s_1434_br_0509=0.549; kms_s_0430r_0509=0.549 | Reaction: s_0185 + s_0430 + s_0763_b + s_1096 => s_0899 + s_1091 + s_1434_b; s_0185, s_0430, s_0763_b, s_0899, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0509*(1/kms_s_0185r_0509)^1*(1/kms_s_0430r_0509)^1*(1/kms_s_0763_br_0509)^1*(1/kms_s_1096r_0509)^1*(s_0185^1*s_0430^1*s_0763_b^1*s_1096^1-s_0899^1*s_1091^1*s_1434_b^1/Keq_r_0509)/(((1+s_0185/kms_s_0185r_0509)*(1+s_0430/kms_s_0430r_0509)*(1+s_0763_b/kms_s_0763_br_0509)*(1+s_1096/kms_s_1096r_0509)+(1+s_0899/kmp_s_0899r_0509)*(1+s_1091/kmp_s_1091r_0509)*(1+s_1434_b/kmp_s_1434_br_0509))-1)/intracellular |
kms_s_0635r_0995=0.549; kmp_s_1434_br_0995=0.549; Keq_r_0995=1.1; Vmax_r_0995=0.0034727; kms_s_0663r_0995=0.549; kmp_s_0641r_0995=0.549 | Reaction: s_0635 + s_0663 => s_0641 + s_1434_b; s_0635, s_0641, s_0663, s_1434_b, Rate Law: intracellular*Vmax_r_0995*(1/kms_s_0635r_0995)^1*(1/kms_s_0663r_0995)^1*(s_0635^1*s_0663^1-s_0641^1*s_1434_b^1/Keq_r_0995)/(((1+s_0635/kms_s_0635r_0995)*(1+s_0663/kms_s_0663r_0995)+(1+s_0641/kmp_s_0641r_0995)*(1+s_1434_b/kmp_s_1434_br_0995))-1)/intracellular |
kmp_s_0530r_0351=0.549; kms_s_1087r_0351=0.0867353; Vmax_r_0351=3.30331; Keq_r_0351=34.7263; kms_s_0763_br_0351=0.549; kmp_s_1082r_0351=1.50326; kms_s_0529r_0351=0.549 | Reaction: s_0529 + s_0763_b + s_1087 => s_0530 + s_1082; s_0529, s_0530, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0351*(1/kms_s_0529r_0351)^1*(1/kms_s_0763_br_0351)^1*(1/kms_s_1087r_0351)^1*(s_0529^1*s_0763_b^1*s_1087^1-s_0530^1*s_1082^1/Keq_r_0351)/(((1+s_0529/kms_s_0529r_0351)*(1+s_0763_b/kms_s_0763_br_0351)*(1+s_1087/kms_s_1087r_0351)+(1+s_0530/kmp_s_0530r_0351)*(1+s_1082/kmp_s_1082r_0351))-1)/intracellular |
kms_s_0446r_0891=1.09208; kmp_s_0331r_0891=0.549; kms_s_0427r_0891=0.549; Keq_r_0891=0.696514; kmp_s_0434r_0891=1.25956; Vmax_r_0891=2.25059; kmp_s_0763_br_0891=0.549 | Reaction: s_0427 + s_0446 => s_0331 + s_0434 + s_0763_b; s_0331, s_0427, s_0434, s_0446, s_0763_b, Rate Law: intracellular*Vmax_r_0891*(1/kms_s_0427r_0891)^1*(1/kms_s_0446r_0891)^1*(s_0427^1*s_0446^1-s_0331^1*s_0434^1*s_0763_b^1/Keq_r_0891)/(((1+s_0427/kms_s_0427r_0891)*(1+s_0446/kms_s_0446r_0891)+(1+s_0331/kmp_s_0331r_0891)*(1+s_0434/kmp_s_0434r_0891)*(1+s_0763_b/kmp_s_0763_br_0891))-1)/intracellular |
Keq_r_0031=2.00364; kmp_s_0297r_0031=0.549; kmp_s_0470r_0031=1.0; kms_s_0010r_0031=0.549; Vmax_r_0031=1.0703; kms_s_0763_br_0031=0.549 | Reaction: s_0010 + s_0763_b => s_0297 + s_0470; s_0010, s_0297, s_0470, s_0763_b, Rate Law: intracellular*Vmax_r_0031*(1/kms_s_0010r_0031)^1*(1/kms_s_0763_br_0031)^1*(s_0010^1*s_0763_b^1-s_0297^1*s_0470^1/Keq_r_0031)/(((1+s_0010/kms_s_0010r_0031)*(1+s_0763_b/kms_s_0763_br_0031)+(1+s_0297/kmp_s_0297r_0031)*(1+s_0470/kmp_s_0470r_0031))-1)/intracellular |
kmp_s_0079r_0881=0.549; Keq_r_0881=2.00364; kms_s_1434_br_0881=0.549; Vmax_r_0881=0.229351; kms_s_0080r_0881=0.549 | Reaction: s_0080 + s_1434_b => s_0079; s_0079, s_0080, s_1434_b, Rate Law: intracellular*Vmax_r_0881*(1/kms_s_0080r_0881)^1*(1/kms_s_1434_br_0881)^1*(s_0080^1*s_1434_b^1-s_0079^1/Keq_r_0881)/(((1+s_0080/kms_s_0080r_0881)*(1+s_1434_b/kms_s_1434_br_0881)+1+s_0079/kmp_s_0079r_0881)-1)/intracellular |
kms_s_0315r_0604=0.549; kms_s_0907r_0604=0.549; kmp_s_0763_br_0604=0.549; Vmax_r_0604=0.871524; kmp_s_0899r_0604=0.549; kmp_s_0317r_0604=0.549; Keq_r_0604=0.331541; kmp_s_0532r_0604=0.549 | Reaction: s_0315 + s_0907 => s_0317 + s_0532 + s_0763_b + s_0899; s_0315, s_0317, s_0532, s_0763_b, s_0899, s_0907, Rate Law: intracellular*Vmax_r_0604*(1/kms_s_0315r_0604)^1*(1/kms_s_0907r_0604)^1*(s_0315^1*s_0907^1-s_0317^1*s_0532^1*s_0763_b^1*s_0899^1/Keq_r_0604)/(((1+s_0315/kms_s_0315r_0604)*(1+s_0907/kms_s_0907r_0604)+(1+s_0317/kmp_s_0317r_0604)*(1+s_0532/kmp_s_0532r_0604)*(1+s_0763_b/kmp_s_0763_br_0604)*(1+s_0899/kmp_s_0899r_0604))-1)/intracellular |
kmp_s_0564r_0360=0.549; kmp_s_0446r_0360=1.09208; Keq_r_0360=0.698801; Vmax_r_0360=0.015323; kms_s_0400r_0360=1.71907; kms_s_0562r_0360=0.549 | Reaction: s_0400 + s_0562 => s_0446 + s_0564; s_0400, s_0446, s_0562, s_0564, Rate Law: intracellular*Vmax_r_0360*(1/kms_s_0400r_0360)^1*(1/kms_s_0562r_0360)^1*(s_0400^1*s_0562^1-s_0446^1*s_0564^1/Keq_r_0360)/(((1+s_0400/kms_s_0400r_0360)*(1+s_0562/kms_s_0562r_0360)+(1+s_0446/kmp_s_0446r_0360)*(1+s_0564/kmp_s_0564r_0360))-1)/intracellular |
Keq_r_0064=0.0348439; kms_s_1082r_0064=1.50326; Vmax_r_0064=1.68189; kmp_s_0763_br_0064=0.549; kmp_s_1087r_0064=0.0867353; kms_s_0008r_0064=0.549; kmp_s_0010r_0064=0.549 | Reaction: s_0008 + s_1082 => s_0010 + s_0763_b + s_1087; s_0008, s_0010, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0064*(1/kms_s_0008r_0064)^1*(1/kms_s_1082r_0064)^1*(s_0008^1*s_1082^1-s_0010^1*s_0763_b^1*s_1087^1/Keq_r_0064)/(((1+s_0008/kms_s_0008r_0064)*(1+s_1082/kms_s_1082r_0064)+(1+s_0010/kmp_s_0010r_0064)*(1+s_0763_b/kmp_s_0763_br_0064)*(1+s_1087/kmp_s_1087r_0064))-1)/intracellular |
kmp_s_0763_br_0856=0.549; kmp_s_1517r_0856=0.549; kms_s_1521r_0856=0.549; Vmax_r_0856=1.07843; Keq_r_0856=0.182016; kmp_s_1349r_0856=0.549; kmp_s_0397r_0856=0.549; kms_s_0206r_0856=0.549 | Reaction: s_0206 + s_1521 => s_0397 + s_0763_b + s_1349 + s_1517; s_0206, s_0397, s_0763_b, s_1349, s_1517, s_1521, Rate Law: intracellular*Vmax_r_0856*(1/kms_s_0206r_0856)^1*(1/kms_s_1521r_0856)^1*(s_0206^1*s_1521^1-s_0397^1*s_0763_b^2*s_1349^1*s_1517^1/Keq_r_0856)/(((1+s_0206/kms_s_0206r_0856)*(1+s_1521/kms_s_1521r_0856)+(1+s_0397/kmp_s_0397r_0856)*(1+s_0763_b/kmp_s_0763_br_0856)*(1+s_1349/kmp_s_1349r_0856)*(1+s_1517/kmp_s_1517r_0856))-1)/intracellular |
kms_s_0539r_1035=0.104555; Vmax_r_1035=0.14014; kms_s_0533r_1035=0.549; kmp_s_0731r_1035=0.0436363; kmp_s_1304r_1035=0.549; Keq_r_1035=0.459088 | Reaction: s_0533 + s_0539 => s_0731 + s_1304; s_0533, s_0539, s_0731, s_1304, Rate Law: intracellular*Vmax_r_1035*(1/kms_s_0533r_1035)^1*(1/kms_s_0539r_1035)^1*(s_0533^1*s_0539^1-s_0731^1*s_1304^1/Keq_r_1035)/(((1+s_0533/kms_s_0533r_1035)*(1+s_0539/kms_s_0539r_1035)+(1+s_0731/kmp_s_0731r_1035)*(1+s_1304/kmp_s_1304r_1035))-1)/intracellular |
kms_s_0561r_0965=0.549; Keq_r_0965=1.1; Vmax_r_0965=0.5577; kmp_s_0557r_0965=0.549 | Reaction: s_0561 => s_0557; s_0557, s_0561, Rate Law: intracellular*Vmax_r_0965*(1/kms_s_0561r_0965)^1*(s_0561^1-s_0557^1/Keq_r_0965)/((1+s_0561/kms_s_0561r_0965+1+s_0557/kmp_s_0557r_0965)-1)/intracellular |
kmp_s_0386r_1672=0.549; Keq_r_1672=1.1; Vmax_r_1672=0.026268; kms_s_1342r_1672=0.549 | Reaction: s_1342 => s_0386; s_0386, s_1342, Rate Law: intracellular*Vmax_r_1672*(1/kms_s_1342r_1672)^1*(s_1342^1-s_0386^1/Keq_r_1672)/((1+s_1342/kms_s_1342r_1672+1+s_0386/kmp_s_0386r_1672)-1)/intracellular |
kmp_s_0400r_0573=1.71907; Vmax_r_0573=1.99579; kms_s_0545r_0573=0.0987587; kmp_s_0410r_0573=0.549; Keq_r_0573=2000.0; kms_s_0446r_0573=1.09208; kmp_s_0763_br_0573=0.549 | Reaction: s_0446 + s_0545 => s_0400 + s_0410 + s_0763_b; s_0400, s_0410, s_0446, s_0545, s_0763_b, Rate Law: intracellular*Vmax_r_0573*(1/kms_s_0446r_0573)^1*(1/kms_s_0545r_0573)^1*(s_0446^1*s_0545^1-s_0400^1*s_0410^1*s_0763_b^1/Keq_r_0573)/(((1+s_0446/kms_s_0446r_0573)*(1+s_0545/kms_s_0545r_0573)+(1+s_0400/kmp_s_0400r_0573)*(1+s_0410/kmp_s_0410r_0573)*(1+s_0763_b/kmp_s_0763_br_0573))-1)/intracellular |
kmp_s_0763_br_0526=0.549; Keq_r_0526=2.21027; kmp_s_1096r_0526=0.549; kms_s_1091r_0526=0.549; Vmax_r_0526=5.48128; kmp_s_0734r_0526=0.549; kms_s_0732r_0526=0.15 | Reaction: s_0732 + s_1091 => s_0734 + s_0763_b + s_1096; s_0732, s_0734, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0526*(1/kms_s_0732r_0526)^1*(1/kms_s_1091r_0526)^1*(s_0732^1*s_1091^1-s_0734^1*s_0763_b^1*s_1096^1/Keq_r_0526)/(((1+s_0732/kms_s_0732r_0526)*(1+s_1091/kms_s_1091r_0526)+(1+s_0734/kmp_s_0734r_0526)*(1+s_0763_b/kmp_s_0763_br_0526)*(1+s_1096/kmp_s_1096r_0526))-1)/intracellular |
kmp_s_0533r_1037=0.549; Keq_r_1037=72.6682; kmp_s_0561r_1037=0.549; kms_s_0731r_1037=0.0436363; Vmax_r_1037=1.1627; kms_s_0539r_1037=0.104555 | Reaction: s_0539 + s_0731 => s_0533 + s_0561; s_0533, s_0539, s_0561, s_0731, Rate Law: intracellular*Vmax_r_1037*(1/kms_s_0539r_1037)^1*(1/kms_s_0731r_1037)^1*(s_0539^1*s_0731^1-s_0533^1*s_0561^1/Keq_r_1037)/(((1+s_0539/kms_s_0539r_1037)*(1+s_0731/kms_s_0731r_1037)+(1+s_0533/kmp_s_0533r_1037)*(1+s_0561/kmp_s_0561r_1037))-1)/intracellular |
kms_s_0755r_0170=0.549; kms_s_0816r_0170=0.549; Keq_r_0170=0.331541; kmp_s_1053r_0170=0.549; kmp_s_0706r_0170=0.549; kmp_s_1207r_0170=0.549; kmp_s_0763_br_0170=0.549; kms_s_0881r_0170=0.549; Vmax_r_0170=1.8216 | Reaction: s_0755 + s_0816 + s_0881 => s_0706 + s_0763_b + s_1053 + s_1207; s_0706, s_0755, s_0763_b, s_0816, s_0881, s_1053, s_1207, Rate Law: intracellular*Vmax_r_0170*(1/kms_s_0755r_0170)^1*(1/kms_s_0816r_0170)^1*(1/kms_s_0881r_0170)^1*(s_0755^1*s_0816^1*s_0881^1-s_0706^1*s_0763_b^2*s_1053^1*s_1207^1/Keq_r_0170)/(((1+s_0755/kms_s_0755r_0170)*(1+s_0816/kms_s_0816r_0170)*(1+s_0881/kms_s_0881r_0170)+(1+s_0706/kmp_s_0706r_0170)*(1+s_0763_b/kmp_s_0763_br_0170)*(1+s_1053/kmp_s_1053r_0170)*(1+s_1207/kmp_s_1207r_0170))-1)/intracellular |
kmp_s_0317r_0169=0.549; kms_s_0009r_0169=0.549; Vmax_r_0169=0.333848; Keq_r_0169=0.6039; kmp_s_0692r_0169=0.549 | Reaction: s_0009 => s_0317 + s_0692; s_0009, s_0317, s_0692, Rate Law: intracellular*Vmax_r_0169*(1/kms_s_0009r_0169)^1*(s_0009^1-s_0317^1*s_0692^1/Keq_r_0169)/((1+s_0009/kms_s_0009r_0169+(1+s_0317/kmp_s_0317r_0169)*(1+s_0692/kmp_s_0692r_0169))-1)/intracellular |
Vmax_r_0936=0.863944; kmp_s_1091r_0936=0.549; kms_s_0763_br_0936=0.549; Keq_r_0936=3.64962; kms_s_0120r_0936=0.549; kmp_s_0939r_0936=0.549; kms_s_1096r_0936=0.549 | Reaction: s_0120 + s_0763_b + s_1096 => s_0939 + s_1091; s_0120, s_0763_b, s_0939, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0936*(1/kms_s_0120r_0936)^1*(1/kms_s_0763_br_0936)^2*(1/kms_s_1096r_0936)^1*(s_0120^1*s_0763_b^2*s_1096^1-s_0939^1*s_1091^1/Keq_r_0936)/(((1+s_0120/kms_s_0120r_0936)*(1+s_0763_b/kms_s_0763_br_0936)*(1+s_1096/kms_s_1096r_0936)+(1+s_0939/kmp_s_0939r_0936)*(1+s_1091/kmp_s_1091r_0936))-1)/intracellular |
kms_s_0514r_0437=0.549; kmp_s_0434r_0437=1.25956; Keq_r_0437=1.26869; kmp_s_1355r_0437=0.549; kms_s_0987r_0437=0.549; Vmax_r_0437=0.0038115; kms_s_0446r_0437=1.09208; kmp_s_0605r_0437=0.549 | Reaction: s_0446 + s_0514 + s_0987 => s_0434 + s_0605 + s_1355; s_0434, s_0446, s_0514, s_0605, s_0987, s_1355, Rate Law: intracellular*Vmax_r_0437*(1/kms_s_0446r_0437)^1*(1/kms_s_0514r_0437)^1*(1/kms_s_0987r_0437)^1*(s_0446^1*s_0514^1*s_0987^1-s_0434^1*s_0605^1*s_1355^1/Keq_r_0437)/(((1+s_0446/kms_s_0446r_0437)*(1+s_0514/kms_s_0514r_0437)*(1+s_0987/kms_s_0987r_0437)+(1+s_0434/kmp_s_0434r_0437)*(1+s_0605/kmp_s_0605r_0437)*(1+s_1355/kmp_s_1355r_0437))-1)/intracellular |
Vmax_r_0875=1.5048; Keq_r_0875=1.1; kms_s_0554r_0875=0.549; kmp_s_0553r_0875=0.549 | Reaction: s_0554 => s_0553; s_0553, s_0554, Rate Law: intracellular*Vmax_r_0875*(1/kms_s_0554r_0875)^1*(s_0554^1-s_0553^1/Keq_r_0875)/((1+s_0554/kms_s_0554r_0875+1+s_0553/kmp_s_0553r_0875)-1)/intracellular |
Keq_r_0725=1.1; kmp_s_1207r_0725=0.549; kms_s_1434_br_0725=0.549; kms_s_0128r_0725=0.549; Vmax_r_0725=0.006545; kmp_s_1020r_0725=0.549 | Reaction: s_0128 + s_1434_b => s_1020 + s_1207; s_0128, s_1020, s_1207, s_1434_b, Rate Law: intracellular*Vmax_r_0725*(1/kms_s_0128r_0725)^1*(1/kms_s_1434_br_0725)^1*(s_0128^1*s_1434_b^1-s_1020^1*s_1207^1/Keq_r_0725)/(((1+s_0128/kms_s_0128r_0725)*(1+s_1434_b/kms_s_1434_br_0725)+(1+s_1020/kmp_s_1020r_0725)*(1+s_1207/kmp_s_1207r_0725))-1)/intracellular |
kms_s_0446r_0130=1.09208; Vmax_r_0130=0.58058; kmp_s_1070r_0130=0.549; Keq_r_0130=1.73154; kmp_s_0400r_0130=1.71907; kms_s_1071r_0130=0.549 | Reaction: s_0446 + s_1071 => s_0400 + s_1070; s_0400, s_0446, s_1070, s_1071, Rate Law: intracellular*Vmax_r_0130*(1/kms_s_0446r_0130)^1*(1/kms_s_1071r_0130)^1*(s_0446^1*s_1071^1-s_0400^1*s_1070^1/Keq_r_0130)/(((1+s_0446/kms_s_0446r_0130)*(1+s_1071/kms_s_1071r_0130)+(1+s_0400/kmp_s_0400r_0130)*(1+s_1070/kmp_s_1070r_0130))-1)/intracellular |
Vmax_r_0225=0.414697; kms_s_0017r_0225=0.549; kmp_s_0692r_0225=0.549; Keq_r_0225=0.6039; kmp_s_0873r_0225=0.549 | Reaction: s_0017 => s_0692 + s_0873; s_0017, s_0692, s_0873, Rate Law: intracellular*Vmax_r_0225*(1/kms_s_0017r_0225)^1*(s_0017^1-s_0692^1*s_0873^1/Keq_r_0225)/((1+s_0017/kms_s_0017r_0225+(1+s_0692/kmp_s_0692r_0225)*(1+s_0873/kmp_s_0873r_0225))-1)/intracellular |
kmp_s_1207r_0728=0.549; kmp_s_0149r_0728=0.549; kms_s_1070r_0728=0.549; kms_s_0763_br_0728=0.549; Vmax_r_0728=1.2441; Keq_r_0728=1.1; kms_s_1096r_0728=0.549; kmp_s_1091r_0728=0.549 | Reaction: s_0763_b + s_1070 + s_1096 => s_0149 + s_1091 + s_1207; s_0149, s_0763_b, s_1070, s_1091, s_1096, s_1207, Rate Law: intracellular*Vmax_r_0728*(1/kms_s_0763_br_0728)^1*(1/kms_s_1070r_0728)^1*(1/kms_s_1096r_0728)^1*(s_0763_b^1*s_1070^1*s_1096^1-s_0149^1*s_1091^1*s_1207^1/Keq_r_0728)/(((1+s_0763_b/kms_s_0763_br_0728)*(1+s_1070/kms_s_1070r_0728)*(1+s_1096/kms_s_1096r_0728)+(1+s_0149/kmp_s_0149r_0728)*(1+s_1091/kmp_s_1091r_0728)*(1+s_1207/kmp_s_1207r_0728))-1)/intracellular |
kmp_s_0514r_0534=0.549; kms_s_0386r_0534=0.549; kms_s_1315r_0534=12.8511; Vmax_r_0534=0.0421077; kmp_s_0763_br_0534=0.549; Keq_r_0534=0.0141635; kmp_s_0083r_0534=0.549 | Reaction: s_0386 + s_1315 => s_0083 + s_0514 + s_0763_b; s_0083, s_0386, s_0514, s_0763_b, s_1315, Rate Law: intracellular*Vmax_r_0534*(1/kms_s_0386r_0534)^1*(1/kms_s_1315r_0534)^1*(s_0386^1*s_1315^1-s_0083^1*s_0514^1*s_0763_b^2/Keq_r_0534)/(((1+s_0386/kms_s_0386r_0534)*(1+s_1315/kms_s_1315r_0534)+(1+s_0083/kmp_s_0083r_0534)*(1+s_0514/kmp_s_0514r_0534)*(1+s_0763_b/kmp_s_0763_br_0534))-1)/intracellular |
kms_s_0816r_0607=0.549; kms_s_1434_br_0607=0.549; Vmax_r_0607=0.501598; Keq_r_0607=0.063468; kmp_s_0306r_0607=0.549; kmp_s_1087r_0607=0.0867353; kms_s_1082r_0607=1.50326; kmp_s_0763_br_0607=0.549 | Reaction: s_0816 + s_1082 + s_1434_b => s_0306 + s_0763_b + s_1087; s_0306, s_0763_b, s_0816, s_1082, s_1087, s_1434_b, Rate Law: intracellular*Vmax_r_0607*(1/kms_s_0816r_0607)^1*(1/kms_s_1082r_0607)^1*(1/kms_s_1434_br_0607)^1*(s_0816^1*s_1082^1*s_1434_b^1-s_0306^1*s_0763_b^1*s_1087^1/Keq_r_0607)/(((1+s_0816/kms_s_0816r_0607)*(1+s_1082/kms_s_1082r_0607)*(1+s_1434_b/kms_s_1434_br_0607)+(1+s_0306/kmp_s_0306r_0607)*(1+s_0763_b/kmp_s_0763_br_0607)*(1+s_1087/kmp_s_1087r_0607))-1)/intracellular |
kms_s_0079r_0008=0.549; kmp_s_0315r_0008=0.549; Vmax_r_0008=0.13761; Keq_r_0008=1.1 | Reaction: s_0079 => s_0315; s_0079, s_0315, Rate Law: intracellular*Vmax_r_0008*(1/kms_s_0079r_0008)^1*(s_0079^1-s_0315^1/Keq_r_0008)/((1+s_0079/kms_s_0079r_0008+1+s_0315/kmp_s_0315r_0008)-1)/intracellular |
kmp_s_1347r_1507=0.549; Vmax_r_1507=0.0190579; kms_s_1348_br_1507=42.2; Keq_r_1507=1.0 | Reaction: s_1348_b => s_1347; s_1347, s_1348_b, Rate Law: Vmax_r_1507*(1/kms_s_1348_br_1507)^1*(s_1348_b^1-s_1347^1/Keq_r_1507)/((1+s_1348_b/kms_s_1348_br_1507+1+s_1347/kmp_s_1347r_1507)-1) |
Keq_r_0165=0.805968; kmp_s_0434r_0165=1.25956; kmp_s_0755r_0165=0.549; Vmax_r_0165=4.0656; kms_s_0706r_0165=0.549; kms_s_0400r_0165=1.71907 | Reaction: s_0400 + s_0706 => s_0434 + s_0755; s_0400, s_0434, s_0706, s_0755, Rate Law: intracellular*Vmax_r_0165*(1/kms_s_0400r_0165)^1*(1/kms_s_0706r_0165)^1*(s_0400^1*s_0706^1-s_0434^1*s_0755^1/Keq_r_0165)/(((1+s_0400/kms_s_0400r_0165)*(1+s_0706/kms_s_0706r_0165)+(1+s_0434/kmp_s_0434r_0165)*(1+s_0755/kmp_s_0755r_0165))-1)/intracellular |
kmp_s_0309r_0889=0.549; kmp_s_1052r_0889=0.549; kms_s_0122r_0889=0.549; kmp_s_0763_br_0889=0.549; Vmax_r_0889=0.734467; kms_s_1048r_0889=0.549; Keq_r_0889=0.6039 | Reaction: s_0122 + s_1048 => s_0309 + s_0763_b + s_1052; s_0122, s_0309, s_0763_b, s_1048, s_1052, Rate Law: intracellular*Vmax_r_0889*(1/kms_s_0122r_0889)^1*(1/kms_s_1048r_0889)^1*(s_0122^1*s_1048^1-s_0309^1*s_0763_b^1*s_1052^1/Keq_r_0889)/(((1+s_0122/kms_s_0122r_0889)*(1+s_1048/kms_s_1048r_0889)+(1+s_0309/kmp_s_0309r_0889)*(1+s_0763_b/kmp_s_0763_br_0889)*(1+s_1052/kmp_s_1052r_0889))-1)/intracellular |
kms_s_0446r_0123=1.09208; kmp_s_0400r_0123=1.71907; kmp_s_0763_br_0123=0.549; kmp_s_1207r_0123=0.549; Keq_r_0123=0.950614; Vmax_r_0123=0.105501; kmp_s_1005r_0123=0.549; kms_s_0458r_0123=0.549; kms_s_0380r_0123=0.549 | Reaction: s_0380 + s_0446 + s_0458 => s_0400 + s_0763_b + s_1005 + s_1207; s_0380, s_0400, s_0446, s_0458, s_0763_b, s_1005, s_1207, Rate Law: intracellular*Vmax_r_0123*(1/kms_s_0380r_0123)^1*(1/kms_s_0446r_0123)^1*(1/kms_s_0458r_0123)^1*(s_0380^1*s_0446^1*s_0458^1-s_0400^1*s_0763_b^1*s_1005^1*s_1207^1/Keq_r_0123)/(((1+s_0380/kms_s_0380r_0123)*(1+s_0446/kms_s_0446r_0123)*(1+s_0458/kms_s_0458r_0123)+(1+s_0400/kmp_s_0400r_0123)*(1+s_0763_b/kmp_s_0763_br_0123)*(1+s_1005/kmp_s_1005r_0123)*(1+s_1207/kmp_s_1207r_0123))-1)/intracellular |
Keq_r_0213=0.6039; kmp_s_0763_br_0213=0.549; kms_s_0410r_0213=0.549; Vmax_r_0213=0.174824; kmp_s_0419r_0213=0.549; kmp_s_1411r_0213=0.549; kmI_s_1415rm_0213=6.0; kms_s_1415r_0213=0.549 | Reaction: s_0410 + s_1415 => s_0419 + s_0763_b + s_1411; s_1415, s_0410, s_1415, s_0419, s_0763_b, s_1411, Rate Law: intracellular*Vmax_r_0213*(1/kms_s_0410r_0213)^1*(1/kms_s_1415r_0213)^1*(s_0410^1*s_1415^1-s_0419^1*s_0763_b^1*s_1411^1/Keq_r_0213)/(((1+s_0410/kms_s_0410r_0213)*(1+s_1415/kms_s_1415r_0213)+(1+s_0419/kmp_s_0419r_0213)*(1+s_0763_b/kmp_s_0763_br_0213)*(1+s_1411/kmp_s_1411r_0213)+1+s_1415/kmI_s_1415rm_0213)-1)/intracellular |
kms_s_1349r_1008=0.549; Keq_r_1008=3.64962; kms_s_0763_br_1008=0.549; kmp_s_1434_br_1008=0.549; kms_s_1096r_1008=0.549; Vmax_r_1008=0.851402; kmp_s_0805r_1008=0.549; kmp_s_1091r_1008=0.549 | Reaction: s_0763_b + s_1096 + s_1349 => s_0805 + s_1091 + s_1434_b; s_0763_b, s_0805, s_1091, s_1096, s_1349, s_1434_b, Rate Law: intracellular*Vmax_r_1008*(1/kms_s_0763_br_1008)^5*(1/kms_s_1096r_1008)^3*(1/kms_s_1349r_1008)^1*(s_0763_b^5*s_1096^3*s_1349^1-s_0805^1*s_1091^3*s_1434_b^3/Keq_r_1008)/(((1+s_0763_b/kms_s_0763_br_1008)*(1+s_1096/kms_s_1096r_1008)*(1+s_1349/kms_s_1349r_1008)+(1+s_0805/kmp_s_0805r_1008)*(1+s_1091/kmp_s_1091r_1008)*(1+s_1434_b/kmp_s_1434_br_1008))-1)/intracellular |
kmp_s_0316r_0884=0.549; Keq_r_0884=0.286516; kmp_s_0763_br_0884=0.549; kmp_s_1207r_0884=0.549; kmp_s_0400r_0884=1.71907; kms_s_0446r_0884=1.09208; Vmax_r_0884=1.26862; kms_s_0158r_0884=0.549 | Reaction: s_0158 + s_0446 => s_0316 + s_0400 + s_0763_b + s_1207; s_0158, s_0316, s_0400, s_0446, s_0763_b, s_1207, Rate Law: intracellular*Vmax_r_0884*(1/kms_s_0158r_0884)^1*(1/kms_s_0446r_0884)^1*(s_0158^1*s_0446^1-s_0316^1*s_0400^1*s_0763_b^2*s_1207^1/Keq_r_0884)/(((1+s_0158/kms_s_0158r_0884)*(1+s_0446/kms_s_0446r_0884)+(1+s_0316/kmp_s_0316r_0884)*(1+s_0400/kmp_s_0400r_0884)*(1+s_0763_b/kmp_s_0763_br_0884)*(1+s_1207/kmp_s_1207r_0884))-1)/intracellular |
kmp_s_1342r_1003=0.549; kms_s_0514r_1003=0.549; kmp_s_1207r_1003=0.549; Keq_r_1003=1.73154; kms_s_0446r_1003=1.09208; Vmax_r_1003=0.13134; kms_s_1338r_1003=0.549; kmp_s_0400r_1003=1.71907 | Reaction: s_0446 + s_0514 + s_1338 => s_0400 + s_1207 + s_1342; s_0400, s_0446, s_0514, s_1207, s_1338, s_1342, Rate Law: intracellular*Vmax_r_1003*(1/kms_s_0446r_1003)^1*(1/kms_s_0514r_1003)^1*(1/kms_s_1338r_1003)^1*(s_0446^1*s_0514^1*s_1338^1-s_0400^1*s_1207^1*s_1342^1/Keq_r_1003)/(((1+s_0446/kms_s_0446r_1003)*(1+s_0514/kms_s_0514r_1003)*(1+s_1338/kms_s_1338r_1003)+(1+s_0400/kmp_s_0400r_1003)*(1+s_1207/kmp_s_1207r_1003)*(1+s_1342/kmp_s_1342r_1003))-1)/intracellular |
kms_s_1156r_0688=0.549; kms_s_0763_br_0688=0.549; kmp_s_0069r_0688=0.549; Vmax_r_0688=4.58593; kmp_s_1082r_0688=1.50326; kms_s_1087r_0688=0.0867353; Keq_r_0688=34.7263 | Reaction: s_0763_b + s_1087 + s_1156 => s_0069 + s_1082; s_0069, s_0763_b, s_1082, s_1087, s_1156, Rate Law: intracellular*Vmax_r_0688*(1/kms_s_0763_br_0688)^1*(1/kms_s_1087r_0688)^1*(1/kms_s_1156r_0688)^1*(s_0763_b^1*s_1087^1*s_1156^1-s_0069^1*s_1082^1/Keq_r_0688)/(((1+s_0763_b/kms_s_0763_br_0688)*(1+s_1087/kms_s_1087r_0688)*(1+s_1156/kms_s_1156r_0688)+(1+s_0069/kmp_s_0069r_0688)*(1+s_1082/kmp_s_1082r_0688))-1)/intracellular |
kmp_s_0798r_0582=0.549; kms_s_0185r_0582=0.549; Keq_r_0582=1.1; kmp_s_0763_br_0582=0.549; Vmax_r_0582=2.1945; kmp_s_0514r_0582=0.549; kms_s_1434_br_0582=0.549; kms_s_0380r_0582=0.549 | Reaction: s_0185 + s_0380 + s_1434_b => s_0514 + s_0763_b + s_0798; s_0185, s_0380, s_0514, s_0763_b, s_0798, s_1434_b, Rate Law: intracellular*Vmax_r_0582*(1/kms_s_0185r_0582)^1*(1/kms_s_0380r_0582)^1*(1/kms_s_1434_br_0582)^1*(s_0185^1*s_0380^1*s_1434_b^1-s_0514^1*s_0763_b^1*s_0798^1/Keq_r_0582)/(((1+s_0185/kms_s_0185r_0582)*(1+s_0380/kms_s_0380r_0582)*(1+s_1434_b/kms_s_1434_br_0582)+(1+s_0514/kmp_s_0514r_0582)*(1+s_0763_b/kmp_s_0763_br_0582)*(1+s_0798/kmp_s_0798r_0582))-1)/intracellular |
kmp_s_0434r_0551=1.25956; kmp_s_0899r_0551=0.549; kmp_s_0752r_0551=0.549; kmp_s_0605r_0551=0.549; kms_s_0907r_0551=0.549; kms_s_1434_br_0551=0.549; Keq_r_0551=0.382386; kms_s_0446r_0551=1.09208; kms_s_0306r_0551=0.549; kmp_s_0763_br_0551=0.549; Vmax_r_0551=1.57168 | Reaction: s_0306 + s_0446 + s_0907 + s_1434_b => s_0434 + s_0605 + s_0752 + s_0763_b + s_0899; s_0306, s_0434, s_0446, s_0605, s_0752, s_0763_b, s_0899, s_0907, s_1434_b, Rate Law: intracellular*Vmax_r_0551*(1/kms_s_0306r_0551)^1*(1/kms_s_0446r_0551)^1*(1/kms_s_0907r_0551)^1*(1/kms_s_1434_br_0551)^1*(s_0306^1*s_0446^1*s_0907^1*s_1434_b^1-s_0434^1*s_0605^1*s_0752^1*s_0763_b^2*s_0899^1/Keq_r_0551)/(((1+s_0306/kms_s_0306r_0551)*(1+s_0446/kms_s_0446r_0551)*(1+s_0907/kms_s_0907r_0551)*(1+s_1434_b/kms_s_1434_br_0551)+(1+s_0434/kmp_s_0434r_0551)*(1+s_0605/kmp_s_0605r_0551)*(1+s_0752/kmp_s_0752r_0551)*(1+s_0763_b/kmp_s_0763_br_0551)*(1+s_0899/kmp_s_0899r_0551))-1)/intracellular |
kms_s_1434_br_0699=0.549; kmp_s_0122r_0699=0.549; Keq_r_0699=1.1; Vmax_r_0699=1.2166; kms_s_0015r_0699=0.549; kmp_s_0763_br_0699=0.549 | Reaction: s_0015 + s_1434_b => s_0122 + s_0763_b; s_0015, s_0122, s_0763_b, s_1434_b, Rate Law: intracellular*Vmax_r_0699*(1/kms_s_0015r_0699)^1*(1/kms_s_1434_br_0699)^1*(s_0015^1*s_1434_b^1-s_0122^1*s_0763_b^1/Keq_r_0699)/(((1+s_0015/kms_s_0015r_0699)*(1+s_1434_b/kms_s_1434_br_0699)+(1+s_0122/kmp_s_0122r_0699)*(1+s_0763_b/kmp_s_0763_br_0699))-1)/intracellular |
kms_s_0317r_0885=0.549; kmp_s_0325r_0885=0.549; kms_s_0122r_0885=0.549; Keq_r_0885=1.1; kmp_s_0309r_0885=0.549; Vmax_r_0885=0.7854 | Reaction: s_0122 + s_0317 => s_0309 + s_0325; s_0122, s_0309, s_0317, s_0325, Rate Law: intracellular*Vmax_r_0885*(1/kms_s_0122r_0885)^1*(1/kms_s_0317r_0885)^1*(s_0122^1*s_0317^1-s_0309^1*s_0325^1/Keq_r_0885)/(((1+s_0122/kms_s_0122r_0885)*(1+s_0317/kms_s_0317r_0885)+(1+s_0309/kmp_s_0309r_0885)*(1+s_0325/kmp_s_0325r_0885))-1)/intracellular |
kmp_s_0867r_0650=0.549; kms_s_0763_br_0650=0.549; kmp_s_0434r_0650=1.25956; Vmax_r_0650=4.53532; kmp_s_0605r_0650=0.549; kms_s_1087r_0650=0.0867353; kmp_s_1082r_0650=1.50326; kms_s_0446r_0650=1.09208; Keq_r_0650=21.9885; kms_s_0861r_0650=0.549 | Reaction: s_0446 + s_0763_b + s_0861 + s_1087 => s_0434 + s_0605 + s_0867 + s_1082; s_0434, s_0446, s_0605, s_0763_b, s_0861, s_0867, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0650*(1/kms_s_0446r_0650)^1*(1/kms_s_0763_br_0650)^1*(1/kms_s_0861r_0650)^1*(1/kms_s_1087r_0650)^1*(s_0446^1*s_0763_b^1*s_0861^1*s_1087^1-s_0434^1*s_0605^1*s_0867^1*s_1082^1/Keq_r_0650)/(((1+s_0446/kms_s_0446r_0650)*(1+s_0763_b/kms_s_0763_br_0650)*(1+s_0861/kms_s_0861r_0650)*(1+s_1087/kms_s_1087r_0650)+(1+s_0434/kmp_s_0434r_0650)*(1+s_0605/kmp_s_0605r_0650)*(1+s_0867/kmp_s_0867r_0650)*(1+s_1082/kmp_s_1082r_0650))-1)/intracellular |
Keq_r_0425=40.2; Vmax_r_0425=0.0118696; kmp_s_0987r_0425=0.549; kmp_s_1091r_0425=0.549; kms_s_1005r_0425=0.549; kms_s_1329r_0425=0.549; kmp_s_0470r_0425=1.0; kmp_s_0514r_0425=0.549; kms_s_0763_br_0425=0.549; kmp_s_1434_br_0425=0.549; kms_s_1096r_0425=0.549 | Reaction: s_0763_b + s_1005 + s_1096 + s_1329 => s_0470 + s_0514 + s_0987 + s_1091 + s_1434_b; s_0470, s_0514, s_0763_b, s_0987, s_1005, s_1091, s_1096, s_1329, s_1434_b, Rate Law: intracellular*Vmax_r_0425*(1/kms_s_0763_br_0425)^9*(1/kms_s_1005r_0425)^3*(1/kms_s_1096r_0425)^6*(1/kms_s_1329r_0425)^1*(s_0763_b^9*s_1005^3*s_1096^6*s_1329^1-s_0470^3*s_0514^3*s_0987^1*s_1091^6*s_1434_b^3/Keq_r_0425)/(((1+s_0763_b/kms_s_0763_br_0425)*(1+s_1005/kms_s_1005r_0425)*(1+s_1096/kms_s_1096r_0425)*(1+s_1329/kms_s_1329r_0425)+(1+s_0470/kmp_s_0470r_0425)*(1+s_0514/kmp_s_0514r_0425)*(1+s_0987/kmp_s_0987r_0425)*(1+s_1091/kmp_s_1091r_0425)*(1+s_1434_b/kmp_s_1434_br_0425))-1)/intracellular |
Vmax_r_0963=0.5544; kms_s_0557r_0963=0.549; kmp_s_0427r_0963=0.549; Keq_r_0963=1.1 | Reaction: s_0557 => s_0427; s_0427, s_0557, Rate Law: intracellular*Vmax_r_0963*(1/kms_s_0557r_0963)^1*(s_0557^1-s_0427^1/Keq_r_0963)/((1+s_0557/kms_s_0557r_0963+1+s_0427/kmp_s_0427r_0963)-1)/intracellular |
Keq_r_0357=0.6039; kms_s_0430r_0357=0.549; kmp_s_0569r_0357=0.549; Vmax_r_0357=0.0163349; kmp_s_1434_br_0357=0.549; kmp_s_0763_br_0357=0.549; kms_s_0624r_0357=0.549 | Reaction: s_0430 + s_0624 => s_0569 + s_0763_b + s_1434_b; s_0430, s_0569, s_0624, s_0763_b, s_1434_b, Rate Law: intracellular*Vmax_r_0357*(1/kms_s_0430r_0357)^1*(1/kms_s_0624r_0357)^1*(s_0430^1*s_0624^1-s_0569^1*s_0763_b^1*s_1434_b^1/Keq_r_0357)/(((1+s_0430/kms_s_0430r_0357)*(1+s_0624/kms_s_0624r_0357)+(1+s_0569/kmp_s_0569r_0357)*(1+s_0763_b/kmp_s_0763_br_0357)*(1+s_1434_b/kmp_s_1434_br_0357))-1)/intracellular |
kmp_s_1140r_0430=0.549; Vmax_r_0430=0.0237906; kmp_s_0470r_0430=1.0; kmp_s_1091r_0430=0.549; kms_s_1005r_0430=0.549; kmp_s_0514r_0430=0.549; Keq_r_0430=40.2; kms_s_0380r_0430=0.549; kmp_s_1434_br_0430=0.549; kms_s_1096r_0430=0.549; kms_s_0763_br_0430=0.549 | Reaction: s_0380 + s_0763_b + s_1005 + s_1096 => s_0470 + s_0514 + s_1091 + s_1140 + s_1434_b; s_0380, s_0470, s_0514, s_0763_b, s_1005, s_1091, s_1096, s_1140, s_1434_b, Rate Law: intracellular*Vmax_r_0430*(1/kms_s_0380r_0430)^1*(1/kms_s_0763_br_0430)^9*(1/kms_s_1005r_0430)^3*(1/kms_s_1096r_0430)^6*(s_0380^1*s_0763_b^9*s_1005^3*s_1096^6-s_0470^3*s_0514^3*s_1091^6*s_1140^1*s_1434_b^3/Keq_r_0430)/(((1+s_0380/kms_s_0380r_0430)*(1+s_0763_b/kms_s_0763_br_0430)*(1+s_1005/kms_s_1005r_0430)*(1+s_1096/kms_s_1096r_0430)+(1+s_0470/kmp_s_0470r_0430)*(1+s_0514/kmp_s_0514r_0430)*(1+s_1091/kmp_s_1091r_0430)*(1+s_1140/kmp_s_1140r_0430)*(1+s_1434_b/kmp_s_1434_br_0430))-1)/intracellular |
Vmax_r_0290=0.00279509; kms_s_1325r_0290=0.549; kmp_s_0763_br_0290=0.549; kmp_s_0514r_0290=0.549; kmp_s_1080r_0290=0.549; Keq_r_0290=0.6039; kms_s_1355r_0290=0.549 | Reaction: s_1325 + s_1355 => s_0514 + s_0763_b + s_1080; s_0514, s_0763_b, s_1080, s_1325, s_1355, Rate Law: intracellular*Vmax_r_0290*(1/kms_s_1325r_0290)^1*(1/kms_s_1355r_0290)^1*(s_1325^1*s_1355^1-s_0514^1*s_0763_b^1*s_1080^1/Keq_r_0290)/(((1+s_1325/kms_s_1325r_0290)*(1+s_1355/kms_s_1355r_0290)+(1+s_0514/kmp_s_0514r_0290)*(1+s_0763_b/kmp_s_0763_br_0290)*(1+s_1080/kmp_s_1080r_0290))-1)/intracellular |
kms_s_0659r_0488=0.549; kmp_s_1338r_0488=0.549; Vmax_r_0488=4.5199; Keq_r_0488=1.1; kmp_s_0657r_0488=0.549; kms_s_0692r_0488=0.549 | Reaction: s_0659 + s_0692 => s_0657 + s_1338; s_0657, s_0659, s_0692, s_1338, Rate Law: intracellular*Vmax_r_0488*(1/kms_s_0659r_0488)^1*(1/kms_s_0692r_0488)^1*(s_0659^1*s_0692^1-s_0657^1*s_1338^1/Keq_r_0488)/(((1+s_0659/kms_s_0659r_0488)*(1+s_0692/kms_s_0692r_0488)+(1+s_0657/kmp_s_0657r_0488)*(1+s_1338/kmp_s_1338r_0488))-1)/intracellular |
Vmax_r_0063=0.764505; kmp_s_0008r_0063=0.549; Keq_r_0063=2.00364; kms_s_0170r_0063=0.549; kms_s_1434_br_0063=0.549 | Reaction: s_0170 + s_1434_b => s_0008; s_0008, s_0170, s_1434_b, Rate Law: intracellular*Vmax_r_0063*(1/kms_s_0170r_0063)^1*(1/kms_s_1434_br_0063)^1*(s_0170^1*s_1434_b^1-s_0008^1/Keq_r_0063)/(((1+s_0170/kms_s_0170r_0063)*(1+s_1434_b/kms_s_1434_br_0063)+1+s_0008/kmp_s_0008r_0063)-1)/intracellular |
kmp_s_0763_br_0859=0.549; kmI_s_0446mr_0859=4.0; intracellular=1.0; kmp_s_0400r_0859=1.71907; Keq_r_0859=12.2086; Vmax_r_0859=84.3466; kms_s_0446r_0859=1.09208; kmp_s_0537r_0859=1.34278; kms_s_0539r_0859=0.104555 | Reaction: s_0446 + s_0539 => s_0400 + s_0537 + s_0763_b; s_0446, s_0446, s_0539, s_0400, s_0537, s_0763_b, Rate Law: intracellular*Vmax_r_0859*(1/kms_s_0446r_0859)^1*(1/kms_s_0539r_0859)^1*(s_0446^1*s_0539^1-s_0400^1*s_0537^1*s_0763_b^1/Keq_r_0859)/((1+s_0446/kms_s_0446r_0859)*(1+s_0539/kms_s_0539r_0859)+(1+s_0400/kmp_s_0400r_0859)*(1+s_0537/kmp_s_0537r_0859)*(1+s_0763_b/kmp_s_0763_br_0859)+((1+s_0446/kmI_s_0446mr_0859)-1))/intracellular |
kms_s_0118r_0661=0.549; Keq_r_0661=63.2537; kmp_s_1082r_0661=1.50326; Vmax_r_0661=3.30332; kmp_s_1379r_0661=0.549; kms_s_0763_br_0661=0.549; kms_s_1087r_0661=0.0867353 | Reaction: s_0118 + s_0763_b + s_1087 => s_1082 + s_1379; s_0118, s_0763_b, s_1082, s_1087, s_1379, Rate Law: intracellular*Vmax_r_0661*(1/kms_s_0118r_0661)^1*(1/kms_s_0763_br_0661)^2*(1/kms_s_1087r_0661)^1*(s_0118^1*s_0763_b^2*s_1087^1-s_1082^1*s_1379^1/Keq_r_0661)/(((1+s_0118/kms_s_0118r_0661)*(1+s_0763_b/kms_s_0763_br_0661)*(1+s_1087/kms_s_1087r_0661)+(1+s_1082/kmp_s_1082r_0661)*(1+s_1379/kmp_s_1379r_0661))-1)/intracellular |
kmp_s_0007r_0640=0.549; kms_s_0763_br_0640=0.549; Keq_r_0640=2.00364; kms_s_1096r_0640=0.549; kms_s_0042r_0640=0.549; Vmax_r_0640=1.15192; kmp_s_1091r_0640=0.549 | Reaction: s_0042 + s_0763_b + s_1096 => s_0007 + s_1091; s_0007, s_0042, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0640*(1/kms_s_0042r_0640)^1*(1/kms_s_0763_br_0640)^1*(1/kms_s_1096r_0640)^1*(s_0042^1*s_0763_b^1*s_1096^1-s_0007^1*s_1091^1/Keq_r_0640)/(((1+s_0042/kms_s_0042r_0640)*(1+s_0763_b/kms_s_0763_br_0640)*(1+s_1096/kms_s_1096r_0640)+(1+s_0007/kmp_s_0007r_0640)*(1+s_1091/kmp_s_1091r_0640))-1)/intracellular |
kmp_s_1517r_0959=0.549; kms_s_0446r_0959=1.09208; Keq_r_0959=0.303587; kms_s_1521r_0959=0.549; kmp_s_1434_br_0959=0.549; kmp_s_0566r_0959=0.549; Vmax_r_0959=0.0120516 | Reaction: s_0446 + s_1521 => s_0566 + s_1434_b + s_1517; s_0446, s_0566, s_1434_b, s_1517, s_1521, Rate Law: intracellular*Vmax_r_0959*(1/kms_s_0446r_0959)^1*(1/kms_s_1521r_0959)^1*(s_0446^1*s_1521^1-s_0566^1*s_1434_b^1*s_1517^1/Keq_r_0959)/(((1+s_0446/kms_s_0446r_0959)*(1+s_1521/kms_s_1521r_0959)+(1+s_0566/kmp_s_0566r_0959)*(1+s_1434_b/kmp_s_1434_br_0959)*(1+s_1517/kmp_s_1517r_0959))-1)/intracellular |
kms_s_0881r_0232=0.549; kmp_s_0763_br_0232=0.549; kmp_s_1073r_0232=0.549; kmp_s_1207r_0232=0.549; Vmax_r_0232=0.826427; kms_s_0469r_0232=0.549; Keq_r_0232=0.6039 | Reaction: s_0469 + s_0881 => s_0763_b + s_1073 + s_1207; s_0469, s_0763_b, s_0881, s_1073, s_1207, Rate Law: intracellular*Vmax_r_0232*(1/kms_s_0469r_0232)^1*(1/kms_s_0881r_0232)^1*(s_0469^1*s_0881^1-s_0763_b^1*s_1073^1*s_1207^1/Keq_r_0232)/(((1+s_0469/kms_s_0469r_0232)*(1+s_0881/kms_s_0881r_0232)+(1+s_0763_b/kmp_s_0763_br_0232)*(1+s_1073/kmp_s_1073r_0232)*(1+s_1207/kmp_s_1207r_0232))-1)/intracellular |
kmp_s_1087r_0940=0.0867353; kms_s_0514r_0940=0.549; Vmax_r_0940=9.4545; kmp_s_0470r_0940=1.0; kmp_s_0380r_0940=0.549; kms_s_1277r_0940=0.0605905; Keq_r_0940=1.04749; kms_s_1082r_0940=1.50326 | Reaction: s_0514 + s_1082 + s_1277 => s_0380 + s_0470 + s_1087; s_0380, s_0470, s_0514, s_1082, s_1087, s_1277, Rate Law: intracellular*Vmax_r_0940*(1/kms_s_0514r_0940)^1*(1/kms_s_1082r_0940)^1*(1/kms_s_1277r_0940)^1*(s_0514^1*s_1082^1*s_1277^1-s_0380^1*s_0470^1*s_1087^1/Keq_r_0940)/(((1+s_0514/kms_s_0514r_0940)*(1+s_1082/kms_s_1082r_0940)*(1+s_1277/kms_s_1277r_0940)+(1+s_0380/kmp_s_0380r_0940)*(1+s_0470/kmp_s_0470r_0940)*(1+s_1087/kmp_s_1087r_0940))-1)/intracellular |
kmp_s_1290r_0874=0.549; Vmax_r_0874=0.0193599; kms_s_1293r_0874=0.549; Keq_r_0874=0.6039; kmp_s_1225r_0874=0.549; kmp_s_0763_br_0874=0.549; kms_s_1226r_0874=0.549 | Reaction: s_1226 + s_1293 => s_0763_b + s_1225 + s_1290; s_0763_b, s_1225, s_1226, s_1290, s_1293, Rate Law: intracellular*Vmax_r_0874*(1/kms_s_1226r_0874)^1*(1/kms_s_1293r_0874)^1*(s_1226^1*s_1293^1-s_0763_b^1*s_1225^1*s_1290^1/Keq_r_0874)/(((1+s_1226/kms_s_1226r_0874)*(1+s_1293/kms_s_1293r_0874)+(1+s_0763_b/kmp_s_0763_br_0874)*(1+s_1225/kmp_s_1225r_0874)*(1+s_1290/kmp_s_1290r_0874))-1)/intracellular |
kmp_s_0766_br_1503=0.1; kmp_s_1339_br_1503=1.0; Keq_r_1503=1.0; kms_s_0763_br_1503=0.549; Vmax_r_1503=0.840147; kms_s_1338r_1503=0.549 | Reaction: s_0763_b + s_1338 => s_0766_b + s_1339_b; s_0763_b, s_0766_b, s_1338, s_1339_b, Rate Law: Vmax_r_1503*(1/kms_s_0763_br_1503)^1*(1/kms_s_1338r_1503)^1*(s_0763_b^1*s_1338^1-s_0766_b^1*s_1339_b^1/Keq_r_1503)/(((1+s_0763_b/kms_s_0763_br_1503)*(1+s_1338/kms_s_1338r_1503)+(1+s_0766_b/kmp_s_0766_br_1503)*(1+s_1339_b/kmp_s_1339_br_1503))-1) |
kmp_s_0763_br_0040=0.549; kms_s_0557r_0040=0.549; kmp_s_0163r_0040=0.549; Vmax_r_0040=0.00989001; kmp_s_0689r_0040=0.549; Keq_r_0040=0.331541 | Reaction: s_0557 => s_0163 + s_0689 + s_0763_b; s_0163, s_0557, s_0689, s_0763_b, Rate Law: intracellular*Vmax_r_0040*(1/kms_s_0557r_0040)^1*(s_0557^1-s_0163^1*s_0689^1*s_0763_b^1/Keq_r_0040)/((1+s_0557/kms_s_0557r_0040+(1+s_0163/kmp_s_0163r_0040)*(1+s_0689/kmp_s_0689r_0040)*(1+s_0763_b/kmp_s_0763_br_0040))-1)/intracellular |
Vmax_r_0951=0.0120515; Keq_r_0951=0.192861; kmp_s_1517r_0951=0.549; kmp_s_0562r_0951=0.549; kmp_s_1434_br_0951=0.549; kms_s_1521r_0951=0.549; kms_s_0400r_0951=1.71907 | Reaction: s_0400 + s_1521 => s_0562 + s_1434_b + s_1517; s_0400, s_0562, s_1434_b, s_1517, s_1521, Rate Law: intracellular*Vmax_r_0951*(1/kms_s_0400r_0951)^1*(1/kms_s_1521r_0951)^1*(s_0400^1*s_1521^1-s_0562^1*s_1434_b^1*s_1517^1/Keq_r_0951)/(((1+s_0400/kms_s_0400r_0951)*(1+s_1521/kms_s_1521r_0951)+(1+s_0562/kmp_s_0562r_0951)*(1+s_1434_b/kmp_s_1434_br_0951)*(1+s_1517/kmp_s_1517r_0951))-1)/intracellular |
kmp_s_1306r_0976=0.549; kms_s_1096r_0976=0.549; kms_s_0217r_0976=0.549; kms_s_0763_br_0976=0.549; Vmax_r_0976=1.60931; Keq_r_0976=2.00364; kmp_s_1091r_0976=0.549 | Reaction: s_0217 + s_0763_b + s_1096 => s_1091 + s_1306; s_0217, s_0763_b, s_1091, s_1096, s_1306, Rate Law: intracellular*Vmax_r_0976*(1/kms_s_0217r_0976)^1*(1/kms_s_0763_br_0976)^1*(1/kms_s_1096r_0976)^1*(s_0217^1*s_0763_b^1*s_1096^1-s_1091^1*s_1306^1/Keq_r_0976)/(((1+s_0217/kms_s_0217r_0976)*(1+s_0763_b/kms_s_0763_br_0976)*(1+s_1096/kms_s_1096r_0976)+(1+s_1091/kmp_s_1091r_0976)*(1+s_1306/kmp_s_1306r_0976))-1)/intracellular |
kms_s_0470r_0883=1.0; kmp_s_0318r_0883=0.549; kms_s_0316r_0883=0.549; Vmax_r_0883=0.46739; Keq_r_0883=0.6039; kmp_s_0763_br_0883=0.549 | Reaction: s_0316 + s_0470 => s_0318 + s_0763_b; s_0316, s_0318, s_0470, s_0763_b, Rate Law: intracellular*Vmax_r_0883*(1/kms_s_0316r_0883)^1*(1/kms_s_0470r_0883)^1*(s_0316^1*s_0470^1-s_0318^1*s_0763_b^1/Keq_r_0883)/(((1+s_0316/kms_s_0316r_0883)*(1+s_0470/kms_s_0470r_0883)+(1+s_0318/kmp_s_0318r_0883)*(1+s_0763_b/kmp_s_0763_br_0883))-1)/intracellular |
kms_s_0218r_0044=0.549; kms_s_1096r_0044=0.549; Vmax_r_0044=0.00279511; kms_s_0763_br_0044=0.549; kmp_s_1325r_0044=0.549; Keq_r_0044=3.64962; kmp_s_1091r_0044=0.549 | Reaction: s_0218 + s_0763_b + s_1096 => s_1091 + s_1325; s_0218, s_0763_b, s_1091, s_1096, s_1325, Rate Law: intracellular*Vmax_r_0044*(1/kms_s_0218r_0044)^1*(1/kms_s_0763_br_0044)^2*(1/kms_s_1096r_0044)^1*(s_0218^1*s_0763_b^2*s_1096^1-s_1091^1*s_1325^1/Keq_r_0044)/(((1+s_0218/kms_s_0218r_0044)*(1+s_0763_b/kms_s_0763_br_0044)*(1+s_1096/kms_s_1096r_0044)+(1+s_1091/kmp_s_1091r_0044)*(1+s_1325/kmp_s_1325r_0044))-1)/intracellular |
Keq_r_0660=0.331541; kmp_s_0118r_0660=0.549; kms_s_1091r_0660=0.549; kmp_s_1096r_0660=0.549; Vmax_r_0660=3.30329; kmp_s_0763_br_0660=0.549; kms_s_1379r_0660=0.549 | Reaction: s_1091 + s_1379 => s_0118 + s_0763_b + s_1096; s_0118, s_0763_b, s_1091, s_1096, s_1379, Rate Law: intracellular*Vmax_r_0660*(1/kms_s_1091r_0660)^1*(1/kms_s_1379r_0660)^1*(s_1091^1*s_1379^1-s_0118^1*s_0763_b^2*s_1096^1/Keq_r_0660)/(((1+s_1091/kms_s_1091r_0660)*(1+s_1379/kms_s_1379r_0660)+(1+s_0118/kmp_s_0118r_0660)*(1+s_0763_b/kmp_s_0763_br_0660)*(1+s_1096/kmp_s_1096r_0660))-1)/intracellular |
kms_s_1096r_0464=0.549; kmp_s_0514r_0464=0.549; kmp_s_1091r_0464=0.549; kms_s_0763_br_0464=0.549; Keq_r_0464=3.64962; kmp_s_1434_br_0464=0.549; kms_s_0582r_0464=0.549; kms_s_1005r_0464=0.549; kmp_s_0470r_0464=1.0; Vmax_r_0464=0.0179399; kmp_s_0977r_0464=0.549 | Reaction: s_0582 + s_0763_b + s_1005 + s_1096 => s_0470 + s_0514 + s_0977 + s_1091 + s_1434_b; s_0470, s_0514, s_0582, s_0763_b, s_0977, s_1005, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0464*(1/kms_s_0582r_0464)^1*(1/kms_s_0763_br_0464)^3*(1/kms_s_1005r_0464)^1*(1/kms_s_1096r_0464)^2*(s_0582^1*s_0763_b^3*s_1005^1*s_1096^2-s_0470^1*s_0514^1*s_0977^1*s_1091^2*s_1434_b^1/Keq_r_0464)/(((1+s_0582/kms_s_0582r_0464)*(1+s_0763_b/kms_s_0763_br_0464)*(1+s_1005/kms_s_1005r_0464)*(1+s_1096/kms_s_1096r_0464)+(1+s_0470/kmp_s_0470r_0464)*(1+s_0514/kmp_s_0514r_0464)*(1+s_0977/kmp_s_0977r_0464)*(1+s_1091/kmp_s_1091r_0464)*(1+s_1434_b/kmp_s_1434_br_0464))-1)/intracellular |
Keq_r_0698=5.77591; Vmax_r_0698=1.5048; kmp_s_0554r_0698=0.549; kms_s_0539r_0698=0.104555 | Reaction: s_0539 => s_0554; s_0539, s_0554, Rate Law: intracellular*Vmax_r_0698*(1/kms_s_0539r_0698)^1*(s_0539^1-s_0554^1/Keq_r_0698)/((1+s_0539/kms_s_0539r_0698+1+s_0554/kmp_s_0554r_0698)-1)/intracellular |
kms_s_0446r_0977=1.09208; kmp_s_0267r_0977=0.549; kms_s_1306r_0977=0.549; Vmax_r_0977=1.60929; kmp_s_0400r_0977=1.71907; Keq_r_0977=0.950614; kmp_s_0763_br_0977=0.549 | Reaction: s_0446 + s_1306 => s_0267 + s_0400 + s_0763_b; s_0267, s_0400, s_0446, s_0763_b, s_1306, Rate Law: intracellular*Vmax_r_0977*(1/kms_s_0446r_0977)^1*(1/kms_s_1306r_0977)^1*(s_0446^1*s_1306^1-s_0267^1*s_0400^1*s_0763_b^1/Keq_r_0977)/(((1+s_0446/kms_s_0446r_0977)*(1+s_1306/kms_s_1306r_0977)+(1+s_0267/kmp_s_0267r_0977)*(1+s_0400/kmp_s_0400r_0977)*(1+s_0763_b/kmp_s_0763_br_0977))-1)/intracellular |
Keq_r_0707=1.1; Vmax_r_0707=1.2166; kms_s_1091r_0707=0.549; kms_s_0307r_0707=0.549; kmp_s_1096r_0707=0.549; kmp_s_0015r_0707=0.549 | Reaction: s_0307 + s_1091 => s_0015 + s_1096; s_0015, s_0307, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0707*(1/kms_s_0307r_0707)^1*(1/kms_s_1091r_0707)^1*(s_0307^1*s_1091^1-s_0015^1*s_1096^1/Keq_r_0707)/(((1+s_0307/kms_s_0307r_0707)*(1+s_1091/kms_s_1091r_0707)+(1+s_0015/kmp_s_0015r_0707)*(1+s_1096/kmp_s_1096r_0707))-1)/intracellular |
a_s_0873r_1812=0.13579; a_s_0949r_1812=0.19653; s_0743_or_1812=0.549; s_0960_or_1812=1.0; s_1283_or_1812=0.549; a_s_0434r_1812=0.051; a_s_0593r_1812=0.002432; s_0936_or_1812=0.549; s_1011_or_1812=0.549; a_s_0564r_1812=0.003587; s_0929_or_1812=0.549; a_s_0943r_1812=0.25371; a_s_0960r_1812=0.25728; a_s_1000r_1812=1.0; a_s_0933r_1812=0.050027; s_0619_or_1812=0.549; a_s_0416r_1812=0.023371; s_0511_or_1812=0.549; s_0920_or_1812=0.549; a_s_0955r_1812=0.096481; s_0889_or_1812=0.549; a_s_0743r_1812=0.51852; a_s_0752r_1812=0.051; a_s_0925r_1812=0.25014; s_0949_or_1812=1.0; s_0863_or_1812=0.549; s_0907_or_1812=0.549; a_s_0907r_1812=0.268; s_0939_or_1812=0.549; a_s_0001r_1812=1.1358; a_s_1011r_1812=0.82099; s_0899_or_1812=0.549; s_0955_or_1812=0.549; s_1347_or_1812=0.549; a_s_0446r_1812=59.276; s_0952_or_1812=1.0; s_0416_or_1812=0.549; a_s_0929r_1812=0.23942; s_0881_or_1812=0.549; a_s_0899r_1812=0.268; s_0873_or_1812=0.549; s_0569_or_1812=0.549; s_0593_or_1812=0.549; V_o=0.0555; a_s_0939r_1812=0.12864; a_s_0881r_1812=0.17152; a_s_0569r_1812=0.002432; s_0877_or_1812=0.549; a_s_0863r_1812=0.35734; a_s_0952r_1812=0.028; a_s_1347r_1812=0.02; s_0933_or_1812=0.549; s_0564_or_1812=0.549; s_0925_or_1812=0.549; a_s_0877r_1812=0.17152; s_1000_or_1812=0.549; a_s_0911r_1812=0.075041; s_0740_or_1812=0.549; s_0752_or_1812=0.549; zero_flux=0.0; a_s_1417r_1812=0.067; s_0001_or_1812=0.549; s_0943_or_1812=0.549; a_s_0920r_1812=0.17152; a_s_0619r_1812=0.003587; a_s_0740r_1812=0.32518; a_s_0511r_1812=0.05; s_1417_or_1812=0.549; a_s_0889r_1812=0.04288; a_s_0936r_1812=0.11435; s_0434_or_1812=1.25956; a_s_1283r_1812=9.0E-4; s_0446_or_1812=1.09208; s_0911_or_1812=0.549 | Reaction: s_0001 + s_0416 + s_0434 + s_0446 + s_0511 + s_0564 + s_0569 + s_0593 + s_0619 + s_0740 + s_0743 + s_0752 + s_0863 + s_0873 + s_0877 + s_0881 + s_0889 + s_0899 + s_0907 + s_0911 + s_0920 + s_0925 + s_0929 + s_0933 + s_0936 + s_0939 + s_0943 + s_0949 + s_0952 + s_0955 + s_0960 + s_1000 + s_1011 + s_1347 + s_1417 + s_1283 => s_0400 + s_0463 + s_1207; s_0547_b, s_0001, s_0416, s_0434, s_0446, s_0511, s_0564, s_0569, s_0593, s_0619, s_0740, s_0743, s_0752, s_0863, s_0873, s_0877, s_0881, s_0889, s_0899, s_0907, s_0911, s_0920, s_0925, s_0929, s_0933, s_0936, s_0939, s_0943, s_0949, s_0952, s_0955, s_0960, s_1000, s_1011, s_1283, s_1347, s_1417, Rate Law: intracellular*piecewise(V_o*(1+a_s_0001r_1812*ln(s_0001/s_0001_or_1812)+a_s_0416r_1812*ln(s_0416/s_0416_or_1812)+a_s_0434r_1812*ln(s_0434/s_0434_or_1812)+a_s_0446r_1812*ln(s_0446/s_0446_or_1812)+a_s_0511r_1812*ln(s_0511/s_0511_or_1812)+a_s_0564r_1812*ln(s_0564/s_0564_or_1812)+a_s_0569r_1812*ln(s_0569/s_0569_or_1812)+a_s_0593r_1812*ln(s_0593/s_0593_or_1812)+a_s_0619r_1812*ln(s_0619/s_0619_or_1812)+a_s_0740r_1812*ln(s_0740/s_0740_or_1812)+a_s_0743r_1812*ln(s_0743/s_0743_or_1812)+a_s_0752r_1812*ln(s_0752/s_0752_or_1812)+a_s_0863r_1812*ln(s_0863/s_0863_or_1812)+a_s_0873r_1812*ln(s_0873/s_0873_or_1812)+a_s_0877r_1812*ln(s_0877/s_0877_or_1812)+a_s_0881r_1812*ln(s_0881/s_0881_or_1812)+a_s_0889r_1812*ln(s_0889/s_0889_or_1812)+a_s_0899r_1812*ln(s_0899/s_0899_or_1812)+a_s_0907r_1812*ln(s_0907/s_0907_or_1812)+a_s_0911r_1812*ln(s_0911/s_0911_or_1812)+a_s_0920r_1812*ln(s_0920/s_0920_or_1812)+a_s_0925r_1812*ln(s_0925/s_0925_or_1812)+a_s_0929r_1812*ln(s_0929/s_0929_or_1812)+a_s_0933r_1812*ln(s_0933/s_0933_or_1812)+a_s_0936r_1812*ln(s_0936/s_0936_or_1812)+a_s_0939r_1812*ln(s_0939/s_0939_or_1812)+a_s_0943r_1812*ln(s_0943/s_0943_or_1812)+a_s_0949r_1812*ln(s_0949/s_0949_or_1812)+a_s_0952r_1812*ln(s_0952/s_0952_or_1812)+a_s_0955r_1812*ln(s_0955/s_0955_or_1812)+a_s_0960r_1812*ln(s_0960/s_0960_or_1812)+a_s_1000r_1812*ln(s_1000/s_1000_or_1812)+a_s_1011r_1812*ln(s_1011/s_1011_or_1812)+a_s_1347r_1812*ln(s_1347/s_1347_or_1812)+a_s_1417r_1812*ln(s_1417/s_1417_or_1812)+a_s_1283r_1812*ln(s_1283/s_1283_or_1812)), (V_o*(1+a_s_0001r_1812*ln(s_0001/s_0001_or_1812)+a_s_0416r_1812*ln(s_0416/s_0416_or_1812)+a_s_0434r_1812*ln(s_0434/s_0434_or_1812)+a_s_0446r_1812*ln(s_0446/s_0446_or_1812)+a_s_0511r_1812*ln(s_0511/s_0511_or_1812)+a_s_0564r_1812*ln(s_0564/s_0564_or_1812)+a_s_0569r_1812*ln(s_0569/s_0569_or_1812)+a_s_0593r_1812*ln(s_0593/s_0593_or_1812)+a_s_0619r_1812*ln(s_0619/s_0619_or_1812)+a_s_0740r_1812*ln(s_0740/s_0740_or_1812)+a_s_0743r_1812*ln(s_0743/s_0743_or_1812)+a_s_0752r_1812*ln(s_0752/s_0752_or_1812)+a_s_0863r_1812*ln(s_0863/s_0863_or_1812)+a_s_0873r_1812*ln(s_0873/s_0873_or_1812)+a_s_0877r_1812*ln(s_0877/s_0877_or_1812)+a_s_0881r_1812*ln(s_0881/s_0881_or_1812)+a_s_0889r_1812*ln(s_0889/s_0889_or_1812)+a_s_0899r_1812*ln(s_0899/s_0899_or_1812)+a_s_0907r_1812*ln(s_0907/s_0907_or_1812)+a_s_0911r_1812*ln(s_0911/s_0911_or_1812)+a_s_0920r_1812*ln(s_0920/s_0920_or_1812)+a_s_0925r_1812*ln(s_0925/s_0925_or_1812)+a_s_0929r_1812*ln(s_0929/s_0929_or_1812)+a_s_0933r_1812*ln(s_0933/s_0933_or_1812)+a_s_0936r_1812*ln(s_0936/s_0936_or_1812)+a_s_0939r_1812*ln(s_0939/s_0939_or_1812)+a_s_0943r_1812*ln(s_0943/s_0943_or_1812)+a_s_0949r_1812*ln(s_0949/s_0949_or_1812)+a_s_0952r_1812*ln(s_0952/s_0952_or_1812)+a_s_0955r_1812*ln(s_0955/s_0955_or_1812)+a_s_0960r_1812*ln(s_0960/s_0960_or_1812)+a_s_1000r_1812*ln(s_1000/s_1000_or_1812)+a_s_1011r_1812*ln(s_1011/s_1011_or_1812)+a_s_1347r_1812*ln(s_1347/s_1347_or_1812)+a_s_1417r_1812*ln(s_1417/s_1417_or_1812)+a_s_1283r_1812*ln(s_1283/s_1283_or_1812))) >= zero_flux, zero_flux)/intracellular |
Vmax_r_0568=0.0076692; kmp_s_0706r_0568=0.549; Keq_r_0568=1.1; kms_s_0566r_0568=0.549; kms_s_0752r_0568=0.549; kmp_s_0562r_0568=0.549 | Reaction: s_0566 + s_0752 => s_0562 + s_0706; s_0562, s_0566, s_0706, s_0752, Rate Law: intracellular*Vmax_r_0568*(1/kms_s_0566r_0568)^1*(1/kms_s_0752r_0568)^1*(s_0566^1*s_0752^1-s_0562^1*s_0706^1/Keq_r_0568)/(((1+s_0566/kms_s_0566r_0568)*(1+s_0752/kms_s_0752r_0568)+(1+s_0562/kmp_s_0562r_0568)*(1+s_0706/kmp_s_0706r_0568))-1)/intracellular |
kmp_s_1187r_0466=0.549; Keq_r_0466=3.64962; Vmax_r_0466=0.0179399; kmp_s_1434_br_0466=0.549; kms_s_1096r_0466=0.549; kmp_s_1091r_0466=0.549; kmp_s_0514r_0466=0.549; kms_s_0763_br_0466=0.549; kms_s_1005r_0466=0.549; kmp_s_0470r_0466=1.0; kms_s_1044r_0466=0.549 | Reaction: s_0763_b + s_1005 + s_1044 + s_1096 => s_0470 + s_0514 + s_1091 + s_1187 + s_1434_b; s_0470, s_0514, s_0763_b, s_1005, s_1044, s_1091, s_1096, s_1187, s_1434_b, Rate Law: intracellular*Vmax_r_0466*(1/kms_s_0763_br_0466)^3*(1/kms_s_1005r_0466)^1*(1/kms_s_1044r_0466)^1*(1/kms_s_1096r_0466)^2*(s_0763_b^3*s_1005^1*s_1044^1*s_1096^2-s_0470^1*s_0514^1*s_1091^2*s_1187^1*s_1434_b^1/Keq_r_0466)/(((1+s_0763_b/kms_s_0763_br_0466)*(1+s_1005/kms_s_1005r_0466)*(1+s_1044/kms_s_1044r_0466)*(1+s_1096/kms_s_1096r_0466)+(1+s_0470/kmp_s_0470r_0466)*(1+s_0514/kmp_s_0514r_0466)*(1+s_1091/kmp_s_1091r_0466)*(1+s_1187/kmp_s_1187r_0466)*(1+s_1434_b/kmp_s_1434_br_0466))-1)/intracellular |
States:
Name | Description |
---|---|
s 0315 | [5-[(5-phospho-1-deoxy-D-ribulos-1-ylimino)methylamino]-1-(5-phospho-beta-D-ribosyl)imidazole-4-carboxamide] |
s 0009 | [SAICAR] |
s 0881 | [L-aspartate(1-)] |
s 0562 | [dADP] |
s 0650 | [ethanol] |
s 0514 | [coenzyme A] |
s 1325 | [sphinganine] |
s 0015 | [(6R)-5,10-methenyltetrahydrofolic acid] |
s 0566 | [dATP] |
s 1355 | [tetracosanoyl-CoA] |
s 0316 | [5-amino-1-(5-phospho-D-ribosyl)imidazole] |
s 0564 | [dAMP] |
s 1070 | [N-acetyl-L-gamma-glutamyl phosphate] |
s 0641 | [ergosteryl ester] |
s 0416 | [alpha,alpha-trehalose] |
s 1342 | [succinyl-CoA] |
s 0419 | [alpha,alpha-trehalose 6-phosphate] |
s 1082 | [NAD(+)] |
s 0366 | [acetaldehyde] |
s 1434 b | [water] |
s 0434 | [AMP] |
s 0008 | [(2R,3S)-3-isopropylmalate(2-)] |
s 0539 | [keto-D-fructose 6-phosphate] |
s 0446 | [ATP(4-)] |
s 0554 | [D-mannose 6-phosphate] |
s 1306 | [shikimate] |
s 0706 | [GDP] |
s 1338 | [succinate(2-)] |
s 0763 b | [proton] |
s 0410 | [aldehydo-D-glucose 6-phosphate] |
s 1347 | [sulfate] |
s 0533 | [D-erythrose 4-phosphate(2-)] |
s 1379 | [trans-4-hydroxy-L-proline] |
s 0557 | [D-ribulose 5-phosphate] |
s 0569 | [dCMP] |
s 1096 | [NADPH] |
s 0309 | [5,6,7,8-tetrahydrofolic acid] |
s 0561 | [D-xylulose 5-phosphate] |
s 1349 | [sulfite] |
s 0537 | [keto-D-fructose 1,6-bisphosphate] |
s 0427 | [alpha-D-ribofuranose 5-phosphate] |
s 0317 | [AICA ribonucleotide] |
s 0007 | [(2R,3R)-2,3-dihydroxy-3-methylpentanoate] |
s 0010 | [(2S)-2-isopropyl-3-oxosuccinate(2-)] |
s 1315 | [sn-glycerol 3-phosphate] |
s 0692 | [fumarate(2-)] |
BIOMD0000000496
— v0.0.1Stanford2013 - Kinetic model of yeast metabolic network (standard)Large-scale model construction based on a logical laye…
Details
The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. link: http://identifiers.org/pubmed/24324546
Parameters:
Name | Description |
---|---|
kms_s_1091r_0722=0.549; kmp_s_1096r_0722=0.549; kms_s_0055r_0722=0.549; Keq_r_0722=0.6039; kmp_s_0261r_0722=0.549; Vmax_r_0722=3.30329; kmp_s_0763_br_0722=0.549 | Reaction: s_0055 + s_1091 => s_0261 + s_0763_b + s_1096; s_0055, s_0261, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0722*(1/kms_s_0055r_0722)^1*(1/kms_s_1091r_0722)^1*(s_0055^1*s_1091^1-s_0261^1*s_0763_b^1*s_1096^1/Keq_r_0722)/(((1+s_0055/kms_s_0055r_0722)*(1+s_1091/kms_s_1091r_0722)+(1+s_0261/kmp_s_0261r_0722)*(1+s_0763_b/kmp_s_0763_br_0722)*(1+s_1096/kmp_s_1096r_0722))-1)/intracellular |
Vmax_r_1435=0.0232306; kmp_s_1160r_1435=0.549; Keq_r_1435=1.0; kms_s_1162_br_1435=24.5 | Reaction: s_1162_b => s_1160; s_1160, s_1162_b, Rate Law: Vmax_r_1435*(1/kms_s_1162_br_1435)^1*(s_1162_b^1-s_1160^1/Keq_r_1435)/((1+s_1162_b/kms_s_1162_br_1435+1+s_1160/kmp_s_1160r_1435)-1) |
Keq_r_0370=0.0999269; Vmax_r_0370=0.0120878; kmp_s_0514r_0370=0.549; kms_s_0386r_0370=0.549; kmp_s_1399r_0370=0.549; kms_s_0596r_0370=0.549; kmp_s_0763_br_0370=0.549 | Reaction: s_0386 + s_0596 => s_0514 + s_0763_b + s_1399; s_0386, s_0514, s_0596, s_0763_b, s_1399, Rate Law: intracellular*Vmax_r_0370*(1/kms_s_0386r_0370)^1*(1/kms_s_0596r_0370)^1*(s_0386^1*s_0596^1-s_0514^1*s_0763_b^4*s_1399^1/Keq_r_0370)/(((1+s_0386/kms_s_0386r_0370)*(1+s_0596/kms_s_0596r_0370)+(1+s_0514/kmp_s_0514r_0370)*(1+s_0763_b/kmp_s_0763_br_0370)*(1+s_1399/kmp_s_1399r_0370))-1)/intracellular |
kmp_s_0731r_1042=0.0436363; kms_s_0088r_1042=0.549; Keq_r_1042=0.0874316; kmp_s_1434_br_1042=0.549; kmp_s_0952r_1042=1.0; Vmax_r_1042=0.187549; kms_s_0943r_1042=0.549 | Reaction: s_0088 + s_0943 => s_0731 + s_0952 + s_1434_b; s_0088, s_0731, s_0943, s_0952, s_1434_b, Rate Law: intracellular*Vmax_r_1042*(1/kms_s_0088r_1042)^1*(1/kms_s_0943r_1042)^1*(s_0088^1*s_0943^1-s_0731^1*s_0952^1*s_1434_b^1/Keq_r_1042)/(((1+s_0088/kms_s_0088r_1042)*(1+s_0943/kms_s_0943r_1042)+(1+s_0731/kmp_s_0731r_1042)*(1+s_0952/kmp_s_0952r_1042)*(1+s_1434_b/kmp_s_1434_br_1042))-1)/intracellular |
Vmax_r_1038=0.1001; kms_s_1434_br_1038=0.549; Keq_r_1038=1.1; kms_s_0419r_1038=0.549; kmp_s_0416r_1038=0.549; kmp_s_1207r_1038=0.549 | Reaction: s_0419 + s_1434_b => s_0416 + s_1207; s_0416, s_0419, s_1207, s_1434_b, Rate Law: intracellular*Vmax_r_1038*(1/kms_s_0419r_1038)^1*(1/kms_s_1434_br_1038)^1*(s_0419^1*s_1434_b^1-s_0416^1*s_1207^1/Keq_r_1038)/(((1+s_0419/kms_s_0419r_1038)*(1+s_1434_b/kms_s_1434_br_1038)+(1+s_0416/kmp_s_0416r_1038)*(1+s_1207/kmp_s_1207r_1038))-1)/intracellular |
kms_s_0438r_0006=0.549; Vmax_r_0006=1.58399; kmp_s_0743r_0006=0.549; kmp_s_1434_br_0006=0.549; Keq_r_0006=0.6039 | Reaction: s_0438 => s_0743 + s_1434_b; s_0438, s_0743, s_1434_b, Rate Law: intracellular*Vmax_r_0006*(1/kms_s_0438r_0006)^1*(s_0438^1-s_0743^1*s_1434_b^1/Keq_r_0006)/((1+s_0438/kms_s_0438r_0006+(1+s_0743/kmp_s_0743r_0006)*(1+s_1434_b/kmp_s_1434_br_0006))-1)/intracellular |
Keq_r_0721=0.6039; kmp_s_0763_br_0721=0.549; kms_s_1091r_0721=0.549; kms_s_0234r_0721=0.549; Vmax_r_0721=3.30329; kmp_s_0254r_0721=0.549; kmp_s_1096r_0721=0.549 | Reaction: s_0234 + s_1091 => s_0254 + s_0763_b + s_1096; s_0234, s_0254, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0721*(1/kms_s_0234r_0721)^1*(1/kms_s_1091r_0721)^1*(s_0234^1*s_1091^1-s_0254^1*s_0763_b^1*s_1096^1/Keq_r_0721)/(((1+s_0234/kms_s_0234r_0721)*(1+s_1091/kms_s_1091r_0721)+(1+s_0254/kmp_s_0254r_0721)*(1+s_0763_b/kmp_s_0763_br_0721)*(1+s_1096/kmp_s_1096r_0721))-1)/intracellular |
Keq_r_0118=1.1; kmp_s_0374r_0118=0.549; kms_s_0380r_0118=0.549; Vmax_r_0118=0.125399; kmp_s_0514r_0118=0.549 | Reaction: s_0380 => s_0374 + s_0514; s_0374, s_0380, s_0514, Rate Law: intracellular*Vmax_r_0118*(1/kms_s_0380r_0118)^2*(s_0380^2-s_0374^1*s_0514^1/Keq_r_0118)/((1+s_0380/kms_s_0380r_0118+(1+s_0374/kmp_s_0374r_0118)*(1+s_0514/kmp_s_0514r_0118))-1)/intracellular |
kms_s_0446r_0499=1.09208; Keq_r_0499=4.77829; kmp_s_0763_br_0499=0.549; kms_s_0545r_0499=0.0987587; kmp_s_0400r_0499=1.71907; kmp_s_0455r_0499=0.496414; Vmax_r_0499=72.4789 | Reaction: s_0446 + s_0545 => s_0400 + s_0455 + s_0763_b; s_0400, s_0446, s_0455, s_0545, s_0763_b, Rate Law: intracellular*Vmax_r_0499*(1/kms_s_0446r_0499)^1*(1/kms_s_0545r_0499)^1*(s_0446^1*s_0545^1-s_0400^1*s_0455^1*s_0763_b^1/Keq_r_0499)/(((1+s_0446/kms_s_0446r_0499)*(1+s_0545/kms_s_0545r_0499)+(1+s_0400/kmp_s_0400r_0499)*(1+s_0455/kmp_s_0455r_0499)*(1+s_0763_b/kmp_s_0763_br_0499))-1)/intracellular |
Vmax_r_0509=38.2031; kmp_s_0899r_0509=0.549; kms_s_0763_br_0509=0.549; kms_s_1096r_0509=0.549; kmp_s_1091r_0509=0.549; kms_s_0185r_0509=0.549; Keq_r_0509=2.00364; kmp_s_1434_br_0509=0.549; kms_s_0430r_0509=0.549 | Reaction: s_0185 + s_0430 + s_0763_b + s_1096 => s_0899 + s_1091 + s_1434_b; s_0185, s_0430, s_0763_b, s_0899, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0509*(1/kms_s_0185r_0509)^1*(1/kms_s_0430r_0509)^1*(1/kms_s_0763_br_0509)^1*(1/kms_s_1096r_0509)^1*(s_0185^1*s_0430^1*s_0763_b^1*s_1096^1-s_0899^1*s_1091^1*s_1434_b^1/Keq_r_0509)/(((1+s_0185/kms_s_0185r_0509)*(1+s_0430/kms_s_0430r_0509)*(1+s_0763_b/kms_s_0763_br_0509)*(1+s_1096/kms_s_1096r_0509)+(1+s_0899/kmp_s_0899r_0509)*(1+s_1091/kmp_s_1091r_0509)*(1+s_1434_b/kmp_s_1434_br_0509))-1)/intracellular |
Vmax_r_0890=1.53571; kmp_s_0400r_0890=1.71907; kmp_s_0763_br_0890=0.549; kmp_s_1048r_0890=0.549; kmp_s_1207r_0890=0.549; Keq_r_0890=0.950614; kms_s_0333r_0890=0.549; kms_s_0446r_0890=1.09208; kms_s_0740r_0890=0.549 | Reaction: s_0333 + s_0446 + s_0740 => s_0400 + s_0763_b + s_1048 + s_1207; s_0333, s_0400, s_0446, s_0740, s_0763_b, s_1048, s_1207, Rate Law: intracellular*Vmax_r_0890*(1/kms_s_0333r_0890)^1*(1/kms_s_0446r_0890)^1*(1/kms_s_0740r_0890)^1*(s_0333^1*s_0446^1*s_0740^1-s_0400^1*s_0763_b^1*s_1048^1*s_1207^1/Keq_r_0890)/(((1+s_0333/kms_s_0333r_0890)*(1+s_0446/kms_s_0446r_0890)*(1+s_0740/kms_s_0740r_0890)+(1+s_0400/kmp_s_0400r_0890)*(1+s_0763_b/kmp_s_0763_br_0890)*(1+s_1048/kmp_s_1048r_0890)*(1+s_1207/kmp_s_1207r_0890))-1)/intracellular |
Keq_r_0720=0.6039; kmp_s_1096r_0720=0.549; Vmax_r_0720=3.30329; kms_s_0052r_0720=0.549; kms_s_1091r_0720=0.549; kmp_s_0763_br_0720=0.549; kmp_s_0257r_0720=0.549 | Reaction: s_0052 + s_1091 => s_0257 + s_0763_b + s_1096; s_0052, s_0257, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0720*(1/kms_s_0052r_0720)^1*(1/kms_s_1091r_0720)^1*(s_0052^1*s_1091^1-s_0257^1*s_0763_b^1*s_1096^1/Keq_r_0720)/(((1+s_0052/kms_s_0052r_0720)*(1+s_1091/kms_s_1091r_0720)+(1+s_0257/kmp_s_0257r_0720)*(1+s_0763_b/kmp_s_0763_br_0720)*(1+s_1096/kmp_s_1096r_0720))-1)/intracellular |
kmp_s_0215r_0262=0.549; kmp_s_0470r_0262=1.0; kms_s_0303r_0262=0.549; kmp_s_1087r_0262=0.0867353; Vmax_r_0262=0.0785834; kms_s_1082r_0262=1.50326; kmp_s_0763_br_0262=0.549; Keq_r_0262=0.0348439 | Reaction: s_0303 + s_1082 => s_0215 + s_0470 + s_0763_b + s_1087; s_0215, s_0303, s_0470, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0262*(1/kms_s_0303r_0262)^1*(1/kms_s_1082r_0262)^1*(s_0303^1*s_1082^1-s_0215^1*s_0470^1*s_0763_b^1*s_1087^1/Keq_r_0262)/(((1+s_0303/kms_s_0303r_0262)*(1+s_1082/kms_s_1082r_0262)+(1+s_0215/kmp_s_0215r_0262)*(1+s_0470/kmp_s_0470r_0262)*(1+s_0763_b/kmp_s_0763_br_0262)*(1+s_1087/kmp_s_1087r_0262))-1)/intracellular |
kms_s_0315r_0604=0.549; kms_s_0907r_0604=0.549; kmp_s_0763_br_0604=0.549; Vmax_r_0604=0.871524; kmp_s_0899r_0604=0.549; kmp_s_0317r_0604=0.549; Keq_r_0604=0.331541; kmp_s_0532r_0604=0.549 | Reaction: s_0315 + s_0907 => s_0317 + s_0532 + s_0763_b + s_0899; s_0315, s_0317, s_0532, s_0763_b, s_0899, s_0907, Rate Law: intracellular*Vmax_r_0604*(1/kms_s_0315r_0604)^1*(1/kms_s_0907r_0604)^1*(s_0315^1*s_0907^1-s_0317^1*s_0532^1*s_0763_b^1*s_0899^1/Keq_r_0604)/(((1+s_0315/kms_s_0315r_0604)*(1+s_0907/kms_s_0907r_0604)+(1+s_0317/kmp_s_0317r_0604)*(1+s_0532/kmp_s_0532r_0604)*(1+s_0763_b/kmp_s_0763_br_0604)*(1+s_0899/kmp_s_0899r_0604))-1)/intracellular |
Keq_r_0064=0.0348439; kms_s_1082r_0064=1.50326; Vmax_r_0064=1.68189; kmp_s_0763_br_0064=0.549; kmp_s_1087r_0064=0.0867353; kms_s_0008r_0064=0.549; kmp_s_0010r_0064=0.549 | Reaction: s_0008 + s_1082 => s_0010 + s_0763_b + s_1087; s_0008, s_0010, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0064*(1/kms_s_0008r_0064)^1*(1/kms_s_1082r_0064)^1*(s_0008^1*s_1082^1-s_0010^1*s_0763_b^1*s_1087^1/Keq_r_0064)/(((1+s_0008/kms_s_0008r_0064)*(1+s_1082/kms_s_1082r_0064)+(1+s_0010/kmp_s_0010r_0064)*(1+s_0763_b/kmp_s_0763_br_0064)*(1+s_1087/kmp_s_1087r_0064))-1)/intracellular |
Vmax_r_0970=3.3649; kmp_s_0942r_0970=0.549; kms_s_0763_br_0970=0.549; kms_s_0899r_0970=0.549; kmp_s_1434_br_0970=0.549; kms_s_0867r_0970=0.549; kms_s_1096r_0970=0.549; Keq_r_0970=2.00364; kmp_s_1091r_0970=0.549 | Reaction: s_0763_b + s_0867 + s_0899 + s_1096 => s_0942 + s_1091 + s_1434_b; s_0763_b, s_0867, s_0899, s_0942, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0970*(1/kms_s_0763_br_0970)^1*(1/kms_s_0867r_0970)^1*(1/kms_s_0899r_0970)^1*(1/kms_s_1096r_0970)^1*(s_0763_b^1*s_0867^1*s_0899^1*s_1096^1-s_0942^1*s_1091^1*s_1434_b^1/Keq_r_0970)/(((1+s_0763_b/kms_s_0763_br_0970)*(1+s_0867/kms_s_0867r_0970)*(1+s_0899/kms_s_0899r_0970)*(1+s_1096/kms_s_1096r_0970)+(1+s_0942/kmp_s_0942r_0970)*(1+s_1091/kmp_s_1091r_0970)*(1+s_1434_b/kmp_s_1434_br_0970))-1)/intracellular |
kms_s_1258r_0913=0.549; kmp_s_0209r_0913=0.549; kmp_s_0470r_0913=1.0; Keq_r_0913=1.1; kms_s_1091r_0913=0.549; kmp_s_1096r_0913=0.549; Vmax_r_0913=0.648558 | Reaction: s_1091 + s_1258 => s_0209 + s_0470 + s_1096; s_0209, s_0470, s_1091, s_1096, s_1258, Rate Law: intracellular*Vmax_r_0913*(1/kms_s_1091r_0913)^1*(1/kms_s_1258r_0913)^1*(s_1091^1*s_1258^1-s_0209^1*s_0470^1*s_1096^1/Keq_r_0913)/(((1+s_1091/kms_s_1091r_0913)*(1+s_1258/kms_s_1258r_0913)+(1+s_0209/kmp_s_0209r_0913)*(1+s_0470/kmp_s_0470r_0913)*(1+s_1096/kmp_s_1096r_0913))-1)/intracellular |
kmp_s_0763_br_0526=0.549; Keq_r_0526=2.21027; kmp_s_1096r_0526=0.549; kms_s_1091r_0526=0.549; Vmax_r_0526=5.48128; kmp_s_0734r_0526=0.549; kms_s_0732r_0526=0.15 | Reaction: s_0732 + s_1091 => s_0734 + s_0763_b + s_1096; s_0732, s_0734, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0526*(1/kms_s_0732r_0526)^1*(1/kms_s_1091r_0526)^1*(s_0732^1*s_1091^1-s_0734^1*s_0763_b^1*s_1096^1/Keq_r_0526)/(((1+s_0732/kms_s_0732r_0526)*(1+s_1091/kms_s_1091r_0526)+(1+s_0734/kmp_s_0734r_0526)*(1+s_0763_b/kmp_s_0763_br_0526)*(1+s_1096/kmp_s_1096r_0526))-1)/intracellular |
kms_s_0455r_0504=0.496414; Keq_r_0504=0.29; kmp_s_0539r_0504=0.104555; Vmax_r_0504=6.56505 | Reaction: s_0455 => s_0539; s_0455, s_0539, Rate Law: intracellular*Vmax_r_0504*(1/kms_s_0455r_0504)^1*(s_0455^1-s_0539^1/Keq_r_0504)/((1+s_0455/kms_s_0455r_0504+1+s_0539/kmp_s_0539r_0504)-1)/intracellular |
Vmax_r_0936=0.863944; kmp_s_1091r_0936=0.549; kms_s_0763_br_0936=0.549; Keq_r_0936=3.64962; kms_s_0120r_0936=0.549; kmp_s_0939r_0936=0.549; kms_s_1096r_0936=0.549 | Reaction: s_0120 + s_0763_b + s_1096 => s_0939 + s_1091; s_0120, s_0763_b, s_0939, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0936*(1/kms_s_0120r_0936)^1*(1/kms_s_0763_br_0936)^2*(1/kms_s_1096r_0936)^1*(s_0120^1*s_0763_b^2*s_1096^1-s_0939^1*s_1091^1/Keq_r_0936)/(((1+s_0120/kms_s_0120r_0936)*(1+s_0763_b/kms_s_0763_br_0936)*(1+s_1096/kms_s_1096r_0936)+(1+s_0939/kmp_s_0939r_0936)*(1+s_1091/kmp_s_1091r_0936))-1)/intracellular |
kms_s_0514r_0437=0.549; kmp_s_0434r_0437=1.25956; Keq_r_0437=1.26869; kmp_s_1355r_0437=0.549; kms_s_0987r_0437=0.549; Vmax_r_0437=0.0038115; kms_s_0446r_0437=1.09208; kmp_s_0605r_0437=0.549 | Reaction: s_0446 + s_0514 + s_0987 => s_0434 + s_0605 + s_1355; s_0434, s_0446, s_0514, s_0605, s_0987, s_1355, Rate Law: intracellular*Vmax_r_0437*(1/kms_s_0446r_0437)^1*(1/kms_s_0514r_0437)^1*(1/kms_s_0987r_0437)^1*(s_0446^1*s_0514^1*s_0987^1-s_0434^1*s_0605^1*s_1355^1/Keq_r_0437)/(((1+s_0446/kms_s_0446r_0437)*(1+s_0514/kms_s_0514r_0437)*(1+s_0987/kms_s_0987r_0437)+(1+s_0434/kmp_s_0434r_0437)*(1+s_0605/kmp_s_0605r_0437)*(1+s_1355/kmp_s_1355r_0437))-1)/intracellular |
kms_s_1455r_0266=0.549; kmp_s_1091r_0266=0.549; Keq_r_0266=1.1; kmp_s_1456r_0266=0.549; kmp_s_1434_br_0266=0.549; Vmax_r_0266=0.0951282; kms_s_1160r_0266=0.549; kms_s_1096r_0266=0.549; kms_s_0763_br_0266=0.549 | Reaction: s_0763_b + s_1096 + s_1160 + s_1455 => s_1091 + s_1434_b + s_1456; s_0763_b, s_1091, s_1096, s_1160, s_1434_b, s_1455, s_1456, Rate Law: intracellular*Vmax_r_0266*(1/kms_s_0763_br_0266)^1*(1/kms_s_1096r_0266)^1*(1/kms_s_1160r_0266)^1*(1/kms_s_1455r_0266)^1*(s_0763_b^1*s_1096^1*s_1160^1*s_1455^1-s_1091^1*s_1434_b^2*s_1456^1/Keq_r_0266)/(((1+s_0763_b/kms_s_0763_br_0266)*(1+s_1096/kms_s_1096r_0266)*(1+s_1160/kms_s_1160r_0266)*(1+s_1455/kms_s_1455r_0266)+(1+s_1091/kmp_s_1091r_0266)*(1+s_1434_b/kmp_s_1434_br_0266)*(1+s_1456/kmp_s_1456r_0266))-1)/intracellular |
kmp_s_1207r_0728=0.549; kmp_s_0149r_0728=0.549; kms_s_1070r_0728=0.549; kms_s_0763_br_0728=0.549; Vmax_r_0728=1.2441; Keq_r_0728=1.1; kms_s_1096r_0728=0.549; kmp_s_1091r_0728=0.549 | Reaction: s_0763_b + s_1070 + s_1096 => s_0149 + s_1091 + s_1207; s_0149, s_0763_b, s_1070, s_1091, s_1096, s_1207, Rate Law: intracellular*Vmax_r_0728*(1/kms_s_0763_br_0728)^1*(1/kms_s_1070r_0728)^1*(1/kms_s_1096r_0728)^1*(s_0763_b^1*s_1070^1*s_1096^1-s_0149^1*s_1091^1*s_1207^1/Keq_r_0728)/(((1+s_0763_b/kms_s_0763_br_0728)*(1+s_1070/kms_s_1070r_0728)*(1+s_1096/kms_s_1096r_0728)+(1+s_0149/kmp_s_0149r_0728)*(1+s_1091/kmp_s_1091r_0728)*(1+s_1207/kmp_s_1207r_0728))-1)/intracellular |
kms_s_1209_br_1461=24.5; Keq_r_1461=1.0; kms_s_0766_br_1461=0.1; kmp_s_1207r_1461=0.549; kmp_s_0763_br_1461=0.549; Vmax_r_1461=0.0925906 | Reaction: s_0766_b + s_1209_b => s_0763_b + s_1207; s_0763_b, s_0766_b, s_1207, s_1209_b, Rate Law: Vmax_r_1461*(1/kms_s_0766_br_1461)^1*(1/kms_s_1209_br_1461)^1*(s_0766_b^1*s_1209_b^1-s_0763_b^1*s_1207^1/Keq_r_1461)/(((1+s_0766_b/kms_s_0766_br_1461)*(1+s_1209_b/kms_s_1209_br_1461)+(1+s_0763_b/kmp_s_0763_br_1461)*(1+s_1207/kmp_s_1207r_1461))-1) |
kms_s_0079r_0008=0.549; kmp_s_0315r_0008=0.549; Vmax_r_0008=0.13761; Keq_r_0008=1.1 | Reaction: s_0079 => s_0315; s_0079, s_0315, Rate Law: intracellular*Vmax_r_0008*(1/kms_s_0079r_0008)^1*(s_0079^1-s_0315^1/Keq_r_0008)/((1+s_0079/kms_s_0079r_0008+1+s_0315/kmp_s_0315r_0008)-1)/intracellular |
kms_s_1132r_0417=0.549; kmp_s_0470r_0417=1.0; kmp_s_0574r_0417=0.549; Vmax_r_0417=0.00599719; kmp_s_0514r_0417=0.549; kmp_s_1091r_0417=0.549; kms_s_1005r_0417=0.549; kmp_s_1434_br_0417=0.549; kms_s_0763_br_0417=0.549; kms_s_1096r_0417=0.549; Keq_r_0417=3.64962 | Reaction: s_0763_b + s_1005 + s_1096 + s_1132 => s_0470 + s_0514 + s_0574 + s_1091 + s_1434_b; s_0470, s_0514, s_0574, s_0763_b, s_1005, s_1091, s_1096, s_1132, s_1434_b, Rate Law: intracellular*Vmax_r_0417*(1/kms_s_0763_br_0417)^3*(1/kms_s_1005r_0417)^1*(1/kms_s_1096r_0417)^2*(1/kms_s_1132r_0417)^1*(s_0763_b^3*s_1005^1*s_1096^2*s_1132^1-s_0470^1*s_0514^1*s_0574^1*s_1091^2*s_1434_b^1/Keq_r_0417)/(((1+s_0763_b/kms_s_0763_br_0417)*(1+s_1005/kms_s_1005r_0417)*(1+s_1096/kms_s_1096r_0417)*(1+s_1132/kms_s_1132r_0417)+(1+s_0470/kmp_s_0470r_0417)*(1+s_0514/kmp_s_0514r_0417)*(1+s_0574/kmp_s_0574r_0417)*(1+s_1091/kmp_s_1091r_0417)*(1+s_1434_b/kmp_s_1434_br_0417))-1)/intracellular |
Vmax_r_0352=3.30329; kmp_s_1096r_0352=0.549; kmp_s_0529r_0352=0.549; Keq_r_0352=0.6039; kmp_s_0763_br_0352=0.549; kms_s_0530r_0352=0.549; kms_s_1091r_0352=0.549 | Reaction: s_0530 + s_1091 => s_0529 + s_0763_b + s_1096; s_0529, s_0530, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0352*(1/kms_s_0530r_0352)^1*(1/kms_s_1091r_0352)^1*(s_0530^1*s_1091^1-s_0529^1*s_0763_b^1*s_1096^1/Keq_r_0352)/(((1+s_0530/kms_s_0530r_0352)*(1+s_1091/kms_s_1091r_0352)+(1+s_0529/kmp_s_0529r_0352)*(1+s_0763_b/kmp_s_0763_br_0352)*(1+s_1096/kmp_s_1096r_0352))-1)/intracellular |
kmp_s_1342r_1003=0.549; kms_s_0514r_1003=0.549; kmp_s_1207r_1003=0.549; Keq_r_1003=1.73154; kms_s_0446r_1003=1.09208; Vmax_r_1003=0.13134; kms_s_1338r_1003=0.549; kmp_s_0400r_1003=1.71907 | Reaction: s_0446 + s_0514 + s_1338 => s_0400 + s_1207 + s_1342; s_0400, s_0446, s_0514, s_1207, s_1338, s_1342, Rate Law: intracellular*Vmax_r_1003*(1/kms_s_0446r_1003)^1*(1/kms_s_0514r_1003)^1*(1/kms_s_1338r_1003)^1*(s_0446^1*s_0514^1*s_1338^1-s_0400^1*s_1207^1*s_1342^1/Keq_r_1003)/(((1+s_0446/kms_s_0446r_1003)*(1+s_0514/kms_s_0514r_1003)*(1+s_1338/kms_s_1338r_1003)+(1+s_0400/kmp_s_0400r_1003)*(1+s_1207/kmp_s_1207r_1003)*(1+s_1342/kmp_s_1342r_1003))-1)/intracellular |
kms_s_1434_br_0562=0.549; Vmax_r_0562=0.0104499; kmp_s_0145r_0562=0.549; kmp_s_0689r_0562=0.549; kmp_s_0763_br_0562=0.549; Keq_r_0562=0.6039; kms_s_0755r_0562=0.549; kmp_s_0605r_0562=0.549 | Reaction: s_0755 + s_1434_b => s_0145 + s_0605 + s_0689 + s_0763_b; s_0145, s_0605, s_0689, s_0755, s_0763_b, s_1434_b, Rate Law: intracellular*Vmax_r_0562*(1/kms_s_0755r_0562)^1*(1/kms_s_1434_br_0562)^3*(s_0755^1*s_1434_b^3-s_0145^1*s_0605^1*s_0689^1*s_0763_b^2/Keq_r_0562)/(((1+s_0755/kms_s_0755r_0562)*(1+s_1434_b/kms_s_1434_br_0562)+(1+s_0145/kmp_s_0145r_0562)*(1+s_0605/kmp_s_0605r_0562)*(1+s_0689/kmp_s_0689r_0562)*(1+s_0763_b/kmp_s_0763_br_0562))-1)/intracellular |
kms_s_1434_br_0014=0.549; Vmax_r_0014=0.00605002; Keq_r_0014=2.00364; kmp_s_0430r_0014=0.549; kmp_s_0319r_0014=0.549; kms_s_0763_br_0014=0.549; kms_s_0146r_0014=0.549 | Reaction: s_0146 + s_0763_b + s_1434_b => s_0319 + s_0430; s_0146, s_0319, s_0430, s_0763_b, s_1434_b, Rate Law: intracellular*Vmax_r_0014*(1/kms_s_0146r_0014)^1*(1/kms_s_0763_br_0014)^1*(1/kms_s_1434_br_0014)^1*(s_0146^1*s_0763_b^1*s_1434_b^1-s_0319^1*s_0430^1/Keq_r_0014)/(((1+s_0146/kms_s_0146r_0014)*(1+s_0763_b/kms_s_0763_br_0014)*(1+s_1434_b/kms_s_1434_br_0014)+(1+s_0319/kmp_s_0319r_0014)*(1+s_0430/kmp_s_0430r_0014))-1)/intracellular |
kms_s_0763_br_0060=0.549; Vmax_r_0060=3.30332; kms_s_1087r_0060=0.0867353; kmp_s_0055r_0060=0.549; kms_s_0261r_0060=0.549; Keq_r_0060=34.7263; kmp_s_1082r_0060=1.50326 | Reaction: s_0261 + s_0763_b + s_1087 => s_0055 + s_1082; s_0055, s_0261, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0060*(1/kms_s_0261r_0060)^1*(1/kms_s_0763_br_0060)^1*(1/kms_s_1087r_0060)^1*(s_0261^1*s_0763_b^1*s_1087^1-s_0055^1*s_1082^1/Keq_r_0060)/(((1+s_0261/kms_s_0261r_0060)*(1+s_0763_b/kms_s_0763_br_0060)*(1+s_1087/kms_s_1087r_0060)+(1+s_0055/kmp_s_0055r_0060)*(1+s_1082/kmp_s_1082r_0060))-1)/intracellular |
kmp_s_0511r_0847=0.549; kms_s_0485r_0847=0.549; kmp_s_0763_br_0847=0.549; kmp_s_0090r_0847=0.549; Vmax_r_0847=0.010285; kms_s_1020r_0847=0.549; Keq_r_0847=0.331541 | Reaction: s_0485 + s_1020 => s_0090 + s_0511 + s_0763_b; s_0090, s_0485, s_0511, s_0763_b, s_1020, Rate Law: intracellular*Vmax_r_0847*(1/kms_s_0485r_0847)^1*(1/kms_s_1020r_0847)^1*(s_0485^1*s_1020^1-s_0090^1*s_0511^1*s_0763_b^2/Keq_r_0847)/(((1+s_0485/kms_s_0485r_0847)*(1+s_1020/kms_s_1020r_0847)+(1+s_0090/kmp_s_0090r_0847)*(1+s_0511/kmp_s_0511r_0847)*(1+s_0763_b/kmp_s_0763_br_0847))-1)/intracellular |
Vmax_r_0290=0.00279509; kms_s_1325r_0290=0.549; kmp_s_0763_br_0290=0.549; kmp_s_0514r_0290=0.549; kmp_s_1080r_0290=0.549; Keq_r_0290=0.6039; kms_s_1355r_0290=0.549 | Reaction: s_1325 + s_1355 => s_0514 + s_0763_b + s_1080; s_0514, s_0763_b, s_1080, s_1325, s_1355, Rate Law: intracellular*Vmax_r_0290*(1/kms_s_1325r_0290)^1*(1/kms_s_1355r_0290)^1*(s_1325^1*s_1355^1-s_0514^1*s_0763_b^1*s_1080^1/Keq_r_0290)/(((1+s_1325/kms_s_1325r_0290)*(1+s_1355/kms_s_1355r_0290)+(1+s_0514/kmp_s_0514r_0290)*(1+s_0763_b/kmp_s_0763_br_0290)*(1+s_1080/kmp_s_1080r_0290))-1)/intracellular |
Keq_r_1293=1.0; kms_s_0547_br_1293=11.1; Vmax_r_1293=2.36101; kmp_s_0545r_1293=0.0987587 | Reaction: s_0547_b => s_0545; s_0545, s_0547_b, Rate Law: Vmax_r_1293*(1/kms_s_0547_br_1293)^1*(s_0547_b^1-s_0545^1/Keq_r_1293)/((1+s_0547_b/kms_s_0547_br_1293+1+s_0545/kmp_s_0545r_1293)-1) |
kmp_s_0763_br_0853=0.549; kms_s_0943r_0853=0.549; Keq_r_0853=0.331541; kmp_s_1219r_0853=0.549; kmp_s_0511r_0853=0.549; kms_s_0485r_0853=0.549; Vmax_r_0853=0.0266199 | Reaction: s_0485 + s_0943 => s_0511 + s_0763_b + s_1219; s_0485, s_0511, s_0763_b, s_0943, s_1219, Rate Law: intracellular*Vmax_r_0853*(1/kms_s_0485r_0853)^1*(1/kms_s_0943r_0853)^1*(s_0485^1*s_0943^1-s_0511^1*s_0763_b^2*s_1219^1/Keq_r_0853)/(((1+s_0485/kms_s_0485r_0853)*(1+s_0943/kms_s_0943r_0853)+(1+s_0511/kmp_s_0511r_0853)*(1+s_0763_b/kmp_s_0763_br_0853)*(1+s_1219/kmp_s_1219r_0853))-1)/intracellular |
kms_s_1096r_0598=0.549; kmp_s_1091r_0598=0.549; kmp_s_0031r_0598=0.549; kmp_s_0514r_0598=0.549; kms_s_0225r_0598=0.549; Keq_r_0598=2.00364; Vmax_r_0598=0.3762; kms_s_0763_br_0598=0.549 | Reaction: s_0225 + s_0763_b + s_1096 => s_0031 + s_0514 + s_1091; s_0031, s_0225, s_0514, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0598*(1/kms_s_0225r_0598)^1*(1/kms_s_0763_br_0598)^2*(1/kms_s_1096r_0598)^2*(s_0225^1*s_0763_b^2*s_1096^2-s_0031^1*s_0514^1*s_1091^2/Keq_r_0598)/(((1+s_0225/kms_s_0225r_0598)*(1+s_0763_b/kms_s_0763_br_0598)*(1+s_1096/kms_s_1096r_0598)+(1+s_0031/kmp_s_0031r_0598)*(1+s_0514/kmp_s_0514r_0598)*(1+s_1091/kmp_s_1091r_0598))-1)/intracellular |
kmp_s_1087r_0940=0.0867353; kms_s_0514r_0940=0.549; Vmax_r_0940=9.4545; kmp_s_0470r_0940=1.0; kmp_s_0380r_0940=0.549; kms_s_1277r_0940=0.0605905; Keq_r_0940=1.04749; kms_s_1082r_0940=1.50326 | Reaction: s_0514 + s_1082 + s_1277 => s_0380 + s_0470 + s_1087; s_0380, s_0470, s_0514, s_1082, s_1087, s_1277, Rate Law: intracellular*Vmax_r_0940*(1/kms_s_0514r_0940)^1*(1/kms_s_1082r_0940)^1*(1/kms_s_1277r_0940)^1*(s_0514^1*s_1082^1*s_1277^1-s_0380^1*s_0470^1*s_1087^1/Keq_r_0940)/(((1+s_0514/kms_s_0514r_0940)*(1+s_1082/kms_s_1082r_0940)*(1+s_1277/kms_s_1277r_0940)+(1+s_0380/kmp_s_0380r_0940)*(1+s_0470/kmp_s_0470r_0940)*(1+s_1087/kmp_s_1087r_0940))-1)/intracellular |
kmp_s_1306r_0976=0.549; kms_s_1096r_0976=0.549; kms_s_0217r_0976=0.549; kms_s_0763_br_0976=0.549; Vmax_r_0976=1.60931; Keq_r_0976=2.00364; kmp_s_1091r_0976=0.549 | Reaction: s_0217 + s_0763_b + s_1096 => s_1091 + s_1306; s_0217, s_0763_b, s_1091, s_1096, s_1306, Rate Law: intracellular*Vmax_r_0976*(1/kms_s_0217r_0976)^1*(1/kms_s_0763_br_0976)^1*(1/kms_s_1096r_0976)^1*(s_0217^1*s_0763_b^1*s_1096^1-s_1091^1*s_1306^1/Keq_r_0976)/(((1+s_0217/kms_s_0217r_0976)*(1+s_0763_b/kms_s_0763_br_0976)*(1+s_1096/kms_s_1096r_0976)+(1+s_1091/kmp_s_1091r_0976)*(1+s_1306/kmp_s_1306r_0976))-1)/intracellular |
kms_s_0380r_0127=0.549; kmp_s_0514r_0127=0.549; kms_s_0434r_0127=1.25956; Vmax_r_0127=25.905; Keq_r_0127=0.953736; kmp_s_0369r_0127=0.549; kmp_s_0446r_0127=1.09208; kms_s_0605r_0127=0.549 | Reaction: s_0380 + s_0434 + s_0605 => s_0369 + s_0446 + s_0514; s_0369, s_0380, s_0434, s_0446, s_0514, s_0605, Rate Law: intracellular*Vmax_r_0127*(1/kms_s_0380r_0127)^1*(1/kms_s_0434r_0127)^1*(1/kms_s_0605r_0127)^1*(s_0380^1*s_0434^1*s_0605^1-s_0369^1*s_0446^1*s_0514^1/Keq_r_0127)/(((1+s_0380/kms_s_0380r_0127)*(1+s_0434/kms_s_0434r_0127)*(1+s_0605/kms_s_0605r_0127)+(1+s_0369/kmp_s_0369r_0127)*(1+s_0446/kmp_s_0446r_0127)*(1+s_0514/kmp_s_0514r_0127))-1)/intracellular |
kms_s_0763_br_0125=0.549; Keq_r_0125=2.00364; kms_s_0369r_0125=0.549; kms_s_0514r_0125=0.549; Vmax_r_0125=26.9831; kmp_s_0380r_0125=0.549; kmp_s_1434_br_0125=0.549 | Reaction: s_0369 + s_0514 + s_0763_b => s_0380 + s_1434_b; s_0369, s_0380, s_0514, s_0763_b, s_1434_b, Rate Law: intracellular*Vmax_r_0125*(1/kms_s_0369r_0125)^1*(1/kms_s_0514r_0125)^1*(1/kms_s_0763_br_0125)^1*(s_0369^1*s_0514^1*s_0763_b^1-s_0380^1*s_1434_b^1/Keq_r_0125)/(((1+s_0369/kms_s_0369r_0125)*(1+s_0514/kms_s_0514r_0125)*(1+s_0763_b/kms_s_0763_br_0125)+(1+s_0380/kmp_s_0380r_0125)*(1+s_1434_b/kmp_s_1434_br_0125))-1)/intracellular |
kmp_s_1207r_0934=0.549; kms_s_0319r_0934=0.549; Vmax_r_0934=0.00385; kmp_s_0320r_0934=0.549; kms_s_1434_br_0934=0.549; Keq_r_0934=1.1 | Reaction: s_0319 + s_1434_b => s_0320 + s_1207; s_0319, s_0320, s_1207, s_1434_b, Rate Law: intracellular*Vmax_r_0934*(1/kms_s_0319r_0934)^1*(1/kms_s_1434_br_0934)^1*(s_0319^1*s_1434_b^1-s_0320^1*s_1207^1/Keq_r_0934)/(((1+s_0319/kms_s_0319r_0934)*(1+s_1434_b/kms_s_1434_br_0934)+(1+s_0320/kmp_s_0320r_0934)*(1+s_1207/kmp_s_1207r_0934))-1)/intracellular |
Vmax_r_0264=0.0454962; kms_s_1458r_0264=0.549; Keq_r_0264=2.00364; kmp_s_1091r_0264=0.549; kms_s_0763_br_0264=0.549; kms_s_1096r_0264=0.549; kmp_s_1447r_0264=0.549 | Reaction: s_0763_b + s_1096 + s_1458 => s_1091 + s_1447; s_0763_b, s_1091, s_1096, s_1447, s_1458, Rate Law: intracellular*Vmax_r_0264*(1/kms_s_0763_br_0264)^1*(1/kms_s_1096r_0264)^1*(1/kms_s_1458r_0264)^1*(s_0763_b^1*s_1096^1*s_1458^1-s_1091^1*s_1447^1/Keq_r_0264)/(((1+s_0763_b/kms_s_0763_br_0264)*(1+s_1096/kms_s_1096r_0264)*(1+s_1458/kms_s_1458r_0264)+(1+s_1091/kmp_s_1091r_0264)*(1+s_1447/kmp_s_1447r_0264))-1)/intracellular |
kms_s_0400r_0362=1.71907; kmp_s_0446r_0362=1.09208; Keq_r_0362=0.698801; Vmax_r_0362=0.010395; kms_s_0591r_0362=0.549; kmp_s_0593r_0362=0.549 | Reaction: s_0400 + s_0591 => s_0446 + s_0593; s_0400, s_0446, s_0591, s_0593, Rate Law: intracellular*Vmax_r_0362*(1/kms_s_0400r_0362)^1*(1/kms_s_0591r_0362)^1*(s_0400^1*s_0591^1-s_0446^1*s_0593^1/Keq_r_0362)/(((1+s_0400/kms_s_0400r_0362)*(1+s_0591/kms_s_0591r_0362)+(1+s_0446/kmp_s_0446r_0362)*(1+s_0593/kmp_s_0593r_0362))-1)/intracellular |
Keq_r_0213=0.6039; kmp_s_0763_br_0213=0.549; kms_s_0410r_0213=0.549; kmp_s_0419r_0213=0.549; kmp_s_1411r_0213=0.549; kms_s_1415r_0213=0.549; Vmax_r_0213=0.157299 | Reaction: s_0410 + s_1415 => s_0419 + s_0763_b + s_1411; s_0410, s_0419, s_0763_b, s_1411, s_1415, Rate Law: intracellular*Vmax_r_0213*(1/kms_s_0410r_0213)^1*(1/kms_s_1415r_0213)^1*(s_0410^1*s_1415^1-s_0419^1*s_0763_b^1*s_1411^1/Keq_r_0213)/(((1+s_0410/kms_s_0410r_0213)*(1+s_1415/kms_s_1415r_0213)+(1+s_0419/kmp_s_0419r_0213)*(1+s_0763_b/kmp_s_0763_br_0213)*(1+s_1411/kmp_s_1411r_0213))-1)/intracellular |
Vmax_r_0328=13.2165; kms_s_1434_br_0328=0.549; Keq_r_0328=1.1; kmp_s_0507r_0328=0.549; kmp_s_0763_br_0328=0.549; kms_s_1156r_0328=0.549; kmp_s_0514r_0328=0.549; kms_s_0380r_0328=0.549 | Reaction: s_0380 + s_1156 + s_1434_b => s_0507 + s_0514 + s_0763_b; s_0380, s_0507, s_0514, s_0763_b, s_1156, s_1434_b, Rate Law: intracellular*Vmax_r_0328*(1/kms_s_0380r_0328)^1*(1/kms_s_1156r_0328)^1*(1/kms_s_1434_br_0328)^1*(s_0380^1*s_1156^1*s_1434_b^1-s_0507^1*s_0514^1*s_0763_b^1/Keq_r_0328)/(((1+s_0380/kms_s_0380r_0328)*(1+s_1156/kms_s_1156r_0328)*(1+s_1434_b/kms_s_1434_br_0328)+(1+s_0507/kmp_s_0507r_0328)*(1+s_0514/kmp_s_0514r_0328)*(1+s_0763_b/kmp_s_0763_br_0328))-1)/intracellular |
Keq_r_0111=2.00364; Vmax_r_0111=3.41221; kms_s_0763_br_0111=0.549; kmp_s_1091r_0111=0.549; kms_s_0150r_0111=0.549; kms_s_1096r_0111=0.549; kmp_s_0018r_0111=0.549 | Reaction: s_0150 + s_0763_b + s_1096 => s_0018 + s_1091; s_0018, s_0150, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0111*(1/kms_s_0150r_0111)^1*(1/kms_s_0763_br_0111)^1*(1/kms_s_1096r_0111)^1*(s_0150^1*s_0763_b^1*s_1096^1-s_0018^1*s_1091^1/Keq_r_0111)/(((1+s_0150/kms_s_0150r_0111)*(1+s_0763_b/kms_s_0763_br_0111)*(1+s_1096/kms_s_1096r_0111)+(1+s_0018/kmp_s_0018r_0111)*(1+s_1091/kmp_s_1091r_0111))-1)/intracellular |
a_s_0873r_1812=0.13579; a_s_0949r_1812=0.19653; s_0743_or_1812=0.549; s_0960_or_1812=1.0; s_1283_or_1812=0.549; a_s_0434r_1812=0.051; a_s_0593r_1812=0.002432; s_0936_or_1812=0.549; s_1011_or_1812=0.549; a_s_0564r_1812=0.003587; s_0929_or_1812=0.549; a_s_0943r_1812=0.25371; a_s_0960r_1812=0.25728; a_s_1000r_1812=1.0; a_s_0933r_1812=0.050027; s_0619_or_1812=0.549; a_s_0416r_1812=0.023371; s_0511_or_1812=0.549; s_0920_or_1812=0.549; a_s_0955r_1812=0.096481; s_0889_or_1812=0.549; a_s_0743r_1812=0.51852; a_s_0752r_1812=0.051; a_s_0925r_1812=0.25014; s_0949_or_1812=1.0; s_0863_or_1812=0.549; s_0907_or_1812=0.549; a_s_0907r_1812=0.268; s_0939_or_1812=0.549; a_s_0001r_1812=1.1358; a_s_1011r_1812=0.82099; s_0899_or_1812=0.549; s_0955_or_1812=0.549; s_1347_or_1812=0.549; a_s_0446r_1812=59.276; s_0952_or_1812=1.0; s_0416_or_1812=0.549; a_s_0929r_1812=0.23942; s_0881_or_1812=0.549; a_s_0899r_1812=0.268; s_0873_or_1812=0.549; s_0569_or_1812=0.549; s_0593_or_1812=0.549; V_o=0.0555; a_s_0939r_1812=0.12864; a_s_0881r_1812=0.17152; a_s_0569r_1812=0.002432; s_0877_or_1812=0.549; a_s_0863r_1812=0.35734; a_s_0952r_1812=0.028; a_s_1347r_1812=0.02; s_0933_or_1812=0.549; s_0564_or_1812=0.549; s_0925_or_1812=0.549; a_s_0877r_1812=0.17152; s_1000_or_1812=0.549; a_s_0911r_1812=0.075041; s_0740_or_1812=0.549; s_0752_or_1812=0.549; zero_flux=0.0; a_s_1417r_1812=0.067; s_0001_or_1812=0.549; s_0943_or_1812=0.549; a_s_0920r_1812=0.17152; a_s_0619r_1812=0.003587; a_s_0740r_1812=0.32518; a_s_0511r_1812=0.05; s_1417_or_1812=0.549; a_s_0889r_1812=0.04288; a_s_0936r_1812=0.11435; s_0434_or_1812=1.25956; a_s_1283r_1812=9.0E-4; s_0446_or_1812=1.09208; s_0911_or_1812=0.549 | Reaction: s_0001 + s_0416 + s_0434 + s_0446 + s_0511 + s_0564 + s_0569 + s_0593 + s_0619 + s_0740 + s_0743 + s_0752 + s_0863 + s_0873 + s_0877 + s_0881 + s_0889 + s_0899 + s_0907 + s_0911 + s_0920 + s_0925 + s_0929 + s_0933 + s_0936 + s_0939 + s_0943 + s_0949 + s_0952 + s_0955 + s_0960 + s_1000 + s_1011 + s_1347 + s_1417 + s_1283 => s_0400 + s_0463 + s_1207; s_0547_b, s_0001, s_0416, s_0434, s_0446, s_0511, s_0564, s_0569, s_0593, s_0619, s_0740, s_0743, s_0752, s_0863, s_0873, s_0877, s_0881, s_0889, s_0899, s_0907, s_0911, s_0920, s_0925, s_0929, s_0933, s_0936, s_0939, s_0943, s_0949, s_0952, s_0955, s_0960, s_1000, s_1011, s_1283, s_1347, s_1417, Rate Law: intracellular*piecewise(V_o*(1+a_s_0001r_1812*ln(s_0001/s_0001_or_1812)+a_s_0416r_1812*ln(s_0416/s_0416_or_1812)+a_s_0434r_1812*ln(s_0434/s_0434_or_1812)+a_s_0446r_1812*ln(s_0446/s_0446_or_1812)+a_s_0511r_1812*ln(s_0511/s_0511_or_1812)+a_s_0564r_1812*ln(s_0564/s_0564_or_1812)+a_s_0569r_1812*ln(s_0569/s_0569_or_1812)+a_s_0593r_1812*ln(s_0593/s_0593_or_1812)+a_s_0619r_1812*ln(s_0619/s_0619_or_1812)+a_s_0740r_1812*ln(s_0740/s_0740_or_1812)+a_s_0743r_1812*ln(s_0743/s_0743_or_1812)+a_s_0752r_1812*ln(s_0752/s_0752_or_1812)+a_s_0863r_1812*ln(s_0863/s_0863_or_1812)+a_s_0873r_1812*ln(s_0873/s_0873_or_1812)+a_s_0877r_1812*ln(s_0877/s_0877_or_1812)+a_s_0881r_1812*ln(s_0881/s_0881_or_1812)+a_s_0889r_1812*ln(s_0889/s_0889_or_1812)+a_s_0899r_1812*ln(s_0899/s_0899_or_1812)+a_s_0907r_1812*ln(s_0907/s_0907_or_1812)+a_s_0911r_1812*ln(s_0911/s_0911_or_1812)+a_s_0920r_1812*ln(s_0920/s_0920_or_1812)+a_s_0925r_1812*ln(s_0925/s_0925_or_1812)+a_s_0929r_1812*ln(s_0929/s_0929_or_1812)+a_s_0933r_1812*ln(s_0933/s_0933_or_1812)+a_s_0936r_1812*ln(s_0936/s_0936_or_1812)+a_s_0939r_1812*ln(s_0939/s_0939_or_1812)+a_s_0943r_1812*ln(s_0943/s_0943_or_1812)+a_s_0949r_1812*ln(s_0949/s_0949_or_1812)+a_s_0952r_1812*ln(s_0952/s_0952_or_1812)+a_s_0955r_1812*ln(s_0955/s_0955_or_1812)+a_s_0960r_1812*ln(s_0960/s_0960_or_1812)+a_s_1000r_1812*ln(s_1000/s_1000_or_1812)+a_s_1011r_1812*ln(s_1011/s_1011_or_1812)+a_s_1347r_1812*ln(s_1347/s_1347_or_1812)+a_s_1417r_1812*ln(s_1417/s_1417_or_1812)+a_s_1283r_1812*ln(s_1283/s_1283_or_1812)), (V_o*(1+a_s_0001r_1812*ln(s_0001/s_0001_or_1812)+a_s_0416r_1812*ln(s_0416/s_0416_or_1812)+a_s_0434r_1812*ln(s_0434/s_0434_or_1812)+a_s_0446r_1812*ln(s_0446/s_0446_or_1812)+a_s_0511r_1812*ln(s_0511/s_0511_or_1812)+a_s_0564r_1812*ln(s_0564/s_0564_or_1812)+a_s_0569r_1812*ln(s_0569/s_0569_or_1812)+a_s_0593r_1812*ln(s_0593/s_0593_or_1812)+a_s_0619r_1812*ln(s_0619/s_0619_or_1812)+a_s_0740r_1812*ln(s_0740/s_0740_or_1812)+a_s_0743r_1812*ln(s_0743/s_0743_or_1812)+a_s_0752r_1812*ln(s_0752/s_0752_or_1812)+a_s_0863r_1812*ln(s_0863/s_0863_or_1812)+a_s_0873r_1812*ln(s_0873/s_0873_or_1812)+a_s_0877r_1812*ln(s_0877/s_0877_or_1812)+a_s_0881r_1812*ln(s_0881/s_0881_or_1812)+a_s_0889r_1812*ln(s_0889/s_0889_or_1812)+a_s_0899r_1812*ln(s_0899/s_0899_or_1812)+a_s_0907r_1812*ln(s_0907/s_0907_or_1812)+a_s_0911r_1812*ln(s_0911/s_0911_or_1812)+a_s_0920r_1812*ln(s_0920/s_0920_or_1812)+a_s_0925r_1812*ln(s_0925/s_0925_or_1812)+a_s_0929r_1812*ln(s_0929/s_0929_or_1812)+a_s_0933r_1812*ln(s_0933/s_0933_or_1812)+a_s_0936r_1812*ln(s_0936/s_0936_or_1812)+a_s_0939r_1812*ln(s_0939/s_0939_or_1812)+a_s_0943r_1812*ln(s_0943/s_0943_or_1812)+a_s_0949r_1812*ln(s_0949/s_0949_or_1812)+a_s_0952r_1812*ln(s_0952/s_0952_or_1812)+a_s_0955r_1812*ln(s_0955/s_0955_or_1812)+a_s_0960r_1812*ln(s_0960/s_0960_or_1812)+a_s_1000r_1812*ln(s_1000/s_1000_or_1812)+a_s_1011r_1812*ln(s_1011/s_1011_or_1812)+a_s_1347r_1812*ln(s_1347/s_1347_or_1812)+a_s_1417r_1812*ln(s_1417/s_1417_or_1812)+a_s_1283r_1812*ln(s_1283/s_1283_or_1812))) >= zero_flux, zero_flux)/intracellular |
kmp_s_1434_br_0418=0.549; Vmax_r_0418=0.00599719; kmp_s_1091r_0418=0.549; kms_s_0763_br_0418=0.549; kmp_s_0514r_0418=0.549; kms_s_1096r_0418=0.549; Keq_r_0418=3.64962; kmp_s_0470r_0418=1.0; kmp_s_0968r_0418=0.549; kms_s_1005r_0418=0.549; kms_s_0574r_0418=0.549 | Reaction: s_0574 + s_0763_b + s_1005 + s_1096 => s_0470 + s_0514 + s_0968 + s_1091 + s_1434_b; s_0470, s_0514, s_0574, s_0763_b, s_0968, s_1005, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0418*(1/kms_s_0574r_0418)^1*(1/kms_s_0763_br_0418)^3*(1/kms_s_1005r_0418)^1*(1/kms_s_1096r_0418)^2*(s_0574^1*s_0763_b^3*s_1005^1*s_1096^2-s_0470^1*s_0514^1*s_0968^1*s_1091^2*s_1434_b^1/Keq_r_0418)/(((1+s_0574/kms_s_0574r_0418)*(1+s_0763_b/kms_s_0763_br_0418)*(1+s_1005/kms_s_1005r_0418)*(1+s_1096/kms_s_1096r_0418)+(1+s_0470/kmp_s_0470r_0418)*(1+s_0514/kmp_s_0514r_0418)*(1+s_0968/kmp_s_0968r_0418)*(1+s_1091/kmp_s_1091r_0418)*(1+s_1434_b/kmp_s_1434_br_0418))-1)/intracellular |
Vmax_r_0015=0.00605002; kmp_s_0146r_0015=0.549; kms_s_1096r_0015=0.549; Keq_r_0015=2.00364; kms_s_0145r_0015=0.549; kmp_s_1091r_0015=0.549; kms_s_0763_br_0015=0.549 | Reaction: s_0145 + s_0763_b + s_1096 => s_0146 + s_1091; s_0145, s_0146, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0015*(1/kms_s_0145r_0015)^1*(1/kms_s_0763_br_0015)^1*(1/kms_s_1096r_0015)^1*(s_0145^1*s_0763_b^1*s_1096^1-s_0146^1*s_1091^1/Keq_r_0015)/(((1+s_0145/kms_s_0145r_0015)*(1+s_0763_b/kms_s_0763_br_0015)*(1+s_1096/kms_s_1096r_0015)+(1+s_0146/kmp_s_0146r_0015)*(1+s_1091/kmp_s_1091r_0015))-1)/intracellular |
Keq_r_0794=2.00364; kms_s_0763_br_0794=0.549; kmp_s_1417r_0794=0.549; Vmax_r_0794=0.52591; kmp_s_0470r_0794=1.0; kms_s_1155r_0794=0.549 | Reaction: s_0763_b + s_1155 => s_0470 + s_1417; s_0470, s_0763_b, s_1155, s_1417, Rate Law: intracellular*Vmax_r_0794*(1/kms_s_0763_br_0794)^1*(1/kms_s_1155r_0794)^1*(s_0763_b^1*s_1155^1-s_0470^1*s_1417^1/Keq_r_0794)/(((1+s_0763_b/kms_s_0763_br_0794)*(1+s_1155/kms_s_1155r_0794)+(1+s_0470/kmp_s_0470r_0794)*(1+s_1417/kmp_s_1417r_0794))-1)/intracellular |
Keq_r_0793=1.1; kmp_s_1155r_0793=0.549; kms_s_0331r_0793=0.549; kmp_s_0605r_0793=0.549; kms_s_1154r_0793=0.549; Vmax_r_0793=0.52591 | Reaction: s_0331 + s_1154 => s_0605 + s_1155; s_0331, s_0605, s_1154, s_1155, Rate Law: intracellular*Vmax_r_0793*(1/kms_s_0331r_0793)^1*(1/kms_s_1154r_0793)^1*(s_0331^1*s_1154^1-s_0605^1*s_1155^1/Keq_r_0793)/(((1+s_0331/kms_s_0331r_0793)*(1+s_1154/kms_s_1154r_0793)+(1+s_0605/kmp_s_0605r_0793)*(1+s_1155/kmp_s_1155r_0793))-1)/intracellular |
kms_s_0798r_0029=0.549; Keq_r_0029=0.6039; kmp_s_0468r_0029=0.549; kmp_s_1434_br_0029=0.549; Vmax_r_0029=0.731496 | Reaction: s_0798 => s_0468 + s_1434_b; s_0468, s_0798, s_1434_b, Rate Law: intracellular*Vmax_r_0029*(1/kms_s_0798r_0029)^1*(s_0798^1-s_0468^1*s_1434_b^1/Keq_r_0029)/((1+s_0798/kms_s_0798r_0029+(1+s_0468/kmp_s_0468r_0029)*(1+s_1434_b/kmp_s_1434_br_0029))-1)/intracellular |
kmp_s_0514r_0442=0.549; kmp_s_1132r_0442=0.549; Vmax_r_0442=0.001914; kms_s_0605r_0442=0.549; kmp_s_0446r_0442=1.09208; kms_s_0434r_0442=1.25956; kms_s_1140r_0442=0.549; Keq_r_0442=0.953736 | Reaction: s_0434 + s_0605 + s_1140 => s_0446 + s_0514 + s_1132; s_0434, s_0446, s_0514, s_0605, s_1132, s_1140, Rate Law: intracellular*Vmax_r_0442*(1/kms_s_0434r_0442)^1*(1/kms_s_0605r_0442)^1*(1/kms_s_1140r_0442)^1*(s_0434^1*s_0605^1*s_1140^1-s_0446^1*s_0514^1*s_1132^1/Keq_r_0442)/(((1+s_0434/kms_s_0434r_0442)*(1+s_0605/kms_s_0605r_0442)*(1+s_1140/kms_s_1140r_0442)+(1+s_0446/kmp_s_0446r_0442)*(1+s_0514/kmp_s_0514r_0442)*(1+s_1132/kmp_s_1132r_0442))-1)/intracellular |
Keq_r_0887=1.1; Vmax_r_0887=0.05115; kms_s_1066r_0887=0.549; kmp_s_0078r_0887=0.549 | Reaction: s_1066 => s_0078; s_0078, s_1066, Rate Law: intracellular*Vmax_r_0887*(1/kms_s_1066r_0887)^1*(s_1066^1-s_0078^1/Keq_r_0887)/((1+s_1066/kms_s_1066r_0887+1+s_0078/kmp_s_0078r_0887)-1)/intracellular |
Vmax_r_1194=2.37902; kmp_s_0472_br_1194=1.0E-5; Keq_r_1194=1.0; kms_s_0470r_1194=1.0 | Reaction: s_0470 => s_0472_b; s_0470, s_0472_b, Rate Law: Vmax_r_1194*(1/kms_s_0470r_1194)^1*(s_0470^1-s_0472_b^1/Keq_r_1194)/((1+s_0470/kms_s_0470r_1194+1+s_0472_b/kmp_s_0472_br_1194)-1) |
kmp_s_0400r_1059=1.71907; Vmax_r_1059=0.23947; kmp_s_1411r_1059=0.549; Keq_r_1059=1.73154; kms_s_0446r_1059=1.09208; kms_s_1417r_1059=0.549 | Reaction: s_0446 + s_1417 => s_0400 + s_1411; s_0400, s_0446, s_1411, s_1417, Rate Law: intracellular*Vmax_r_1059*(1/kms_s_0446r_1059)^1*(1/kms_s_1417r_1059)^1*(s_0446^1*s_1417^1-s_0400^1*s_1411^1/Keq_r_1059)/(((1+s_0446/kms_s_0446r_1059)*(1+s_1417/kms_s_1417r_1059)+(1+s_0400/kmp_s_0400r_1059)*(1+s_1411/kmp_s_1411r_1059))-1)/intracellular |
kmp_s_0564r_0360=0.549; kmp_s_0446r_0360=1.09208; Keq_r_0360=0.698801; Vmax_r_0360=0.015323; kms_s_0400r_0360=1.71907; kms_s_0562r_0360=0.549 | Reaction: s_0400 + s_0562 => s_0446 + s_0564; s_0400, s_0446, s_0562, s_0564, Rate Law: intracellular*Vmax_r_0360*(1/kms_s_0400r_0360)^1*(1/kms_s_0562r_0360)^1*(s_0400^1*s_0562^1-s_0446^1*s_0564^1/Keq_r_0360)/(((1+s_0400/kms_s_0400r_0360)*(1+s_0562/kms_s_0562r_0360)+(1+s_0446/kmp_s_0446r_0360)*(1+s_0564/kmp_s_0564r_0360))-1)/intracellular |
kms_s_0763_br_0112=0.549; kmp_s_0470r_0112=1.0; kmp_s_0150r_0112=0.549; Keq_r_0112=299.629; Vmax_r_0112=2.1714; kms_s_1277r_0112=0.0605905 | Reaction: s_0763_b + s_1277 => s_0150 + s_0470; s_0150, s_0470, s_0763_b, s_1277, Rate Law: intracellular*Vmax_r_0112*(1/kms_s_0763_br_0112)^1*(1/kms_s_1277r_0112)^2*(s_0763_b^1*s_1277^2-s_0150^1*s_0470^1/Keq_r_0112)/(((1+s_0763_b/kms_s_0763_br_0112)*(1+s_1277/kms_s_1277r_0112)+(1+s_0150/kmp_s_0150r_0112)*(1+s_0470/kmp_s_0470r_0112))-1)/intracellular |
Keq_r_0271=2.00364; kms_s_1096r_0271=0.549; kmp_s_1091r_0271=0.549; kms_s_0632r_0271=0.549; Vmax_r_0271=0.0430762; kms_s_0763_br_0271=0.549; kmp_s_0635r_0271=0.549 | Reaction: s_0632 + s_0763_b + s_1096 => s_0635 + s_1091; s_0632, s_0635, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0271*(1/kms_s_0632r_0271)^1*(1/kms_s_0763_br_0271)^1*(1/kms_s_1096r_0271)^1*(s_0632^1*s_0763_b^1*s_1096^1-s_0635^1*s_1091^1/Keq_r_0271)/(((1+s_0632/kms_s_0632r_0271)*(1+s_0763_b/kms_s_0763_br_0271)*(1+s_1096/kms_s_1096r_0271)+(1+s_0635/kmp_s_0635r_0271)*(1+s_1091/kmp_s_1091r_0271))-1)/intracellular |
Keq_r_0633=0.6039; Vmax_r_0633=1.22649; kmp_s_1338r_0633=0.549; kmp_s_0749r_0633=0.549; kms_s_0847r_0633=0.549 | Reaction: s_0847 => s_0749 + s_1338; s_0749, s_0847, s_1338, Rate Law: intracellular*Vmax_r_0633*(1/kms_s_0847r_0633)^1*(s_0847^1-s_0749^1*s_1338^1/Keq_r_0633)/((1+s_0847/kms_s_0847r_0633+(1+s_0749/kmp_s_0749r_0633)*(1+s_1338/kmp_s_1338r_0633))-1)/intracellular |
kmp_s_0514r_0009=0.549; Keq_r_0009=0.0999269; kms_s_0083r_0009=0.549; kmp_s_0763_br_0009=0.549; Vmax_r_0009=0.0421078; kmp_s_1215r_0009=0.549; kms_s_0386r_0009=0.549 | Reaction: s_0083 + s_0386 => s_0514 + s_0763_b + s_1215; s_0083, s_0386, s_0514, s_0763_b, s_1215, Rate Law: intracellular*Vmax_r_0009*(1/kms_s_0083r_0009)^1*(1/kms_s_0386r_0009)^1*(s_0083^1*s_0386^1-s_0514^1*s_0763_b^4*s_1215^1/Keq_r_0009)/(((1+s_0083/kms_s_0083r_0009)*(1+s_0386/kms_s_0386r_0009)+(1+s_0514/kmp_s_0514r_0009)*(1+s_0763_b/kmp_s_0763_br_0009)*(1+s_1215/kmp_s_1215r_0009))-1)/intracellular |
kmp_s_0514r_0534=0.549; kms_s_0386r_0534=0.549; kms_s_1315r_0534=12.8511; Vmax_r_0534=0.0421077; kmp_s_0763_br_0534=0.549; Keq_r_0534=0.0141635; kmp_s_0083r_0534=0.549 | Reaction: s_0386 + s_1315 => s_0083 + s_0514 + s_0763_b; s_0083, s_0386, s_0514, s_0763_b, s_1315, Rate Law: intracellular*Vmax_r_0534*(1/kms_s_0386r_0534)^1*(1/kms_s_1315r_0534)^1*(s_0386^1*s_1315^1-s_0083^1*s_0514^1*s_0763_b^2/Keq_r_0534)/(((1+s_0386/kms_s_0386r_0534)*(1+s_1315/kms_s_1315r_0534)+(1+s_0083/kmp_s_0083r_0534)*(1+s_0514/kmp_s_0514r_0534)*(1+s_0763_b/kmp_s_0763_br_0534))-1)/intracellular |
Keq_r_0165=0.805968; kmp_s_0434r_0165=1.25956; kmp_s_0755r_0165=0.549; Vmax_r_0165=4.0656; kms_s_0706r_0165=0.549; kms_s_0400r_0165=1.71907 | Reaction: s_0400 + s_0706 => s_0434 + s_0755; s_0400, s_0434, s_0706, s_0755, Rate Law: intracellular*Vmax_r_0165*(1/kms_s_0400r_0165)^1*(1/kms_s_0706r_0165)^1*(s_0400^1*s_0706^1-s_0434^1*s_0755^1/Keq_r_0165)/(((1+s_0400/kms_s_0400r_0165)*(1+s_0706/kms_s_0706r_0165)+(1+s_0434/kmp_s_0434r_0165)*(1+s_0755/kmp_s_0755r_0165))-1)/intracellular |
kms_s_1160r_0265=0.549; kms_s_1096r_0265=0.549; kmp_s_1434_br_0265=0.549; kms_s_0763_br_0265=0.549; kms_s_0302r_0265=0.549; Vmax_r_0265=0.0951282; kmp_s_1091r_0265=0.549; kmp_s_1455r_0265=0.549; Keq_r_0265=2.00364 | Reaction: s_0302 + s_0763_b + s_1096 + s_1160 => s_1091 + s_1434_b + s_1455; s_0302, s_0763_b, s_1091, s_1096, s_1160, s_1434_b, s_1455, Rate Law: intracellular*Vmax_r_0265*(1/kms_s_0302r_0265)^1*(1/kms_s_0763_br_0265)^1*(1/kms_s_1096r_0265)^1*(1/kms_s_1160r_0265)^1*(s_0302^1*s_0763_b^1*s_1096^1*s_1160^1-s_1091^1*s_1434_b^1*s_1455^1/Keq_r_0265)/(((1+s_0302/kms_s_0302r_0265)*(1+s_0763_b/kms_s_0763_br_0265)*(1+s_1096/kms_s_1096r_0265)*(1+s_1160/kms_s_1160r_0265)+(1+s_1091/kmp_s_1091r_0265)*(1+s_1434_b/kmp_s_1434_br_0265)*(1+s_1455/kmp_s_1455r_0265))-1)/intracellular |
kms_s_0446r_0123=1.09208; kmp_s_0400r_0123=1.71907; kmp_s_0763_br_0123=0.549; kmp_s_1207r_0123=0.549; Keq_r_0123=0.950614; Vmax_r_0123=0.105501; kmp_s_1005r_0123=0.549; kms_s_0458r_0123=0.549; kms_s_0380r_0123=0.549 | Reaction: s_0380 + s_0446 + s_0458 => s_0400 + s_0763_b + s_1005 + s_1207; s_0380, s_0400, s_0446, s_0458, s_0763_b, s_1005, s_1207, Rate Law: intracellular*Vmax_r_0123*(1/kms_s_0380r_0123)^1*(1/kms_s_0446r_0123)^1*(1/kms_s_0458r_0123)^1*(s_0380^1*s_0446^1*s_0458^1-s_0400^1*s_0763_b^1*s_1005^1*s_1207^1/Keq_r_0123)/(((1+s_0380/kms_s_0380r_0123)*(1+s_0446/kms_s_0446r_0123)*(1+s_0458/kms_s_0458r_0123)+(1+s_0400/kmp_s_0400r_0123)*(1+s_0763_b/kmp_s_0763_br_0123)*(1+s_1005/kmp_s_1005r_0123)*(1+s_1207/kmp_s_1207r_0123))-1)/intracellular |
kms_s_1091r_0719=0.549; kmp_s_1096r_0719=0.549; kms_s_0046r_0719=0.549; kmp_s_0247r_0719=0.549; kmp_s_0763_br_0719=0.549; Keq_r_0719=0.6039; Vmax_r_0719=3.30329 | Reaction: s_0046 + s_1091 => s_0247 + s_0763_b + s_1096; s_0046, s_0247, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0719*(1/kms_s_0046r_0719)^1*(1/kms_s_1091r_0719)^1*(s_0046^1*s_1091^1-s_0247^1*s_0763_b^1*s_1096^1/Keq_r_0719)/(((1+s_0046/kms_s_0046r_0719)*(1+s_1091/kms_s_1091r_0719)+(1+s_0247/kmp_s_0247r_0719)*(1+s_0763_b/kmp_s_0763_br_0719)*(1+s_1096/kmp_s_1096r_0719))-1)/intracellular |
Keq_r_0567=1.73154; Vmax_r_0567=0.008393; kmp_s_0400r_0567=1.71907; kmp_s_0706r_0567=0.549; kms_s_0752r_0567=0.549; kms_s_0446r_0567=1.09208 | Reaction: s_0446 + s_0752 => s_0400 + s_0706; s_0400, s_0446, s_0706, s_0752, Rate Law: intracellular*Vmax_r_0567*(1/kms_s_0446r_0567)^1*(1/kms_s_0752r_0567)^1*(s_0446^1*s_0752^1-s_0400^1*s_0706^1/Keq_r_0567)/(((1+s_0446/kms_s_0446r_0567)*(1+s_0752/kms_s_0752r_0567)+(1+s_0400/kmp_s_0400r_0567)*(1+s_0706/kmp_s_0706r_0567))-1)/intracellular |
Vmax_r_0937=62.2377; Keq_r_0937=8.61335; kms_s_0446r_0937=1.09208; kmp_s_0763_br_0937=0.549; kmp_s_1207r_0937=0.549; kms_s_1277r_0937=0.0605905; kmp_s_0400r_0937=1.71907; kms_s_0458r_0937=0.549; kmp_s_1156r_0937=0.549 | Reaction: s_0446 + s_0458 + s_1277 => s_0400 + s_0763_b + s_1156 + s_1207; s_0400, s_0446, s_0458, s_0763_b, s_1156, s_1207, s_1277, Rate Law: intracellular*Vmax_r_0937*(1/kms_s_0446r_0937)^1*(1/kms_s_0458r_0937)^1*(1/kms_s_1277r_0937)^1*(s_0446^1*s_0458^1*s_1277^1-s_0400^1*s_0763_b^1*s_1156^1*s_1207^1/Keq_r_0937)/(((1+s_0446/kms_s_0446r_0937)*(1+s_0458/kms_s_0458r_0937)*(1+s_1277/kms_s_1277r_0937)+(1+s_0400/kmp_s_0400r_0937)*(1+s_0763_b/kmp_s_0763_br_0937)*(1+s_1156/kmp_s_1156r_0937)*(1+s_1207/kmp_s_1207r_0937))-1)/intracellular |
kms_s_0763_br_1007=0.549; kmp_s_0304r_1007=0.549; kmp_s_1207r_1007=0.549; kms_s_1347r_1007=0.549; Vmax_r_1007=0.624362; kms_s_0400r_1007=1.71907; Keq_r_1007=0.639881 | Reaction: s_0400 + s_0763_b + s_1347 => s_0304 + s_1207; s_0304, s_0400, s_0763_b, s_1207, s_1347, Rate Law: intracellular*Vmax_r_1007*(1/kms_s_0400r_1007)^1*(1/kms_s_0763_br_1007)^1*(1/kms_s_1347r_1007)^1*(s_0400^1*s_0763_b^1*s_1347^1-s_0304^1*s_1207^1/Keq_r_1007)/(((1+s_0400/kms_s_0400r_1007)*(1+s_0763_b/kms_s_0763_br_1007)*(1+s_1347/kms_s_1347r_1007)+(1+s_0304/kmp_s_0304r_1007)*(1+s_1207/kmp_s_1207r_1007))-1)/intracellular |
kms_s_0215r_0263=0.549; kms_s_0763_br_0263=0.549; kms_s_1096r_0263=0.549; Vmax_r_0263=0.0454962; kmp_s_0302r_0263=0.549; Keq_r_0263=2.00364; kmp_s_1091r_0263=0.549 | Reaction: s_0215 + s_0763_b + s_1096 => s_0302 + s_1091; s_0215, s_0302, s_0763_b, s_1091, s_1096, Rate Law: intracellular*Vmax_r_0263*(1/kms_s_0215r_0263)^1*(1/kms_s_0763_br_0263)^1*(1/kms_s_1096r_0263)^1*(s_0215^1*s_0763_b^1*s_1096^1-s_0302^1*s_1091^1/Keq_r_0263)/(((1+s_0215/kms_s_0215r_0263)*(1+s_0763_b/kms_s_0763_br_0263)*(1+s_1096/kms_s_1096r_0263)+(1+s_0302/kmp_s_0302r_0263)*(1+s_1091/kmp_s_1091r_0263))-1)/intracellular |
kmp_s_0434r_0551=1.25956; kmp_s_0899r_0551=0.549; kmp_s_0752r_0551=0.549; kmp_s_0605r_0551=0.549; kms_s_0907r_0551=0.549; kms_s_1434_br_0551=0.549; Keq_r_0551=0.382386; kms_s_0446r_0551=1.09208; kms_s_0306r_0551=0.549; kmp_s_0763_br_0551=0.549; Vmax_r_0551=1.57168 | Reaction: s_0306 + s_0446 + s_0907 + s_1434_b => s_0434 + s_0605 + s_0752 + s_0763_b + s_0899; s_0306, s_0434, s_0446, s_0605, s_0752, s_0763_b, s_0899, s_0907, s_1434_b, Rate Law: intracellular*Vmax_r_0551*(1/kms_s_0306r_0551)^1*(1/kms_s_0446r_0551)^1*(1/kms_s_0907r_0551)^1*(1/kms_s_1434_br_0551)^1*(s_0306^1*s_0446^1*s_0907^1*s_1434_b^1-s_0434^1*s_0605^1*s_0752^1*s_0763_b^2*s_0899^1/Keq_r_0551)/(((1+s_0306/kms_s_0306r_0551)*(1+s_0446/kms_s_0446r_0551)*(1+s_0907/kms_s_0907r_0551)*(1+s_1434_b/kms_s_1434_br_0551)+(1+s_0434/kmp_s_0434r_0551)*(1+s_0605/kmp_s_0605r_0551)*(1+s_0752/kmp_s_0752r_0551)*(1+s_0763_b/kmp_s_0763_br_0551)*(1+s_0899/kmp_s_0899r_0551))-1)/intracellular |
kmp_s_0867r_0650=0.549; kms_s_0763_br_0650=0.549; kmp_s_0434r_0650=1.25956; Vmax_r_0650=4.53532; kmp_s_0605r_0650=0.549; kms_s_1087r_0650=0.0867353; kmp_s_1082r_0650=1.50326; kms_s_0446r_0650=1.09208; Keq_r_0650=21.9885; kms_s_0861r_0650=0.549 | Reaction: s_0446 + s_0763_b + s_0861 + s_1087 => s_0434 + s_0605 + s_0867 + s_1082; s_0434, s_0446, s_0605, s_0763_b, s_0861, s_0867, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0650*(1/kms_s_0446r_0650)^1*(1/kms_s_0763_br_0650)^1*(1/kms_s_0861r_0650)^1*(1/kms_s_1087r_0650)^1*(s_0446^1*s_0763_b^1*s_0861^1*s_1087^1-s_0434^1*s_0605^1*s_0867^1*s_1082^1/Keq_r_0650)/(((1+s_0446/kms_s_0446r_0650)*(1+s_0763_b/kms_s_0763_br_0650)*(1+s_0861/kms_s_0861r_0650)*(1+s_1087/kms_s_1087r_0650)+(1+s_0434/kmp_s_0434r_0650)*(1+s_0605/kmp_s_0605r_0650)*(1+s_0867/kmp_s_0867r_0650)*(1+s_1082/kmp_s_1082r_0650))-1)/intracellular |
Vmax_r_1066=0.025718; kmp_s_0446r_1066=1.09208; Keq_r_1066=0.698801; kms_s_0622r_1066=0.549; kms_s_0400r_1066=1.71907; kmp_s_0624r_1066=0.549 | Reaction: s_0400 + s_0622 => s_0446 + s_0624; s_0400, s_0446, s_0622, s_0624, Rate Law: intracellular*Vmax_r_1066*(1/kms_s_0400r_1066)^1*(1/kms_s_0622r_1066)^1*(s_0400^1*s_0622^1-s_0446^1*s_0624^1/Keq_r_1066)/(((1+s_0400/kms_s_0400r_1066)*(1+s_0622/kms_s_0622r_1066)+(1+s_0446/kmp_s_0446r_1066)*(1+s_0624/kmp_s_0624r_1066))-1)/intracellular |
kms_s_0763_br_0765=0.549; Vmax_r_0765=1.0241; Keq_r_0765=2.00364; kms_s_0180r_0765=0.549; kmp_s_0181r_0765=0.549; kmp_s_0470r_0765=1.0 | Reaction: s_0180 + s_0763_b => s_0181 + s_0470; s_0180, s_0181, s_0470, s_0763_b, Rate Law: intracellular*Vmax_r_0765*(1/kms_s_0180r_0765)^1*(1/kms_s_0763_br_0765)^1*(s_0180^1*s_0763_b^1-s_0181^1*s_0470^1/Keq_r_0765)/(((1+s_0180/kms_s_0180r_0765)*(1+s_0763_b/kms_s_0763_br_0765)+(1+s_0181/kmp_s_0181r_0765)*(1+s_0470/kmp_s_0470r_0765))-1)/intracellular |
Vmax_r_0419=0.00599719; kms_s_0968r_0419=0.549; kms_s_1096r_0419=0.549; kmp_s_1028r_0419=0.549; kmp_s_1091r_0419=0.549; kmp_s_0514r_0419=0.549; Keq_r_0419=3.64962; kmp_s_0470r_0419=1.0; kms_s_1005r_0419=0.549; kmp_s_1434_br_0419=0.549; kms_s_0763_br_0419=0.549 | Reaction: s_0763_b + s_0968 + s_1005 + s_1096 => s_0470 + s_0514 + s_1028 + s_1091 + s_1434_b; s_0470, s_0514, s_0763_b, s_0968, s_1005, s_1028, s_1091, s_1096, s_1434_b, Rate Law: intracellular*Vmax_r_0419*(1/kms_s_0763_br_0419)^3*(1/kms_s_0968r_0419)^1*(1/kms_s_1005r_0419)^1*(1/kms_s_1096r_0419)^2*(s_0763_b^3*s_0968^1*s_1005^1*s_1096^2-s_0470^1*s_0514^1*s_1028^1*s_1091^2*s_1434_b^1/Keq_r_0419)/(((1+s_0763_b/kms_s_0763_br_0419)*(1+s_0968/kms_s_0968r_0419)*(1+s_1005/kms_s_1005r_0419)*(1+s_1096/kms_s_1096r_0419)+(1+s_0470/kmp_s_0470r_0419)*(1+s_0514/kmp_s_0514r_0419)*(1+s_1028/kmp_s_1028r_0419)*(1+s_1091/kmp_s_1091r_0419)*(1+s_1434_b/kmp_s_1434_br_0419))-1)/intracellular |
kms_s_0431_br_1157=38.0; Keq_r_1157=1.0; Vmax_r_1157=0.964941; kmp_s_0430r_1157=0.549 | Reaction: s_0431_b => s_0430; s_0430, s_0431_b, Rate Law: Vmax_r_1157*(1/kms_s_0431_br_1157)^1*(s_0431_b^1-s_0430^1/Keq_r_1157)/((1+s_0431_b/kms_s_0431_br_1157+1+s_0430/kmp_s_0430r_1157)-1) |
kms_s_0532r_0605=0.549; kmp_s_1434_br_0605=0.549; Vmax_r_0605=0.229349; kmp_s_0212r_0605=0.549; Keq_r_0605=0.6039 | Reaction: s_0532 => s_0212 + s_1434_b; s_0212, s_0532, s_1434_b, Rate Law: intracellular*Vmax_r_0605*(1/kms_s_0532r_0605)^1*(s_0532^1-s_0212^1*s_1434_b^1/Keq_r_0605)/((1+s_0532/kms_s_0532r_0605+(1+s_0212/kmp_s_0212r_0605)*(1+s_1434_b/kmp_s_1434_br_0605))-1)/intracellular |
kmp_s_1082r_0058=1.50326; Vmax_r_0058=3.30332; kms_s_0763_br_0058=0.549; Keq_r_0058=34.7263; kms_s_0257r_0058=0.549; kmp_s_0052r_0058=0.549; kms_s_1087r_0058=0.0867353 | Reaction: s_0257 + s_0763_b + s_1087 => s_0052 + s_1082; s_0052, s_0257, s_0763_b, s_1082, s_1087, Rate Law: intracellular*Vmax_r_0058*(1/kms_s_0257r_0058)^1*(1/kms_s_0763_br_0058)^1*(1/kms_s_1087r_0058)^1*(s_0257^1*s_0763_b^1*s_1087^1-s_0052^1*s_1082^1/Keq_r_0058)/(((1+s_0257/kms_s_0257r_0058)*(1+s_0763_b/kms_s_0763_br_0058)*(1+s_1087/kms_s_1087r_0058)+(1+s_0052/kmp_s_0052r_0058)*(1+s_1082/kmp_s_1082r_0058))-1)/intracellular |
kmp_s_0766_br_1503=0.1; kmp_s_1339_br_1503=1.0; Keq_r_1503=1.0; kms_s_0763_br_1503=0.549; Vmax_r_1503=0.840147; kms_s_1338r_1503=0.549 | Reaction: s_0763_b + s_1338 => s_0766_b + s_1339_b; s_0763_b, s_0766_b, s_1338, s_1339_b, Rate Law: Vmax_r_1503*(1/kms_s_0763_br_1503)^1*(1/kms_s_1338r_1503)^1*(s_0763_b^1*s_1338^1-s_0766_b^1*s_1339_b^1/Keq_r_1503)/(((1+s_0763_b/kms_s_0763_br_1503)*(1+s_1338/kms_s_1338r_1503)+(1+s_0766_b/kmp_s_0766_br_1503)*(1+s_1339_b/kmp_s_1339_br_1503))-1) |
kms_s_0446r_0249=1.09208; kmp_s_0766_br_0249=0.1; kmp_s_1207r_0249=0.549; kmp_s_0400r_0249=1.71907; Vmax_r_0249=50.4568; Keq_r_0249=0.173154; kms_s_1434_br_0249=0.549 | Reaction: s_0446 + s_1434_b => s_0400 + s_0766_b + s_1207; s_0400, s_0446, s_0766_b, s_1207, s_1434_b, Rate Law: Vmax_r_0249*(1/kms_s_0446r_0249)^1*(1/kms_s_1434_br_0249)^1*(s_0446^1*s_1434_b^1-s_0400^1*s_0766_b^1*s_1207^1/Keq_r_0249)/(((1+s_0446/kms_s_0446r_0249)*(1+s_1434_b/kms_s_1434_br_0249)+(1+s_0400/kmp_s_0400r_0249)*(1+s_0766_b/kmp_s_0766_br_0249)*(1+s_1207/kmp_s_1207r_0249))-1) |
Vmax_r_1247=4.81765; Keq_r_1247=1.0; kms_s_0650r_1247=50.0; kmp_s_0651_br_1247=24.5 | Reaction: s_0650 => s_0651_b; s_0650, s_0651_b, Rate Law: Vmax_r_1247*(1/kms_s_0650r_1247)^1*(s_0650^1-s_0651_b^1/Keq_r_1247)/((1+s_0650/kms_s_0650r_1247+1+s_0651_b/kmp_s_0651_br_1247)-1) |
kmp_s_0740r_0174=0.549; Vmax_r_0174=1.7171; kms_s_0863r_0174=0.549; kms_s_0749r_0174=0.549; Keq_r_0174=0.121402; kmp_s_1277r_0174=0.0605905 | Reaction: s_0749 + s_0863 => s_0740 + s_1277; s_0740, s_0749, s_0863, s_1277, Rate Law: intracellular*Vmax_r_0174*(1/kms_s_0749r_0174)^1*(1/kms_s_0863r_0174)^1*(s_0749^1*s_0863^1-s_0740^1*s_1277^1/Keq_r_0174)/(((1+s_0749/kms_s_0749r_0174)*(1+s_0863/kms_s_0863r_0174)+(1+s_0740/kmp_s_0740r_0174)*(1+s_1277/kmp_s_1277r_0174))-1)/intracellular |
Keq_r_0948=0.331541; kmp_s_1207r_0948=0.549; kms_s_0163r_0948=0.549; Vmax_r_0948=0.0120878; kms_s_0320r_0948=0.549; kmp_s_0335r_0948=0.549; kmp_s_1434_br_0948=0.549 | Reaction: s_0163 + s_0320 => s_0335 + s_1207 + s_1434_b; s_0163, s_0320, s_0335, s_1207, s_1434_b, Rate Law: intracellular*Vmax_r_0948*(1/kms_s_0163r_0948)^1*(1/kms_s_0320r_0948)^1*(s_0163^1*s_0320^1-s_0335^1*s_1207^1*s_1434_b^2/Keq_r_0948)/(((1+s_0163/kms_s_0163r_0948)*(1+s_0320/kms_s_0320r_0948)+(1+s_0335/kmp_s_0335r_0948)*(1+s_1207/kmp_s_1207r_0948)*(1+s_1434_b/kmp_s_1434_br_0948))-1)/intracellular |
kmp_s_0470r_0608=1.0; kms_s_0763_br_0608=0.549; kmp_s_0088r_0608=0.549; Keq_r_0608=1.1; Vmax_r_0608=0.187549; kms_s_0078r_0608=0.549; kmp_s_1434_br_0608=0.549 | Reaction: s_0078 + s_0763_b => s_0088 + s_0470 + s_1434_b; s_0078, s_0088, s_0470, s_0763_b, s_1434_b, Rate Law: intracellular*Vmax_r_0608*(1/kms_s_0078r_0608)^1*(1/kms_s_0763_br_0608)^1*(s_0078^1*s_0763_b^1-s_0088^1*s_0470^1*s_1434_b^1/Keq_r_0608)/(((1+s_0078/kms_s_0078r_0608)*(1+s_0763_b/kms_s_0763_br_0608)+(1+s_0088/kmp_s_0088r_0608)*(1+s_0470/kmp_s_0470r_0608)*(1+s_1434_b/kmp_s_1434_br_0608))-1)/intracellular |
kmp_s_1207r_0789=0.549; kms_s_1151r_0789=0.549; kmp_s_0763_br_0789=0.549; Vmax_r_0789=0.912336; kmp_s_0887r_0789=0.549; Keq_r_0789=0.6039; kms_s_0469r_0789=0.549 | Reaction: s_0469 + s_1151 => s_0763_b + s_0887 + s_1207; s_0469, s_0763_b, s_0887, s_1151, s_1207, Rate Law: intracellular*Vmax_r_0789*(1/kms_s_0469r_0789)^1*(1/kms_s_1151r_0789)^1*(s_0469^1*s_1151^1-s_0763_b^1*s_0887^1*s_1207^1/Keq_r_0789)/(((1+s_0469/kms_s_0469r_0789)*(1+s_1151/kms_s_1151r_0789)+(1+s_0763_b/kmp_s_0763_br_0789)*(1+s_0887/kmp_s_0887r_0789)*(1+s_1207/kmp_s_1207r_0789))-1)/intracellular |
s_0463_or_1814=0.549; zero_flux=0.0; V_o=0.0555; a_s_0463r_1814=1.0 | Reaction: s_0463 => s_0464_b; s_0547_b, s_0463, Rate Law: piecewise(V_o*(1+a_s_0463r_1814*ln(s_0463/s_0463_or_1814)), (V_o*(1+a_s_0463r_1814*ln(s_0463/s_0463_or_1814))) >= zero_flux, zero_flux) |
kms_s_1122r_1027=0.549; Vmax_r_1027=5.5748; kmp_s_0949r_1027=1.0; kmp_s_1207r_1027=0.549; Keq_r_1027=2.00364; kms_s_1434_br_1027=0.549 | Reaction: s_1122 + s_1434_b => s_0949 + s_1207; s_0949, s_1122, s_1207, s_1434_b, Rate Law: intracellular*Vmax_r_1027*(1/kms_s_1122r_1027)^1*(1/kms_s_1434_br_1027)^1*(s_1122^1*s_1434_b^1-s_0949^1*s_1207^1/Keq_r_1027)/(((1+s_1122/kms_s_1122r_1027)*(1+s_1434_b/kms_s_1434_br_1027)+(1+s_0949/kmp_s_0949r_1027)*(1+s_1207/kmp_s_1207r_1027))-1)/intracellular |
Vmax_r_0568=0.0076692; kmp_s_0706r_0568=0.549; Keq_r_0568=1.1; kms_s_0566r_0568=0.549; kms_s_0752r_0568=0.549; kmp_s_0562r_0568=0.549 | Reaction: s_0566 + s_0752 => s_0562 + s_0706; s_0562, s_0566, s_0706, s_0752, Rate Law: intracellular*Vmax_r_0568*(1/kms_s_0566r_0568)^1*(1/kms_s_0752r_0568)^1*(s_0566^1*s_0752^1-s_0562^1*s_0706^1/Keq_r_0568)/(((1+s_0566/kms_s_0566r_0568)*(1+s_0752/kms_s_0752r_0568)+(1+s_0562/kmp_s_0562r_0568)*(1+s_0706/kmp_s_0706r_0568))-1)/intracellular |
States:
Name | Description |
---|---|
s 0455 | [beta-D-glucose 6-phosphate] |
s 0446 | [ATP(4-)] |
s 0752 | [GMP] |
s 1162 b | [dioxygen] |
s 0380 | [acetyl-CoA] |
s 0146 | [CHEBI_52957] |
s 0532 | [D-erythro-1-(imidazol-4-yl)glycerol 3-phosphate] |
s 1154 | [orotate] |
s 0472 b | [carbon dioxide] |
s 0514 | [coenzyme A] |
s 0763 b | [proton] |
s 0755 | [GTP(4-)] |
s 1207 | [hydrogenphosphate] |
s 0547 b | [D-glucose] |
s 1096 | [NADPH] |
s 0766 b | [proton] |
s 0386 | [acyl-CoA] |
s 0078 | [1-(2-carboxyphenylamino)-1-deoxy-D-ribulose 5-phosphate] |
s 0987 | [lignocerate] |
s 0749 | [glyoxylate] |
s 0651 b | [ethanol] |
s 0419 | [alpha,alpha-trehalose 6-phosphate] |
s 0458 | [hydrogencarbonate] |
s 0530 | [D-arabinose] |
s 0464 b | [no biological data found] |
s 0431 b | [ammonium] |
s 1339 b | [succinate(2-)] |
s 1434 b | [water] |
s 0079 | [1-(5-phospho-beta-D-ribosyl)-5-[(5-phospho-beta-D-ribosylamino)methylideneamino]imidazole-4-carboxamide] |
s 0400 | [ADP] |
s 0434 | [AMP] |
s 0463 | [no biological data found] |
s 0740 | [glycine] |
s 0743 | [glycogen] |
s 0468 | [but-1-ene-1,2,4-tricarboxylic acid] |
MODEL2003190005
— v0.0.1mathematical model, with accompanying quantitative experimental data, for binding and trafficking properties of the epid…
Details
We provide a mathematical model, with accompanying quantitative experimental data, for binding and trafficking properties of the epidermal growth factor (EGF) receptor on B82 fibroblasts, and propose a theoretical dependence of cell proliferation rate on these properties. The signal for cell proliferation is generated by intrinsic EGF-receptor (EGFR) tyrosine kinase activation via EGF binding at the cell surface, and terminated by receptor/growth factor complex internalization and degradation. Our model consists of kinetic equations which describe the binding, internalization, and recycling of EGF and EGFR, along with a simple expression relation the dependence of cell cycle progression on EGFR dynamics. We show that, with key model parameters determined independently from EGF/fibroblast binding and internalization experiments, our model successfully predicts, as a first step, kinetic data for EGF binding to and internalization by B82 cells at 37°C link: http://identifiers.org/doi/10.1016/0009-2509(90)80117-W