SBMLBioModels: S - S

S


Sakmann2000_SodiumCurrent_ModelB: MODEL1006230031v0.0.1

This a model from the article: Distribution of a persistent sodium current across the ventricular wall in guinea pigs.…

Details

A tetrodotoxin-sensitive persistent sodium current, I(pNa), was found in guinea pig ventricular myocytes by whole-cell patch clamping. This current was characterized in cells derived from the basal left ventricular subendocardium, midmyocardium, and subepicardium. Midmyocardial cells show a statistically significant (P<0.05) smaller I(pNa) than subendocardial and subepicardial myocytes. There was no significant difference in I(pNa) current density between subepicardial and subendocardial cells. Computer modeling studies support a role of this current in the dispersion of action potential duration across the ventricular wall. link: http://identifiers.org/pubmed/11073887

Sakmann2000_SodiumCurrent_ModelC: MODEL1006230020v0.0.1

This a model from the article: Distribution of a persistent sodium current across the ventricular wall in guinea pigs.…

Details

A tetrodotoxin-sensitive persistent sodium current, I(pNa), was found in guinea pig ventricular myocytes by whole-cell patch clamping. This current was characterized in cells derived from the basal left ventricular subendocardium, midmyocardium, and subepicardium. Midmyocardial cells show a statistically significant (P<0.05) smaller I(pNa) than subendocardial and subepicardial myocytes. There was no significant difference in I(pNa) current density between subepicardial and subendocardial cells. Computer modeling studies support a role of this current in the dispersion of action potential duration across the ventricular wall. link: http://identifiers.org/pubmed/11073887

Salazar2009_PhotoperiodicRegulation: MODEL1005050000v0.0.1

This a model from the article: Prediction of photoperiodic regulators from quantitative gene circuit models. Salazar…

Details

Photoperiod sensors allow physiological adaptation to the changing seasons. The prevalent hypothesis is that day length perception is mediated through coupling of an endogenous rhythm with an external light signal. Sufficient molecular data are available to test this quantitatively in plants, though not yet in mammals. In Arabidopsis, the clock-regulated genes CONSTANS (CO) and FLAVIN, KELCH, F-BOX (FKF1) and their light-sensitive proteins are thought to form an external coincidence sensor. Here, we model the integration of light and timing information by CO, its target gene FLOWERING LOCUS T (FT), and the circadian clock. Among other predictions, our models show that FKF1 activates FT. We demonstrate experimentally that this effect is independent of the known activation of CO by FKF1, thus we locate a major, novel controller of photoperiodism. External coincidence is part of a complex photoperiod sensor: modeling makes this complexity explicit and may thus contribute to crop improvement. link: http://identifiers.org/pubmed/20005809

Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation: BIOMD0000000725v0.0.1

Salcedo-Sora2016 - Microbial folate biosynthesis and utilisationThis model is described in the article: [ A mathematica…

Details

The metabolic biochemistry of folate biosynthesis and utilisation has evolved into a complex network of reactions. Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole. link: http://identifiers.org/pubmed/26794619

Parameters:

NameDescription
Km=7.4; V=792.064Reaction: DHNTP => AHMDHP + HAD + Pi; DHNTP, Rate Law: compartment*V*DHNTP/(Km+DHNTP)
katp=15.0; vmax=382.2; kahmdhp=3.6Reaction: AHMDHP + ATP => AHMDPP + AMP; AHMDHP, ATP, Rate Law: compartment*vmax*ATP*AHMDHP/(kahmdhp*katp+katp*ATP+kahmdhp*AHMDHP+ATP*AHMDHP)
kdlp=290.0; kgly=4505.0; vmax=751.66Reaction: DLp + Gly => SAmDLp + COTwo; DLp, Gly, Rate Law: compartment*vmax*DLp*Gly/(kgly*kdlp+kgly*Gly+kdlp*DLp+DLp*Gly)
vmax=379.925; khcy=17.0; kmthfglu=30.0Reaction: MTHFGlu + Hcy => THFGlu + Met; MTHFGlu, Hcy, Rate Law: compartment*vmax*MTHFGlu*Hcy/(kmthfglu*khcy+kmthfglu*Hcy+khcy*MTHFGlu+MTHFGlu*Hcy)
kffthfglu=5.0; katp=50.0; vmax=500.0Reaction: ATP + ffTHFGlu => ADP + Pi + meTHFGlu; ATP, ffTHFGlu, Rate Law: compartment*vmax*ATP*ffTHFGlu/(katp*kffthfglu+katp*ffTHFGlu+kffthfglu*ATP+ATP*ffTHFGlu)
k1=0.08; k2=0.031Reaction: meTHFGlu => fTHFGlu; meTHFGlu, fTHFGlu, Rate Law: compartment*(k1*meTHFGlu-k2*fTHFGlu)
kgln=1100.0; kcm=13.0; vmax=26.0Reaction: CM + Gln => ADC + Glu; CM, Gln, Rate Law: compartment*vmax*CM*Gln/(kcm*kgln+kcm*Gln+kgln*CM+CM*Gln)
vmax=15315.3; kformyl=3190.0; kthfglu=134.0; katp=74.5Reaction: fTHFGlu + ADP + Pi => THFGlu + ATP + Formyl; fTHFGlu, ADP, Pi, Rate Law: compartment*vmax*fTHFGlu*ADP*Pi/(kthfglu*kformyl*katp+kthfglu*(ADP+Pi)+kformyl*(fTHFGlu+Pi)+katp*(ADP+fTHFGlu)+fTHFGlu*ADP*Pi)
kep=285.0; kpep=36.0; vmax=578.76Reaction: PEP + EP => DAHP + Pi; PEP, EP, Rate Law: compartment*vmax*EP*PEP/(kpep*kep+kpep*EP+kep*PEP+EP*PEP)
knadp=22.0; kidhf=0.428; vmax=1892.8; kmythfglu=25.0Reaction: myTHFGlu + NADP => meTHFGlu + NADPH; DHF, myTHFGlu, NADP, Rate Law: compartment*vmax*myTHFGlu*NADP/(kmythfglu*(1+DHF/kidhf)*knadp+kmythfglu*NADP+knadp*myTHFGlu+myTHFGlu*NADP)
kthf=26.0; vmax=84.63; kglu=740.0; katp=128.0; kidhf=3.1Reaction: THF + Glu + ATP => THFGlu + ADP + Pi; DHF, THF, Glu, ATP, Rate Law: compartment*vmax*THF*Glu*ATP/(kthf*(1+DHF/kidhf)*kglu*katp+kthf*(Glu+ATP)+kglu*(THF+ATP)+katp*(THF+Glu)+THF*Glu*ATP)
Km=4.7; V=7.462Reaction: DAHP => DHQ + Pi; DAHP, Rate Law: compartment*V*DAHP/(Km+DAHP)
kthfglu=40.0; kithf=0.157; kser=700.0; vmax=682.5Reaction: THFGlu + Ser => myTHFGlu + Gly; THF, THFGlu, Ser, Rate Law: compartment*vmax*THFGlu*Ser/(kthfglu*(1+THF/kithf)*kser+kthfglu*Ser+kser*THFGlu+THFGlu*Ser)
kfthfglu=7.85; vmax=59.332; knadp=0.9Reaction: fTHFGlu + NADP => THFGlu + COTwo + NADPH; fTHFGlu, NADP, Rate Law: compartment*vmax*fTHFGlu*NADP/(kfthfglu*knadp+kfthfglu*NADP+knadp*fTHFGlu+fTHFGlu*NADP)
ksk=200.0; vmax=18200.0; katp=151.5Reaction: SK + ATP => SKP + ADP + Pi; SK, ATP, Rate Law: compartment*vmax*SK*ATP/(ksk*katp+ksk*ATP+katp*SK+SK*ATP)
kdhsk=30.0; knadph=11.0; vmax=17290.0Reaction: DHSK + NADPH => SK + NADP; DHSK, NADPH, Rate Law: compartment*vmax*DHSK*NADPH/(kdhsk*knadph+kdhsk*NADPH+knadph*DHSK+DHSK*NADPH)
kmythfglu=17.0; vmax=49.14; kidhf=0.428; kdump=5.4Reaction: myTHFGlu + dUMP => dTMP + DHF; DHF, myTHFGlu, dUMP, Rate Law: compartment*vmax*myTHFGlu*dUMP/(kmythfglu*(1+DHF/kidhf)*kdump+kmythfglu*dUMP+kdump*myTHFGlu+myTHFGlu*dUMP)
Km=58.0; V=116.48Reaction: DHQ => DHSK; DHQ, Rate Law: compartment*V*DHQ/(Km+DHQ)
kpaba=2.6; vmax=105.014; kahmdpp=3.15Reaction: AHMDPP + pABA => DHP + Pi; AHMDPP, pABA, Rate Law: compartment*vmax*AHMDPP*pABA/(kahmdpp*kpaba+kpaba*AHMDPP+kahmdpp*pABA+AHMDPP*pABA)
kdhf=3.0; vmax=3000.0; knadph=6.12Reaction: DHF + NADPH => THF + NADP; DHF, NADPH, Rate Law: compartment*vmax*DHF*NADPH/(kdhf*knadph+kdhf*NADPH+knadph*DHF+DHF*NADPH)
vmax=196.56; ksamdlp=290.0; kthfglu=67.7Reaction: THFGlu + SAmDLp => myTHFGlu + Lp + Ammonia; THFGlu, SAmDLp, Rate Law: compartment*vmax*THFGlu*SAmDLp/(kthfglu*ksamdlp+kthfglu*SAmDLp+ksamdlp*THFGlu+THFGlu*SAmDLp)
V=2.2; Km=1.1Reaction: ADC => pABA + Pyr; ADC, Rate Law: compartment*V*ADC/(Km+ADC)
vmax=2.821; kglu=1380.0; katp=100.0; kdhp=1.0Reaction: DHP + Glu + ATP => DHF + ADP + Pi; DHP, Glu, ATP, Rate Law: compartment*vmax*DHP*Glu*ATP/(kdhp*kglu*katp+kdhp*(Glu+ATP)+kglu*(DHP+ATP)+katp*(Glu+ATP)+DHP*Glu*ATP)
vmax=738.92; knadph=19.0; kmythfglu=33.0; kidhf=0.428Reaction: myTHFGlu + NADPH => MTHFGlu + NADP; DHF, myTHFGlu, NADPH, Rate Law: compartment*vmax*myTHFGlu*NADPH/(kmythfglu*(1+DHF/kidhf)*knadph+kmythfglu*NADPH+knadph*myTHFGlu+myTHFGlu*NADPH)
klp=1280.0; knadh=58.0; vmax=5432.7Reaction: Lp + NADH => DLp + NAD; NADH, Lp, Rate Law: compartment*vmax*NADH*Lp/(knadh*klp+knadh*Lp+klp*NADH+NADH*Lp)
Km=12.7; V=728.0Reaction: CVPSK => CM + Pi; CVPSK, Rate Law: compartment*V*CVPSK/(Km+CVPSK)
kmtrna=1.07; vmax=116.48; kfthfglu=12.15Reaction: fTHFGlu + mtRNA => fmtRNA + THFGlu; fTHFGlu, mtRNA, Rate Law: compartment*vmax*fTHFGlu*mtRNA/(kfthfglu*kmtrna+kfthfglu*mtRNA+kmtrna*fTHFGlu+fTHFGlu*mtRNA)
Km=10.0; V=22.659Reaction: DHNTP => PTHP + Pi; DHNTP, Rate Law: compartment*V*DHNTP/(Km+DHNTP)
kgtp=17.6; vmax=1515.15; kiTHF=0.157Reaction: GTP => DHNTP + Formyl; THF, GTP, Rate Law: compartment*vmax*GTP/(kgtp*(1+THF/kiTHF)+GTP)
kpep=93.0; kskp=80.0; vmax=1547.0Reaction: SKP + PEP => CVPSK + Pi; SKP, PEP, Rate Law: compartment*vmax*SKP*PEP/(kpep*kskp+kpep*PEP+kskp*SKP+PEP*SKP)
Km=67.0; V=200.0Reaction: meTHFGlu => ffTHFGlu; meTHFGlu, Rate Law: compartment*V*meTHFGlu/(Km+meTHFGlu)

States:

NameDescription
DHSK[3-Dehydroshikimate]
THF[Tetrahydrofolate]
DHF[Dihydrofolate]
NADPH[NADPH]
myTHFGlu[5,10-Methylenetetrahydrofolate]
fTHFGlu[10-Formyltetrahydrofolate]
Lp[Lipoylprotein]
NADP[NADP+]
EP[D-Erythrose 4-phosphate]
THFGlu[THF-polyglutamate]
mtRNA[L-Methionyl-tRNA]
ADC[4-Amino-4-deoxychorismate]
fmtRNA[N-Formylmethionyl-tRNA]
Formyl[Formate]
DLp[Dihydrolipoylprotein]
ADP[ADP]
NAD[NAD+]
HAD[Glycolaldehyde]
COTwoCOTwo
ATP[ATP]
Gln[L-Glutamine]
DHNTP[7,8-Dihydroneopterin 3'-triphosphate]
CM[Chorismate]
Ammonia[Ammonia]
AHMDHP[6-(Hydroxymethyl)-7,8-dihydropterin]
AMP[AMP]
GTP[GTP]
DHP[Dihydropteroate]
ffTHFGlu[Folinic acid]
SKP[Shikimate 3-phosphate]
Glu[L-Glutamate]
meTHFGlu[5,10-Methenyltetrahydrofolate]
AHMDPP[6-Hydroxymethyl-7,8-dihydropterin diphosphate]
PTHP[6-Pyruvoyltetrahydropterin]
NADH[NADH]
SAmDLp[S-Aminomethyldihydrolipoylprotein]
pABA[4-Aminobenzoate]
CVPSK[5-O-(1-Carboxyvinyl)-3-phosphoshikimate]
DAHP[2-Dehydro-3-deoxy-D-arabino-heptonate 7-phosphate]
Pi[Orthophosphate]
SK[Shikimate]
DHQ[3-Dehydroquinate]
dUMP[dUMP]

Sanchez2017 - Inflammatory responses during acute hyperinsulinemia: MODEL1606020000v0.0.1

Sanchez2017 - Inflammatory responses during acute hyperinsulinemia This model is described in the article: [The CD4+ T…

Details

Obesity is frequently linked to insulin resistance, high insulin levels, chronic inflammation, and alterations in the behaviour of CD4+ T cells. Despite the biomedical importance of this condition, the system-level mechanisms that alter CD4+ T cell differentiation and plasticity are not well understood.

We model how hyperinsulinemia alters the dynamics of the CD4+ T regulatory network, and this, in turn, modulates cell differentiation and plasticity. Different polarizing microenvironments are simulated under basal and high levels of insulin to assess impacts on cell-fate attainment and robustness in response to transient perturbations. In the presence of high levels of insulin Th1 and Th17 become more stable to transient perturbations, and their basin sizes are augmented, Tr1 cells become less stable or disappear, while TGF? producing cells remain unaltered. Hence, the model provides a dynamic system-level framework and explanation to further understand the documented and apparently paradoxical role of TGF? in both inflammation and regulation of immune responses, as well as the emergence of the adipose Treg phenotype. Furthermore, our simulations provide new predictions on the impact of the microenvironment in the coexistence of the different cell types, suggesting that in pro-Th1, pro-Th2 and pro-Th17 environments effector and regulatory cells can coexist, but that high levels of insulin severely diminish regulatory cells, especially in a pro-Th17 environment.

This work provides a first step towards a system-level formal and dynamic framework to integrate further experimental data in the study of complex inflammatory diseases. link: http://identifiers.org/doi/10.1186/s12918-017-0436-y

Sandip2013 - Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.: MODEL1912120002v0.0.1

Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin. Banerjee S1,…

Details

A mathematical modeling of hepatitis C virus (HCV) dynamics and antiviral therapy has been presented in this paper. The proposed model, which involves four coupled ordinary differential equations, describes the interaction of target cells (hepatocytes), infected cells, infectious virions and non-infectious virions. The model takes into consideration the addition of ribavirin to interferon therapy and explains the dynamics regarding a biphasic and triphasic decline of viral load in the model. A critical drug efficacy parameter has been defined and it is shown that for an efficacy above this critical value, HCV is eradicated whereas for efficacy lower this critical value, a new steady state for infectious virions is reached, which is lower than the previous steady state value. link: http://identifiers.org/pubmed/23891586

Sandip2013 - Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.: BIOMD0000000892v0.0.1

Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin. Banerjee S1,…

Details

A mathematical modeling of hepatitis C virus (HCV) dynamics and antiviral therapy has been presented in this paper. The proposed model, which involves four coupled ordinary differential equations, describes the interaction of target cells (hepatocytes), infected cells, infectious virions and non-infectious virions. The model takes into consideration the addition of ribavirin to interferon therapy and explains the dynamics regarding a biphasic and triphasic decline of viral load in the model. A critical drug efficacy parameter has been defined and it is shown that for an efficacy above this critical value, HCV is eradicated whereas for efficacy lower this critical value, a new steady state for infectious virions is reached, which is lower than the previous steady state value. link: http://identifiers.org/pubmed/23891586

Parameters:

NameDescription
d2 = 1.0Reaction: I =>, Rate Law: compartment*d2*I
n1 = 0.8; alpha = 2.25E-7; c = 0.5Reaction: => I; VI, T, Rate Law: compartment*(1-c*n1)*alpha*VI*T
r = 1.99; s = 1.0; k = 3.6E7Reaction: => T, Rate Law: compartment*(s+r*T*(1-(T+1)/k))
n1 = 0.8; alpha = 2.25E-7; d1 = 0.01; c = 0.5Reaction: T => ; VI, Rate Law: compartment*(d1*T+(1-c*n1)*alpha*VI*T)
d3 = 6.0Reaction: VI =>, Rate Law: compartment*d3*VI
n1 = 0.8; beta = 2.9; nr = 0.0Reaction: => VI; I, Rate Law: compartment*(1-(nr+n1)/2)*beta*I

States:

NameDescription
I[hepatocyte]
T[Neoplastic Cell]
VNIVNI
VIVI

Sanjuan2013 - Evolution of HIV T-cell epitope, control model: MODEL1302180002v0.0.1

Sanjuan2013 - Evolution of HIV T-cell epitope, control modelControl model in which the virus targets a nonimmune cell ty…

Details

The immune system should constitute a strong selective pressure promoting viral genetic diversity and evolution. However, HIV shows lower sequence variability at T-cell epitopes than elsewhere in the genome, in contrast with other human RNA viruses. Here, we propose that epitope conservation is a consequence of the particular interactions established between HIV and the immune system. On one hand, epitope recognition triggers an anti-HIV response mediated by cytotoxic T-lymphocytes (CTLs), but on the other hand, activation of CD4(+) helper T lymphocytes (TH cells) promotes HIV replication. Mathematical modeling of these opposite selective forces revealed that selection at the intrapatient level can promote either T-cell epitope conservation or escape. We predict greater conservation for epitopes contributing significantly to total immune activation levels (immunodominance), and when TH cell infection is concomitant to epitope recognition (trans-infection). We suggest that HIV-driven immune activation in the lymph nodes during the chronic stage of the disease may offer a favorable scenario for epitope conservation. Our results also support the view that some pathogens draw benefits from the immune response and suggest that vaccination strategies based on conserved TH epitopes may be counterproductive. link: http://identifiers.org/pubmed/23565057

Sanjuan2013 - Evolution of HIV T-cell epitope, immune activation model: MODEL1302180001v0.0.1

Sanjuan2013 - Evolution of HIV T-cell epitope, immune activation modelModel of cellular immune response against HIV. Th…

Details

The immune system should constitute a strong selective pressure promoting viral genetic diversity and evolution. However, HIV shows lower sequence variability at T-cell epitopes than elsewhere in the genome, in contrast with other human RNA viruses. Here, we propose that epitope conservation is a consequence of the particular interactions established between HIV and the immune system. On one hand, epitope recognition triggers an anti-HIV response mediated by cytotoxic T-lymphocytes (CTLs), but on the other hand, activation of CD4(+) helper T lymphocytes (TH cells) promotes HIV replication. Mathematical modeling of these opposite selective forces revealed that selection at the intrapatient level can promote either T-cell epitope conservation or escape. We predict greater conservation for epitopes contributing significantly to total immune activation levels (immunodominance), and when TH cell infection is concomitant to epitope recognition (trans-infection). We suggest that HIV-driven immune activation in the lymph nodes during the chronic stage of the disease may offer a favorable scenario for epitope conservation. Our results also support the view that some pathogens draw benefits from the immune response and suggest that vaccination strategies based on conserved TH epitopes may be counterproductive. link: http://identifiers.org/pubmed/23565057

Santolini2001_nNOS_Mechanism_Regulation: BIOMD0000000199v0.0.1

This is a model of neuronal Nitric Oxide Synthase expressed in Escherichia coli based on [ Santolini J. et al. J B…

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After initiating NO synthesis a majority of neuronal NO synthase (nNOS) quickly partitions into a ferrous heme-NO complex. This down-regulates activity and increases enzyme K(m,O(2)). To understand this process, we developed a 10-step kinetic model in which the ferric heme-NO enzyme forms as the immediate product of catalysis, and then partitions between NO dissociation versus reduction to a ferrous heme-NO complex. Rate constants used for the model were derived from recent literature or were determined here. Computer simulations of the model precisely described both pre-steady and steady-state features of nNOS catalysis, including NADPH consumption and NO production, buildup of a heme-NO complex, changes between pre-steady and steady-state rates, and the change in enzyme K(m,O(2)) in the presence or absence of NO synthesis. The model also correctly simulated the catalytic features of nNOS mutants W409F and W409Y, which are hyperactive and display less heme-NO complex formation in the steady state. Model simulations showed how the rate of heme reduction influences several features of nNOS catalysis, including populations of NO-bound versus NO-free enzyme in the steady state and the rate of NO synthesis. The simulation predicts that there is an optimum rate of heme reduction that is close to the measured rate in nNOS. Ratio between NADPH consumption and NO synthesis is also predicted to increase with faster heme reduction. Our kinetic model is an accurate and versatile tool for understanding catalytic behavior and will provide new perspectives on NOS regulation. link: http://identifiers.org/pubmed/11038356

Parameters:

NameDescription
k9 = 1.0E-4 s^(-1)Reaction: FeII_NO => FeII + NO, Rate Law: cytosol*k9*FeII_NO
k1 = 2.6 s^(-1)Reaction: FeIII + NADPH => FeII + NADPplus, Rate Law: cytosol*k1*FeIII
k2 = 0.9 l*μmol^(-1)*s^(-1)Reaction: FeII + O2 => FeII_O2, Rate Law: cytosol*k2*FeII*O2
k8 = 2.6 s^(-1)Reaction: FeIII_NO + NADPH => FeII_NO + NADPplus, Rate Law: cytosol*k8*FeIII_NO
k7 = 5.0 s^(-1)Reaction: FeIII_NO => FeIII + NO, Rate Law: cytosol*k7*FeIII_NO
k6 = 26.0 s^(-1)Reaction: FeII_star_O2 => FeIII_NO + citrulline, Rate Law: cytosol*k6*FeII_star_O2
k4 = 2.6 s^(-1)Reaction: FeIII_star + NADPH => FeII_star + NADPplus, Rate Law: cytosol*k4*FeIII_star
k3 = 26.0 s^(-1)Reaction: FeII_O2 => FeIII_star, Rate Law: cytosol*k3*FeII_O2
k5 = 0.9 l*μmol^(-1)*s^(-1)Reaction: FeII_star + O2 => FeII_star_O2, Rate Law: cytosol*k5*FeII_star*O2
k10 = 0.0013 l*μmol^(-1)*s^(-1)Reaction: FeII_NO + O2 => FeIII + NO3, Rate Law: cytosol*k10*FeII_NO*O2

States:

NameDescription
citrulline[L-citrulline; L-Citrulline]
NO[nitric oxide; Nitric oxide]
FeII[ferroheme b; iron(2+); Nitric oxide synthase, brain]
FeII star O2[dioxygen; ferroheme b; iron(2+); Nitric oxide synthase, brain]
FeII NO[nitric oxide; ferroheme b; iron(2+); Nitric oxide synthase, brain]
FeIII[iron(3+); ferroheme b; Nitric oxide synthase, brain]
FeIII t[iron(3+); ferroheme b; Nitric oxide synthase, brain]
FeIII star[ferroheme b; iron(3+); Nitric oxide synthase, brain]
NADPH[NADPH; NADPH]
FeIII NO[nitric oxide; ferroheme b; iron(3+); Nitric oxide synthase, brain]
NO3[nitrate; Nitrate]
FeII O2[dioxygen; iron(2+); ferroheme b; Nitric oxide synthase, brain]
NADPplus[NADP(+); NADP+]
FeII star[ferroheme b; iron(2+); Nitric oxide synthase, brain]
O2[dioxygen; Oxygen]

Sarai2003_CardiacSAnodePacemaker: MODEL1006230108v0.0.1

This a model from the article: Role of individual ionic current systems in the SA node hypothesized by a model study.…

Details

This paper discusses the development of a cardiac sinoatrial (SA) node pacemaker model. The model successfully reconstructs the experimental action potentials at various concentrations of external Ca2+ and K+. Increasing the amplitude of L-type Ca2+ current (I(CaL)) prolongs the duration of the action potential and thereby slightly decreases the spontaneous rate. On the other hand, a negative voltage shift of I(CaL) gating by a few mV markedly increases the spontaneous rate. When the amplitude of sustained inward current (I(st)) is increased, the spontaneous rate is increased irrespective of the I(CaL) amplitude. Increasing Ca2+ shortens the action potential and increases the spontaneous rate. When the spontaneous activity is stopped by decreasing I(CaL) amplitude, the resting potential is nearly constant (-35 mV) over 1-15 mM K+ as observed in the experiment. This is because the conductance of the inward background non-selective cation current balances with the outward K+-dependent K+ conductance. The unique role of individual voltage- and time-dependent ion channels is clearly demonstrated and distinguished from that of the background current by calculating an instantaneous zero current potential ("lead potential") during the course of the spontaneous activity. link: http://identifiers.org/pubmed/12877768

Sarkar2020 - SAIR model of COVID-19 transmission with quarantine measures in India: BIOMD0000000977v0.0.1

In India, 100,340 confirmed cases and 3155 confirmed deaths due to COVID-19 were reported as of May 18, 2020. Due to abs…

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In India, 100,340 confirmed cases and 3155 confirmed deaths due to COVID-19 were reported as of May 18, 2020. Due to absence of specific vaccine or therapy, non-pharmacological interventions including social distancing, contact tracing are essential to end the worldwide COVID-19. We propose a mathematical model that predicts the dynamics of COVID-19 in 17 provinces of India and the overall India. A complete scenario is given to demonstrate the estimated pandemic life cycle along with the real data or history to date, which in turn divulges the predicted inflection point and ending phase of SARS-CoV-2. The proposed model monitors the dynamics of six compartments, namely susceptible (S), asymptomatic (A), recovered (R), infected (I), isolated infected (Iq ) and quarantined susceptible (Sq ), collectively expressed SARIIqSq . A sensitivity analysis is conducted to determine the robustness of model predictions to parameter values and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in India. Our results reveal that achieving a reduction in the contact rate between uninfected and infected individuals by quarantined the susceptible individuals, can effectively reduce the basic reproduction number. Our model simulations demonstrate that the elimination of ongoing SARS-CoV-2 pandemic is possible by combining the restrictive social distancing and contact tracing. Our predictions are based on real data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced. link: http://identifiers.org/pubmed/32834603

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_PSEQ): MODEL1204280005v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_PSEQ_short_duration_signal): MODEL1204280013v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_USEQ): MODEL1204280001v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_USEQ_short_duration_signal): MODEL1204280009v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K1_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_PSEQ): MODEL1204280021v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_PSEQ_short_duration_signal): MODEL1204280029v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_QSS_PSEQ): MODEL1204280037v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_QSS_PSEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_QSS_USEQ): MODEL1204280033v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_QSS_USEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_USEQ): MODEL1204280017v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_USEQ_short_duration_signal): MODEL1204280025v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M1_K2_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_PSEQ): MODEL1204280006v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_PSEQ_short_duration_signal): MODEL1204280014v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_USEQ): MODEL1204280002v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_USEQ_short_duration_signal): MODEL1204280010v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K1_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_PSEQ): MODEL1204280022v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_PSEQ_short_duration_signal): MODEL1204280030v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_QSS_PSEQ): MODEL1204280038v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_QSS_PSEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_QSS_USEQ): MODEL1204280034v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_QSS_USEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_USEQ): MODEL1204280018v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_USEQ_short_duration_signal): MODEL1204280026v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M2_K2_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_PSEQ): MODEL1204280007v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_PSEQ_short_duration_signal): MODEL1204280015v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_USEQ): MODEL1204280003v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_USEQ_short_duration_signal): MODEL1204280011v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K1_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_PSEQ): MODEL1204280023v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_PSEQ_short_duration_signal): MODEL1204280031v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_QSS_PSEQ): MODEL1204280039v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_QSS_PSEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_QSS_USEQ): MODEL1204280035v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_QSS_USEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_USEQ): MODEL1204280019v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_USEQ_short_duration_signal): MODEL1204280027v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M3_K2_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_PSEQ): MODEL1204280008v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_PSEQ_short_duration_signal): MODEL1204280016v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_USEQ): MODEL1204280004v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_USEQ_short_duration_signal): MODEL1204280012v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K1_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ): BIOMD0000000431v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Parameters:

NameDescription
k1=0.02; k2=1.0Reaction: species_1 + species_2 => species_3; species_1, species_2, species_3, Rate Law: compartment_1*(k1*species_1*species_2-k2*species_3)
k1=1.0Reaction: species_17 => species_8 + species_18; species_17, Rate Law: compartment_1*k1*species_17
k1=15.0Reaction: species_11 => species_2 + species_8; species_11, Rate Law: compartment_1*k1*species_11
k1=0.092Reaction: species_21 => species_4 + species_13; species_21, Rate Law: compartment_1*k1*species_21
k1=0.5Reaction: species_14 => species_15; species_14, Rate Law: compartment_1*k1*species_14
k1=0.01Reaction: species_3 => species_4 + species_2; species_3, Rate Law: compartment_1*k1*species_3
k1=0.032; k2=1.0Reaction: species_4 + species_2 => species_5; species_4, species_2, species_5, Rate Law: compartment_1*(k1*species_4*species_2-k2*species_5)
k1=0.01; k2=1.0Reaction: species_10 + species_13 => species_14; species_10, species_13, species_14, Rate Law: compartment_1*(k1*species_10*species_13-k2*species_14)
k2=0.005; k1=0.086Reaction: species_23 => species_1 + species_13; species_23, species_1, species_13, Rate Law: compartment_1*(k1*species_23-k2*species_1*species_13)
k1=0.045; k2=1.0Reaction: species_2 + species_13 => species_12; species_2, species_13, species_12, Rate Law: compartment_1*(k1*species_2*species_13-k2*species_12)

States:

NameDescription
species 9[RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 27[RAF proto-oncogene serine/threonine-protein kinase; Dual specificity protein phosphatase 3]
species 1[Mitogen-activated protein kinase 3]
species 18[Ras-related protein Rap-2b]
species 4[Mitogen-activated protein kinase 3; Phosphoprotein]
species 20[Dual specificity protein phosphatase 3]
species 16[RAF proto-oncogene serine/threonine-protein kinase]
species 21[Mitogen-activated protein kinase 3; Dual specificity protein phosphatase 3; Phosphoprotein]
species 8[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 17[RAF proto-oncogene serine/threonine-protein kinase; Ras-related protein Rap-2b]
species 12[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 25[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 5[Mitogen-activated protein kinase 3; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 15[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3]
species 2[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 6[Mitogen-activated protein kinase 3; Phosphoprotein]
species 19[RAF proto-oncogene serine/threonine-protein kinase; Dual specificity protein phosphatase 3; Phosphoprotein]
species 10[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 11[RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 24[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 14[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 22[Mitogen-activated protein kinase 3; Dual specificity protein phosphatase 3; Phosphoprotein]
species 3[Mitogen-activated protein kinase 3; Phosphoprotein]
species 23[Mitogen-activated protein kinase 3; Dual specificity protein phosphatase 3]
species 7[Dual specificity mitogen-activated protein kinase kinase 1]
species 26[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3]
species 13[Dual specificity protein phosphatase 3]

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ_short_duration_signal): MODEL1204280032v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ): BIOMD0000000433v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Parameters:

NameDescription
parameter_7 = 50.5; k7=0.01; parameter_8 = 500.0Reaction: species_6 => species_7; species_5, species_7, species_5, species_6, species_7, Rate Law: compartment_1*k7*species_5*species_6/parameter_7/(1+species_6/parameter_7+species_7/parameter_8)
k4=15.0; parameter_4 = 500.0; parameter_3 = 50.5Reaction: species_4 => species_5; species_2, species_3, species_2, species_4, species_3, Rate Law: compartment_1*k4*species_2*species_4/parameter_4/(1+species_3/parameter_3+species_4/parameter_4)
parameter_10 = 108.6; parameter_12 = 0.06; k10b=0.086; parameter_14 = 108.6; parameter_13 = 24.3; parameter_9 = 24.3Reaction: species_7 => species_6; species_5, species_4, species_8, species_3, species_6, species_10, species_7, species_5, species_4, species_8, species_3, species_6, species_10, Rate Law: compartment_1*k10b*species_10*species_7/parameter_10/(1+species_5/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9)
parameter_10 = 108.6; k6b=0.086; k6a=0.086; parameter_11 = 0.06; parameter_12 = 0.06; parameter_14 = 108.6; parameter_6 = 108.6; parameter_13 = 24.3; parameter_5 = 24.3; parameter_2 = 54.3; parameter_9 = 24.3Reaction: species_4 => species_3; species_9, species_5, species_7, species_8, species_3, species_6, species_1, species_2, species_10, species_9, species_4, species_5, species_7, species_8, species_3, species_6, species_1, species_2, species_10, Rate Law: compartment_1*(k6a*species_9*species_4/parameter_6/(1+species_2/parameter_2+species_1/parameter_11+species_5/parameter_5+species_4/parameter_6+species_3/parameter_11)+k6b*species_10*species_4/parameter_14/(1+species_5/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9))
k1=1.0; parameter_1 = 100.0Reaction: species_1 => species_2; species_11, species_1, species_11, Rate Law: compartment_1*k1*species_11*species_1/(parameter_1+species_1)
parameter_11 = 0.06; parameter_6 = 108.6; parameter_5 = 24.3; parameter_2 = 54.3; k2a=0.086Reaction: species_2 => species_1; species_1, species_9, species_5, species_4, species_3, species_2, species_1, species_9, species_5, species_4, species_3, Rate Law: compartment_1*k2a*species_2*species_9/parameter_2/(1+species_2/parameter_2+species_1/parameter_11+species_5/parameter_5+species_4/parameter_6+species_3/parameter_11)
parameter_10 = 108.6; k5a=0.092; k5b=0.092; parameter_11 = 0.06; parameter_12 = 0.06; parameter_14 = 108.6; parameter_6 = 108.6; parameter_13 = 24.3; parameter_5 = 24.3; parameter_2 = 54.3; parameter_9 = 24.3Reaction: species_5 => species_4; species_4, species_7, species_8, species_9, species_3, species_6, species_1, species_2, species_10, species_5, species_4, species_7, species_8, species_9, species_3, species_6, species_1, species_2, species_10, Rate Law: compartment_1*(k5a*species_9*species_5/parameter_5/(1+species_2/parameter_2+species_1/parameter_11+species_5/parameter_5+species_4/parameter_6+species_3/parameter_11)+k5b*species_10*species_5/parameter_13/(1+species_5/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9))
k7=15.0; parameter_7 = 50.5; parameter_8 = 500.0Reaction: species_7 => species_8; species_5, species_6, species_5, species_7, species_6, Rate Law: compartment_1*k7*species_5*species_7/parameter_8/(1+species_6/parameter_7+species_7/parameter_8)
k3=0.01; parameter_4 = 500.0; parameter_3 = 50.5Reaction: species_3 => species_4; species_2, species_4, species_2, species_3, species_4, Rate Law: compartment_1*k3*species_2*species_3/parameter_3/(1+species_3/parameter_3+species_4/parameter_4)
parameter_12 = 0.06; parameter_10 = 108.6; k9b=0.092; parameter_14 = 108.6; parameter_13 = 24.3; parameter_9 = 24.3Reaction: species_8 => species_7; species_9, species_4, species_7, species_3, species_6, species_10, species_8, species_9, species_4, species_7, species_3, species_6, species_10, Rate Law: compartment_1*k9b*species_10*species_8/parameter_9/(1+species_9/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9)

States:

NameDescription
species 2[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 6[Mitogen-activated protein kinase 3]
species 3[Dual specificity mitogen-activated protein kinase kinase 1]
species 1[RAF proto-oncogene serine/threonine-protein kinase]
species 8[Mitogen-activated protein kinase 3; Phosphoprotein]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 7[Mitogen-activated protein kinase 3; Phosphoprotein]
species 5[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ): BIOMD0000000432v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ)The paper presents the various interaction topologies…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Parameters:

NameDescription
parameter_10 = 108.6; k6b=0.086; k6a=0.086; parameter_12 = 3.0E51; parameter_11 = 3.0E51; parameter_14 = 108.6; parameter_6 = 108.6; parameter_13 = 24.3; parameter_5 = 24.3; parameter_2 = 54.3; parameter_9 = 24.3Reaction: species_4 => species_3; species_9, species_5, species_7, species_8, species_3, species_6, species_1, species_2, species_10, species_9, species_4, species_5, species_7, species_8, species_3, species_6, species_1, species_2, species_10, Rate Law: compartment_1*(k6a*species_9*species_4/parameter_6/(1+species_2/parameter_2+species_1/parameter_11+species_5/parameter_5+species_4/parameter_6+species_3/parameter_11)+k6b*species_10*species_4/parameter_14/(1+species_5/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9))
k4=15.0; parameter_4 = 500.0; parameter_3 = 50.5Reaction: species_4 => species_5; species_2, species_3, species_2, species_4, species_3, Rate Law: compartment_1*k4*species_2*species_4/parameter_4/(1+species_3/parameter_3+species_4/parameter_4)
parameter_10 = 108.6; k10b=0.086; parameter_14 = 108.6; parameter_13 = 24.3; parameter_12 = 3.0E51; parameter_9 = 24.3Reaction: species_7 => species_6; species_5, species_4, species_8, species_3, species_6, species_10, species_7, species_5, species_4, species_8, species_3, species_6, species_10, Rate Law: compartment_1*k10b*species_10*species_7/parameter_10/(1+species_5/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9)
parameter_10 = 108.6; k5a=0.092; k5b=0.092; parameter_12 = 3.0E51; parameter_11 = 3.0E51; parameter_14 = 108.6; parameter_6 = 108.6; parameter_13 = 24.3; parameter_5 = 24.3; parameter_2 = 54.3; parameter_9 = 24.3Reaction: species_5 => species_4; species_4, species_7, species_8, species_9, species_3, species_6, species_1, species_2, species_10, species_5, species_4, species_7, species_8, species_9, species_3, species_6, species_1, species_2, species_10, Rate Law: compartment_1*(k5a*species_9*species_5/parameter_5/(1+species_2/parameter_2+species_1/parameter_11+species_5/parameter_5+species_4/parameter_6+species_3/parameter_11)+k5b*species_10*species_5/parameter_13/(1+species_5/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9))
parameter_7 = 50.5; k7=0.01; parameter_8 = 500.0Reaction: species_6 => species_7; species_5, species_7, species_5, species_6, species_7, Rate Law: compartment_1*k7*species_5*species_6/parameter_7/(1+species_6/parameter_7+species_7/parameter_8)
parameter_10 = 108.6; k9b=0.092; parameter_14 = 108.6; parameter_13 = 24.3; parameter_9 = 24.3; parameter_12 = 3.0E51Reaction: species_8 => species_7; species_9, species_4, species_7, species_3, species_6, species_10, species_8, species_9, species_4, species_7, species_3, species_6, species_10, Rate Law: compartment_1*k9b*species_10*species_8/parameter_9/(1+species_9/parameter_13+species_4/parameter_14+species_3/parameter_12+species_6/parameter_12+species_7/parameter_10+species_8/parameter_9)
k1=1.0; parameter_1 = 100.0Reaction: species_1 => species_2; species_11, species_1, species_11, Rate Law: compartment_1*k1*species_11*species_1/(parameter_1+species_1)
k3=0.01; parameter_4 = 500.0; parameter_3 = 50.5Reaction: species_3 => species_4; species_2, species_4, species_2, species_3, species_4, Rate Law: compartment_1*k3*species_2*species_3/parameter_3/(1+species_3/parameter_3+species_4/parameter_4)
parameter_11 = 3.0E51; parameter_6 = 108.6; parameter_5 = 24.3; parameter_2 = 54.3; k2a=0.086Reaction: species_2 => species_1; species_1, species_9, species_5, species_4, species_3, species_2, species_1, species_9, species_5, species_4, species_3, Rate Law: compartment_1*k2a*species_2*species_9/parameter_2/(1+species_2/parameter_2+species_1/parameter_11+species_5/parameter_5+species_4/parameter_6+species_3/parameter_11)
k7=15.0; parameter_7 = 50.5; parameter_8 = 500.0Reaction: species_7 => species_8; species_5, species_6, species_5, species_7, species_6, Rate Law: compartment_1*k7*species_5*species_7/parameter_8/(1+species_6/parameter_7+species_7/parameter_8)

States:

NameDescription
species 2[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 6[Mitogen-activated protein kinase 3]
species 3[Dual specificity mitogen-activated protein kinase kinase 1]
species 1[RAF proto-oncogene serine/threonine-protein kinase]
species 8[Mitogen-activated protein kinase 3; Phosphoprotein]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 7[Mitogen-activated protein kinase 3; Phosphoprotein]
species 5[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ): BIOMD0000000430v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ)The paper presents the various interaction topologies bet…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Parameters:

NameDescription
k1=0.02; k2=1.0Reaction: species_7 + species_8 => species_9; species_7, species_8, species_9, Rate Law: compartment_1*(k1*species_7*species_8-k2*species_9)
k1=1.0Reaction: species_17 => species_8 + species_18; species_17, Rate Law: compartment_1*k1*species_17
k1=15.0Reaction: species_5 => species_6 + species_2; species_5, Rate Law: compartment_1*k1*species_5
k1=0.092Reaction: species_12 => species_10 + species_13; species_12, Rate Law: compartment_1*k1*species_12
k1=0.032; k2=1.0Reaction: species_10 + species_8 => species_11; species_10, species_8, species_11, Rate Law: compartment_1*(k1*species_10*species_8-k2*species_11)
k1=0.01Reaction: species_3 => species_4 + species_2; species_3, Rate Law: compartment_1*k1*species_3
k1=0.086Reaction: species_22 => species_1 + species_13; species_22, Rate Law: compartment_1*k1*species_22
k1=0.0; k2=0.0Reaction: species_26 => species_7 + species_20; species_26, species_7, species_20, Rate Law: compartment_1*(k1*species_26-k2*species_7*species_20)
k1=0.01; k2=1.0Reaction: species_10 + species_20 => species_25; species_10, species_20, species_25, Rate Law: compartment_1*(k1*species_10*species_20-k2*species_25)
k1=0.045; k2=1.0Reaction: species_2 + species_20 => species_24; species_2, species_20, species_24, Rate Law: compartment_1*(k1*species_2*species_20-k2*species_24)

States:

NameDescription
species 9[Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 3; Phosphoprotein]
species 27[RAF proto-oncogene serine/threonine-protein kinase; Dual specificity protein phosphatase 3; Phosphoprotein]
species 1[Mitogen-activated protein kinase 3]
species 18[Ras-related protein Rap-2b]
species 20[Dual specificity protein phosphatase 3]
species 16[RAF proto-oncogene serine/threonine-protein kinase]
species 4[Mitogen-activated protein kinase 3; Phosphoprotein]
species 21[Mitogen-activated protein kinase 3; Dual specificity protein phosphatase 3; Phosphoprotein]
species 8[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 17[Ras-related protein Rap-2b; RAF proto-oncogene serine/threonine-protein kinase]
species 12[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 25[Dual specificity protein phosphatase 3; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 5[Mitogen-activated protein kinase 3; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 15[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3]
species 2[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 6[Mitogen-activated protein kinase 3; Phosphoprotein]
species 19[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 10[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 11[RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 24[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 14[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 22[Mitogen-activated protein kinase 3; Dual specificity protein phosphatase 3]
species 3[Mitogen-activated protein kinase 3; Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 23[Mitogen-activated protein kinase 3; Dual specificity protein phosphatase 3; Phosphoprotein]
species 7[Dual specificity mitogen-activated protein kinase kinase 1]
species 26[Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3; Phosphoprotein]
species 13[Dual specificity protein phosphatase 3]

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ_short_duration_signal): MODEL1204280028v0.0.1

Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ_short_duration_signal)The paper presents the various inte…

Details

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design. link: http://identifiers.org/pubmed/22748295

Sarma2012 - Oscillations in MAPK cascade (S1): BIOMD0000000440v0.0.1

Sarma2012 - Oscillations in MAPK cascade (S1)Two plausible designs (S1 and S2) of coupled positive and negative feedback…

Details

BACKGROUND: Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs. RESULTS: We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case. CONCLUSION: The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing. link: http://identifiers.org/pubmed/22694947

Parameters:

NameDescription
k9=0.1; K9=200.0Reaction: species_7 => species_6; species_10, species_6, species_5, species_10, species_6, species_7, Rate Law: compartment_0*k9*species_10*species_7/K9/(1+species_7/K9+species_6/K9)
K6=200.0; k6=0.1Reaction: species_3 => species_2; species_9, species_4, species_3, species_4, species_9, Rate Law: compartment_0*k6*species_9*species_3/K6/(1+species_4/K6+species_3/K6)
K5=200.0; k5=0.1Reaction: species_4 => species_3; species_9, species_3, species_3, species_4, species_9, Rate Law: compartment_0*k5*species_9*species_4/K5/(1+species_4/K5+species_3/K5)
A=10.0; k4=0.1; K4=20.0; Ka=500.0Reaction: species_3 => species_4; species_1, species_2, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k4*species_1*species_3/K4/(1+species_3/K4+species_2/K4)*(1+A*species_7/Ka)/(1+species_7/Ka)
K8=20.0; k8=0.1Reaction: species_6 => species_7; species_4, species_5, species_4, species_5, species_6, Rate Law: compartment_0*k8*species_4*species_6/K8/(1+species_5/K8+species_6/K8)
KI=9.0; K1=20.0; V1=2.5Reaction: species_0 => species_1; species_7, species_0, species_7, Rate Law: compartment_0*V1*species_0/K1/((1+species_0/K1)*(1+species_7/KI))
K2=200.0; k2=0.025Reaction: species_1 => species_0; species_8, species_1, species_8, Rate Law: compartment_0*k2*species_8*species_1/K2/(1+species_1/K2)
A=10.0; K3=20.0; Ka=500.0; k3=0.1Reaction: species_2 => species_3; species_1, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k3*species_1*species_2/K3/(1+species_2/K3+species_3/K3)*(1+A*species_7/Ka)/(1+species_7/Ka)
k10=0.1; K10=200.0Reaction: species_6 => species_5; species_10, species_7, species_5, species_10, species_6, species_7, Rate Law: compartment_0*k10*species_10*species_6/K10/(1+species_7/K10+species_6/K10)
K7=20.0; k7=0.1Reaction: species_5 => species_6; species_4, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k7*species_4*species_5/K7/(1+species_5/K7+species_6/K7)

States:

NameDescription
species 2[Dual specificity mitogen-activated protein kinase kinase 1]
species 6[Mitogen-activated protein kinase 1; Phosphoprotein]
species 3[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 0[RAF proto-oncogene serine/threonine-protein kinase]
species 1[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 7[Mitogen-activated protein kinase 1; Phosphoprotein]
species 5[Mitogen-activated protein kinase 1]

Sarma2012 - Oscillations in MAPK cascade (S1n): BIOMD0000000443v0.0.1

Sarma2012 - Oscillations in MAPK cascade (S1n)Two plausible designs (S1 and S2) of coupled positive and negative feedbac…

Details

BACKGROUND: Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs. RESULTS: We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case. CONCLUSION: The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing. link: http://identifiers.org/pubmed/22694947

Parameters:

NameDescription
k1=22.56; k2=15.4Reaction: species_14 => species_18; species_14, species_18, species_14, species_18, Rate Law: compartment_0*(k1*species_14-k2*species_18)
K22i=10300.0; K22=87.0; k22=0.31Reaction: species_17 => species_16; species_18, species_11, species_18, species_17, species_11, species_18, species_17, species_11, Rate Law: compartment_0*k22*species_18*species_17/K22/(1+species_17/K22+species_11/K22i)
K5=200.0; k5=0.1Reaction: species_4 => species_3; species_9, species_3, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k5*species_9*species_4/K5/(1+species_4/K5+species_3/K5)
A=10.0; k4=0.1; K4=20.0; Ka=500.0Reaction: species_3 => species_4; species_1, species_2, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k4*species_1*species_3/K4/(1+species_3/K4+species_2/K4)*(1+A*species_7/Ka)/(1+species_7/Ka)
K6=200.0; k6=0.1Reaction: species_3 => species_2; species_9, species_4, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k6*species_9*species_3/K6/(1+species_4/K6+species_3/K6)
KI=9.0; K1=20.0; V1=2.5Reaction: species_0 => species_1; species_7, species_0, species_7, species_0, species_7, Rate Law: compartment_0*V1*species_0/K1/((1+species_0/K1)*(1+species_7/KI))
K8=20.0; k8=0.1Reaction: species_6 => species_7; species_4, species_5, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k8*species_4*species_6/K8/(1+species_5/K8+species_6/K8)
k11b=2.86; k11f=10.34Reaction: species_7 => species_11; species_7, species_11, species_7, species_11, Rate Law: compartment_0*(k11f*species_7-k11b*species_11)
A=10.0; K3=20.0; Ka=500.0; k3=0.1Reaction: species_2 => species_3; species_1, species_3, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k3*species_1*species_2/K3/(1+species_2/K3+species_3/K3)*(1+A*species_7/Ka)/(1+species_7/Ka)
K21i=87.0; k21=0.68; K21=10300.0Reaction: species_11 => species_17; species_17, species_18, species_11, species_17, species_18, species_11, species_17, species_18, Rate Law: compartment_0*k21*species_18*species_11/K21/(1+species_11/K21+species_17/K21i)
V12=29.24; K12=169.0; n12=3.97Reaction: => species_12; species_11, species_11, species_11, Rate Law: compartment_0*V12*species_11^n12/(K12^n12+species_11^n12)
k9=0.1; K9=200.0Reaction: species_7 => species_6; species_10, species_6, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k9*species_10*species_7/K9/(1+species_7/K9+species_6/K9)
k1=0.022Reaction: species_12 => species_13; species_12, species_12, Rate Law: compartment_0*k1*species_12
k15=0.0012Reaction: => species_14; species_13, species_13, species_13, Rate Law: compartment_0*k15*species_13
k1=0.0078Reaction: species_13 => ; species_13, species_13, Rate Law: compartment_0*k1*species_13
K2=200.0; k2=0.025Reaction: species_1 => species_0; species_8, species_1, species_8, species_1, species_8, Rate Law: compartment_0*k2*species_8*species_1/K2/(1+species_1/K2)
k2=2.86; k1=10.34Reaction: species_5 => species_16; species_5, species_16, species_5, species_16, Rate Law: compartment_0*(k1*species_5-k2*species_16)
k1=2.5E-4Reaction: species_14 => ; species_14, species_14, Rate Law: compartment_0*k1*species_14
k10=0.1; K10=200.0Reaction: species_6 => species_5; species_10, species_7, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k10*species_10*species_6/K10/(1+species_7/K10+species_6/K10)
K7=20.0; k7=0.1Reaction: species_5 => species_6; species_4, species_6, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k7*species_4*species_5/K7/(1+species_5/K7+species_6/K7)

States:

NameDescription
species 2[Dual specificity mitogen-activated protein kinase kinase 1]
species 6[Mitogen-activated protein kinase 1; Phosphoprotein]
species 11[Mitogen-activated protein kinase 1; Phosphoprotein; nucleus]
species 1[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 18[Dual specificity protein phosphatase 3; nucleus]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 16[Mitogen-activated protein kinase 1; nucleus]
species 14[Dual specificity protein phosphatase 3; cytoplasm]
species 3[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 0[RAF proto-oncogene serine/threonine-protein kinase]
species 17[Mitogen-activated protein kinase 1; Phosphoprotein; nucleus]
species 12[Dual specificity protein phosphatase 3; nucleus]
species 7[Mitogen-activated protein kinase 1; Phosphoprotein]
species 5[Mitogen-activated protein kinase 1]
species 13[Dual specificity protein phosphatase 3; nucleus]

Sarma2012 - Oscillations in MAPK cascade (S2): BIOMD0000000441v0.0.1

Sarma2012 - Oscillations in MAPK cascade (S2)Two plausible designs (S1 and S2) of coupled positive and negative feedback…

Details

BACKGROUND: Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs. RESULTS: We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case. CONCLUSION: The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing. link: http://identifiers.org/pubmed/22694947

Parameters:

NameDescription
K2=100.0; k2=0.1Reaction: species_1 => species_0; species_8, species_1, species_8, species_1, species_8, Rate Law: compartment_0*k2*species_8*species_1/K2/(1+species_1/K2)
K10=20.0; k10=0.02Reaction: species_6 => species_5; species_10, species_7, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k10*species_10*species_6/K10/(1+species_7/K10+species_6/K10)
KI=9.0; k4=0.1; K4=20.0Reaction: species_3 => species_4; species_1, species_2, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k4*species_1*species_3/K4/((1+species_2/K4+species_3/K4)*(1+species_7/KI))
K5=20.0; k5=0.02Reaction: species_4 => species_3; species_9, species_3, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k5*species_9*species_4/K5/(1+species_4/K5+species_3/K5)
K8=20.0; k8=0.1Reaction: species_6 => species_7; species_4, species_5, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k8*species_4*species_6/K8/(1+species_5/K8+species_6/K8)
K9=20.0; k9=0.02Reaction: species_7 => species_6; species_10, species_6, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k9*species_10*species_7/K9/(1+species_7/K9+species_6/K9)
A=100.0; V1=6.0; Ka=500.0; K1=15.0Reaction: species_0 => species_1; species_7, species_0, species_7, species_0, species_7, Rate Law: compartment_0*V1*species_0/K1/(1+species_0/K1)*(1+A*species_7/Ka)/(1+species_7/Ka)
KI=9.0; K3=20.0; k3=0.1Reaction: species_2 => species_3; species_1, species_3, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k3*species_1*species_2/K3/((1+species_2/K3+species_3/K3)*(1+species_7/KI))
k6=0.02; K6=20.0Reaction: species_3 => species_2; species_9, species_4, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k6*species_9*species_3/K6/(1+species_4/K6+species_3/K6)
K7=20.0; k7=0.1Reaction: species_5 => species_6; species_4, species_6, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k7*species_4*species_5/K7/(1+species_5/K7+species_6/K7)

States:

NameDescription
species 2[Dual specificity mitogen-activated protein kinase kinase 1]
species 6[Mitogen-activated protein kinase 1; Phosphoprotein]
species 3[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 0[RAF proto-oncogene serine/threonine-protein kinase]
species 1[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 7[Mitogen-activated protein kinase 1; Phosphoprotein]
species 5[Mitogen-activated protein kinase 1]

Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling module: BIOMD0000000442v0.0.1

Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling moduleTwo plausible designs (S1 and S2)…

Details

BACKGROUND: Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs. RESULTS: We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case. CONCLUSION: The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing. link: http://identifiers.org/pubmed/22694947

Parameters:

NameDescription
K5=20.0; k5=0.02Reaction: species_4 => species_3; species_9, species_3, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k5*species_9*species_4/K5/(1+species_4/K5+species_3/K5)
K8=20.0; k8=0.1Reaction: species_6 => species_7; species_4, species_5, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k8*species_4*species_6/K8/(1+species_5/K8+species_6/K8)
K9=20.0; k9=0.02Reaction: species_7 => species_6; species_10, species_6, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k9*species_10*species_7/K9/(1+species_7/K9+species_6/K9)
A=100.0; V1=6.0; Ka=500.0; K1=15.0Reaction: species_0 => species_1; species_7, species_0, species_7, species_0, species_7, Rate Law: compartment_0*V1*species_0/K1/(1+species_0/K1)*(1+A*species_7/Ka)/(1+species_7/Ka)
k6=0.02; K6=20.0Reaction: species_3 => species_2; species_9, species_4, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k6*species_9*species_3/K6/(1+species_4/K6+species_3/K6)
K10=20.0; k10=0.02Reaction: species_6 => species_5; species_10, species_7, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k10*species_10*species_6/K10/(1+species_7/K10+species_6/K10)
V12=0.5; K12=50.0Reaction: species_12 => species_11; species_12, species_12, Rate Law: compartment_0*V12*species_12/(K12+species_12)
K2=100.0; k2=0.1Reaction: species_1 => species_0; species_8, species_1, species_8, species_1, species_8, Rate Law: compartment_0*k2*species_8*species_1/K2/(1+species_1/K2)
A=100.0; K11=50.0; V11=0.1; Ka=500.0Reaction: species_11 => species_12; species_7, species_11, species_7, species_11, species_7, Rate Law: compartment_0*V11*species_11/K11/(1+species_11/K11)*(1+A*species_7/Ka)/(1+species_7/Ka)
KI=9.0; k4=0.1; K4=20.0Reaction: species_3 => species_4; species_1, species_2, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k4*species_1*species_3/K4/((1+species_2/K4+species_3/K4)*(1+species_7/KI))
KI=9.0; K3=20.0; k3=0.1Reaction: species_2 => species_3; species_1, species_3, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k3*species_1*species_2/K3/((1+species_2/K3+species_3/K3)*(1+species_7/KI))
K7=20.0; k7=0.1Reaction: species_5 => species_6; species_4, species_6, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k7*species_4*species_5/K7/(1+species_5/K7+species_6/K7)

States:

NameDescription
species 2[Dual specificity mitogen-activated protein kinase kinase 1]
species 6[Mitogen-activated protein kinase 1; Phosphoprotein]
species 3[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 0[RAF proto-oncogene serine/threonine-protein kinase]
species 1[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 11[Mitogen-activated protein kinase 1]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 7[Mitogen-activated protein kinase 1; Phosphoprotein]
species 12[Mitogen-activated protein kinase 1; Phosphoprotein]
species 5[Mitogen-activated protein kinase 1]

Sarma2012 - Oscillations in MAPK cascade (S2n): BIOMD0000000444v0.0.1

Sarma2012 - Oscillations in MAPK cascade (S2n)Two plausible designs (S1 and S2) of coupled positive and negative feedbac…

Details

BACKGROUND: Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs. RESULTS: We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case. CONCLUSION: The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing. link: http://identifiers.org/pubmed/22694947

Parameters:

NameDescription
k1=22.56; k2=15.4Reaction: species_14 => species_15; species_14, species_15, species_14, species_15, Rate Law: compartment_0*(k1*species_14-k2*species_15)
K22i=10300.0; K22=87.0; k22=0.31Reaction: species_17 => species_16; species_15, species_11, species_15, species_17, species_11, species_15, species_17, species_11, Rate Law: compartment_0*k22*species_15*species_17/K22/(1+species_17/K22+species_11/K22i)
K5=20.0; k5=0.02Reaction: species_4 => species_3; species_9, species_3, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k5*species_9*species_4/K5/(1+species_4/K5+species_3/K5)
K8=20.0; k8=0.1Reaction: species_6 => species_7; species_4, species_5, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k8*species_4*species_6/K8/(1+species_5/K8+species_6/K8)
A=100.0; V1=6.0; Ka=500.0; K1=15.0Reaction: species_0 => species_1; species_7, species_0, species_7, species_0, species_7, Rate Law: compartment_0*V1*species_0/K1/(1+species_0/K1)*(1+A*species_7/Ka)/(1+species_7/Ka)
K9=20.0; k9=0.02Reaction: species_7 => species_6; species_10, species_6, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k9*species_10*species_7/K9/(1+species_7/K9+species_6/K9)
k11b=2.86; k11f=10.34Reaction: species_7 => species_11; species_7, species_11, species_7, species_11, Rate Law: compartment_0*(k11f*species_7-k11b*species_11)
K21i=87.0; k21=0.68; K21=10300.0Reaction: species_11 => species_17; species_15, species_17, species_15, species_11, species_17, species_15, species_11, species_17, Rate Law: compartment_0*k21*species_15*species_11/K21/(1+species_11/K21+species_17/K21i)
k6=0.02; K6=20.0Reaction: species_3 => species_2; species_9, species_4, species_3, species_4, species_9, species_3, species_4, species_9, Rate Law: compartment_0*k6*species_9*species_3/K6/(1+species_4/K6+species_3/K6)
K10=20.0; k10=0.02Reaction: species_6 => species_5; species_10, species_7, species_5, species_10, species_6, species_7, species_10, species_6, species_7, Rate Law: compartment_0*k10*species_10*species_6/K10/(1+species_7/K10+species_6/K10)
V12=29.24; K12=169.0; n12=3.97Reaction: => species_12; species_11, species_11, species_11, Rate Law: compartment_0*V12*species_11^n12/(K12^n12+species_11^n12)
K2=100.0; k2=0.1Reaction: species_1 => species_0; species_8, species_1, species_8, species_1, species_8, Rate Law: compartment_0*k2*species_8*species_1/K2/(1+species_1/K2)
k1=0.022Reaction: species_12 => species_13; species_12, species_12, Rate Law: compartment_0*k1*species_12
k15=0.0012Reaction: => species_14; species_13, species_13, species_13, Rate Law: compartment_0*k15*species_13
KI=9.0; k4=0.1; K4=20.0Reaction: species_3 => species_4; species_1, species_2, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k4*species_1*species_3/K4/((1+species_2/K4+species_3/K4)*(1+species_7/KI))
k1=0.0078Reaction: species_13 => ; species_13, species_13, Rate Law: compartment_0*k1*species_13
KI=9.0; K3=20.0; k3=0.1Reaction: species_2 => species_3; species_1, species_3, species_7, species_1, species_2, species_3, species_7, species_1, species_2, species_3, species_7, Rate Law: compartment_0*k3*species_1*species_2/K3/((1+species_2/K3+species_3/K3)*(1+species_7/KI))
k1=2.5E-4Reaction: species_15 => ; species_15, species_15, Rate Law: compartment_0*k1*species_15
k2=2.86; k1=10.34Reaction: species_5 => species_16; species_5, species_16, species_5, species_16, Rate Law: compartment_0*(k1*species_5-k2*species_16)
K7=20.0; k7=0.1Reaction: species_5 => species_6; species_4, species_6, species_4, species_5, species_6, species_4, species_5, species_6, Rate Law: compartment_0*k7*species_4*species_5/K7/(1+species_5/K7+species_6/K7)

States:

NameDescription
species 2[Dual specificity mitogen-activated protein kinase kinase 1]
species 6[Mitogen-activated protein kinase 1; Phosphoprotein]
species 11[Mitogen-activated protein kinase 1; Phosphoprotein; nucleus]
species 1[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein]
species 4[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 16[Mitogen-activated protein kinase 1; nucleus]
species 14[Dual specificity protein phosphatase 3; cytoplasm]
species 3[Dual specificity mitogen-activated protein kinase kinase 1; Phosphoprotein]
species 0[RAF proto-oncogene serine/threonine-protein kinase]
species 17[Mitogen-activated protein kinase 1; Phosphoprotein; nucleus]
species 12[Dual specificity protein phosphatase 3; nucleus]
species 7[Mitogen-activated protein kinase 1; Phosphoprotein]
species 5[Mitogen-activated protein kinase 1]
species 15[Dual specificity protein phosphatase 3; nucleus]
species 13[Dual specificity protein phosphatase 3; nucleus]

Sasagawa2005_MAPK: BIOMD0000000049v0.0.1

This a model from the article: Prediction and validation of the distinct dynamics of transient and sustained ERK acti…

Details

To elucidate the hidden dynamics of extracellular-signal-regulated kinase (ERK) signalling networks, we developed a simulation model of ERK signalling networks by constraining in silico dynamics based on in vivo dynamics in PC12 cells. We predicted and validated that transient ERK activation depends on rapid increases of epidermal growth factor and nerve growth factor (NGF) but not on their final concentrations, whereas sustained ERK activation depends on the final concentration of NGF but not on the temporal rate of increase. These ERK dynamics depend on Ras and Rap1 dynamics, the inactivation processes of which are growth-factor-dependent and -independent, respectively. Therefore, the Ras and Rap1 systems capture the temporal rate and concentration of growth factors, and encode these distinct physical properties into transient and sustained ERK activation, respectively. link: http://identifiers.org/pubmed/15793571

Parameters:

NameDescription
J82_k=0.1Reaction: Shc_pTrkA => pShc_pTrkA, Rate Law: c1*J82_k*Shc_pTrkA
J52_k1=60.0; J52_k2=0.5Reaction: c_Raf + Ras_GTP => c_Raf_Ras_GTP, Rate Law: c1*(J52_k1*c_Raf*Ras_GTP-J52_k2*c_Raf_Ras_GTP)
J133_k2=0.6; J133_k1=16.304Reaction: ERK + MEK => MEK_ERK, Rate Law: c1*(J133_k1*ERK*MEK-J133_k2*MEK_ERK)
J148_k1=9.375; J148_k2=1.2Reaction: B_Raf_Rap1_GTP + MEK => B_Raf_Rap1_GTP_MEK, Rate Law: c1*(J148_k1*B_Raf_Rap1_GTP*MEK-J148_k2*B_Raf_Rap1_GTP_MEK)
J95_k1=10.0; J95_k2=0.2Reaction: SOS_Grb2 + pShc_pTrkA_endo => Grb2_SOS_pShc_pTrkA_endo, Rate Law: c1*(J95_k1*SOS_Grb2*pShc_pTrkA_endo-J95_k2*Grb2_SOS_pShc_pTrkA_endo)
J47_k=0.001Reaction: pFRS2_dpEGFR_c_Cbl_ubiq => proteosome + c_Cbl + pFRS2, Rate Law: c1*J47_k*pFRS2_dpEGFR_c_Cbl_ubiq
J12_k2=0.2; J12_k1=10.0Reaction: L_dpEGFR + Shc => Shc_dpEGFR, Rate Law: c1*(J12_k1*L_dpEGFR*Shc-J12_k2*Shc_dpEGFR)
J11_k=0.002Reaction: pSOS_Grb2 => SOS_Grb2, Rate Law: c1*J11_k*pSOS_Grb2
J90_k=0.0022Reaction: pShc_pTrkA => degradation + pShc, Rate Law: c1*J90_k*pShc_pTrkA
J136_k=0.15Reaction: ppMEK_ERK => ppERK + ppMEK, Rate Law: c1*J136_k*ppMEK_ERK
J121_Vmax=10.0; J121_Km1=1.0Reaction: Ras_GTP => Ras_GDP; pDok_RasGAP, Rate Law: c1*J121_Vmax*Ras_GTP*pDok_RasGAP/(J121_Km1+Ras_GTP)
J25_k2=0.2; J25_k1=0.5Reaction: c_Cbl + Grb2_SOS_pShc_dpEGFR => Grb2_SOS_pShc_dpEGFR_c_Cbl, Rate Law: c1*(J25_k1*c_Cbl*Grb2_SOS_pShc_dpEGFR-J25_k2*Grb2_SOS_pShc_dpEGFR_c_Cbl)
J21_k=1.0Reaction: Shc_dpEGFR_c_Cbl => pShc_dpEGFR_c_Cbl, Rate Law: c1*J21_k*Shc_dpEGFR_c_Cbl
J163_k=0.3Reaction: B_Raf_Rap1_GTP_pMEK_ERK => B_Raf_Rap1_GTP + ppMEK_ERK, Rate Law: c1*J163_k*B_Raf_Rap1_GTP_pMEK_ERK
re8_k2=0.02; re8_k1=10.0Reaction: L_EGFR => L_EGFR_dimer, Rate Law: compartment*(re8_k1*L_EGFR*L_EGFR-re8_k2*L_EGFR_dimer)
J154_k=0.5Reaction: c_Raf_Ras_GTP_MEK_ERK => c_Raf_Ras_GTP + pMEK_ERK, Rate Law: c1*J154_k*c_Raf_Ras_GTP_MEK_ERK
J100_k=6.3E-4Reaction: FRS2_pTrkA => FRS2_pTrkA_endo, Rate Law: c1*J100_k*FRS2_pTrkA
J103_k1=1.0; J103_k2=0.2Reaction: Crk_C3G + pFRS2_pTrkA => Crk_C3G_pFRS2_pTrkA, Rate Law: c1*(J103_k1*Crk_C3G*pFRS2_pTrkA-J103_k2*Crk_C3G_pFRS2_pTrkA)
J3_k2=0.0168; J3_k1=0.03Reaction: SOS + Grb2 => SOS_Grb2, Rate Law: c1*(J3_k1*SOS*Grb2-J3_k2*SOS_Grb2)
J8_k2=1.0E-5; J8_k1=0.002Reaction: pDok => Dok, Rate Law: c1*(J8_k1*pDok-J8_k2*Dok)
J149_k2=1.2; J149_k1=9.375Reaction: B_Raf_Rap1_GTP + pMEK => B_Raf_Rap1_GTP_pMEK, Rate Law: c1*(J149_k1*B_Raf_Rap1_GTP*pMEK-J149_k2*B_Raf_Rap1_GTP_pMEK)
re1_k2=1.0E-4; re1_k1=1.0E-4Reaction: pro_EGFR => EGFR, Rate Law: compartment*(re1_k1*pro_EGFR-re1_k2*EGFR)
J107_k=0.0022Reaction: Crk_C3G_pFRS2_pTrkA => degradation + pFRS2 + Crk_C3G, Rate Law: c1*J107_k*Crk_C3G_pFRS2_pTrkA
J33_k=0.005Reaction: pFRS2 => FRS2, Rate Law: c1*J33_k*pFRS2
J38_k1=1.0; J38_k2=0.2Reaction: pFRS2_dpEGFR + Crk_C3G => Crk_C3G_pFRS2_dpEGFR, Rate Law: c1*(J38_k1*pFRS2_dpEGFR*Crk_C3G-J38_k2*Crk_C3G_pFRS2_dpEGFR)
J51_Km1=25.641; J51_Vmax=1.0Reaction: SOS => pSOS; dppERK, Rate Law: c1*J51_Vmax*SOS*dppERK/(J51_Km1+SOS)
J37_k=1.0Reaction: FRS2_dpEGFR => pFRS2_dpEGFR, Rate Law: c1*J37_k*FRS2_dpEGFR
J6_k2=0.2; J6_k1=0.5Reaction: L_dpEGFR + c_Cbl => dpEGFR_c_Cbl, Rate Law: c1*(J6_k1*L_dpEGFR*c_Cbl-J6_k2*dpEGFR_c_Cbl)
J23_k1=10.0; J23_k2=0.2Reaction: L_dpEGFR + Grb2_SOS_pShc => Grb2_SOS_pShc_dpEGFR, Rate Law: c1*(J23_k1*L_dpEGFR*Grb2_SOS_pShc-J23_k2*Grb2_SOS_pShc_dpEGFR)
J78_k1=5.0; J78_k2=0.1Reaction: pFRS2 + pTrkA => pFRS2_pTrkA, Rate Law: c1*(J78_k1*pFRS2*pTrkA-J78_k2*pFRS2_pTrkA)
J120_k1=1.0; J120_k2=0.2Reaction: dpEGFR_c_Cbl + pFRS2 => pFRS2_dpEGFR_c_Cbl, Rate Law: c1*(J120_k1*dpEGFR_c_Cbl*pFRS2-J120_k2*pFRS2_dpEGFR_c_Cbl)
J167_k=0.06Reaction: ppERK_MKP3 => ERK + MKP3, Rate Law: c1*J167_k*ppERK_MKP3
J162_k=0.3Reaction: B_Raf_Rap1_GTP_MEK_ERK => B_Raf_Rap1_GTP + pMEK_ERK, Rate Law: c1*J162_k*B_Raf_Rap1_GTP_MEK_ERK
J40_k1=0.5; J40_k2=0.2Reaction: c_Cbl + pFRS2_dpEGFR => pFRS2_dpEGFR_c_Cbl, Rate Law: c1*(J40_k1*c_Cbl*pFRS2_dpEGFR-J40_k2*pFRS2_dpEGFR_c_Cbl)
J10_k=0.002Reaction: pSOS => SOS, Rate Law: c1*J10_k*pSOS
J5_k1=4.0; J5_k2=0.001Reaction: L_EGFR_dimer => L_dpEGFR, Rate Law: compartment*(J5_k1*L_EGFR_dimer-J5_k2*L_dpEGFR)
J72_k=6.3E-4Reaction: pTrkA => pTrkA_endo, Rate Law: c1*J72_k*pTrkA
J143_k2=2.0; J143_k1=15.625Reaction: c_Raf_Ras_GTP + pMEK_ERK => c_Raf_Ras_GTP_pMEK_ERK, Rate Law: c1*(J143_k1*c_Raf_Ras_GTP*pMEK_ERK-J143_k2*c_Raf_Ras_GTP_pMEK_ERK)
J102_k=6.3E-4Reaction: Shc_pTrkA => Shc_pTrkA_endo, Rate Law: c1*J102_k*Shc_pTrkA
J112_k=4.2E-4Reaction: pFRS2_pTrkA_endo => degradation + pFRS2, Rate Law: J112_k*pFRS2_pTrkA_endo
J63_k1=10.0; J63_k2=0.075Reaction: ppERK => dppERK, Rate Law: c1*(J63_k1*ppERK*ppERK-J63_k2*dppERK)
J44_k1=1.0; J44_k2=0.2Reaction: pFRS2_dpEGFR_c_Cbl + Crk_C3G => Crk_C3G_pFRS2_dpEGFR_c_Cbl, Rate Law: c1*(J44_k1*pFRS2_dpEGFR_c_Cbl*Crk_C3G-J44_k2*Crk_C3G_pFRS2_dpEGFR_c_Cbl)
J165_k1=15.0; J165_k2=0.24Reaction: MKP3 + dppERK => dppERK_MKP3, Rate Law: c1*(J165_k1*MKP3*dppERK-J165_k2*dppERK_MKP3)
J77_k2=0.1; J77_k1=5.0Reaction: FRS2 + pTrkA => FRS2_pTrkA, Rate Law: c1*(J77_k1*FRS2*pTrkA-J77_k2*FRS2_pTrkA)
J81_k=0.1Reaction: Shc_pTrkA_endo => pShc_pTrkA_endo, Rate Law: c1*J81_k*Shc_pTrkA_endo
J108_k=4.2E-4Reaction: Crk_C3G_pFRS2_pTrkA_endo => degradation + Crk_C3G + pFRS2, Rate Law: c1*J108_k*Crk_C3G_pFRS2_pTrkA_endo
J71_k=1.0Reaction: L_NGFR => pTrkA, Rate Law: compartment*J71_k*L_NGFR
J164_k=0.001Reaction: Crk_C3G_pFRS2_dpEGFR_c_Cbl_ubiq => c_Cbl + pFRS2 + Crk_C3G, Rate Law: c1*J164_k*Crk_C3G_pFRS2_dpEGFR_c_Cbl_ubiq
J168_k=0.06Reaction: dppERK_MKP3 => ppERK + ERK + MKP3, Rate Law: c1*J168_k*dppERK_MKP3
J17_k=0.05Reaction: Shc_dpEGFR_c_Cbl => Shc_dpEGFR_c_Cbl_ubiq, Rate Law: c1*J17_k*Shc_dpEGFR_c_Cbl
J138_Vmax=10.0; J138_Km1=1.0Reaction: B_Raf_Ras_GTP => B_Raf + Ras_GDP; pDok_RasGAP, Rate Law: c1*J138_Vmax*B_Raf_Ras_GTP*pDok_RasGAP/(J138_Km1+B_Raf_Ras_GTP)
J135_k1=16.304; J135_k2=0.6Reaction: ERK + ppMEK => ppMEK_ERK, Rate Law: c1*(J135_k1*ERK*ppMEK-J135_k2*ppMEK_ERK)
J46_k=0.001Reaction: FRS2_dpEGFR_c_Cbl_ubiq => proteosome + c_Cbl + FRS2, Rate Law: c1*J46_k*FRS2_dpEGFR_c_Cbl_ubiq
J43_k=1.0Reaction: FRS2_dpEGFR_c_Cbl => pFRS2_dpEGFR_c_Cbl, Rate Law: c1*J43_k*FRS2_dpEGFR_c_Cbl
J160_k=0.3Reaction: B_Raf_Rap1_GTP_MEK => B_Raf_Rap1_GTP + pMEK, Rate Law: c1*J160_k*B_Raf_Rap1_GTP_MEK
J69_Km1=0.02; J69_Vmax=2.0Reaction: Ras_GDP => Ras_GTP; Grb2_SOS_pShc_dpEGFR_c_Cbl, Grb2_SOS_pShc_dpEGFR, Grb2_SOS_pShc_pTrkA, Rate Law: c1*J69_Vmax*Ras_GDP*(Grb2_SOS_pShc_dpEGFR+Grb2_SOS_pShc_dpEGFR_c_Cbl+Grb2_SOS_pShc_pTrkA)/(J69_Km1+Ras_GDP)
J99_k=6.3E-4Reaction: pFRS2_pTrkA => pFRS2_pTrkA_endo, Rate Law: c1*J99_k*pFRS2_pTrkA
re2_k2=0.0029666; re2_k1=2.2833Reaction: EGF + EGFR => L_EGFR, Rate Law: compartment*(re2_k1*EGF*EGFR-re2_k2*L_EGFR)
J24_k1=10.0; J24_k2=0.2Reaction: pShc_dpEGFR + SOS_Grb2 => Grb2_SOS_pShc_dpEGFR, Rate Law: c1*(J24_k1*pShc_dpEGFR*SOS_Grb2-J24_k2*Grb2_SOS_pShc_dpEGFR)
J50_Vmax=1.0; J50_Km1=25.641Reaction: SOS_Grb2 => pSOS_Grb2; dppERK, Rate Law: c1*J50_Vmax*SOS_Grb2*dppERK/(J50_Km1+SOS_Grb2)
J85_k2=0.1; J85_k1=5.0Reaction: pTrkA_endo + pFRS2 => pFRS2_pTrkA_endo, Rate Law: c1*(J85_k1*pTrkA_endo*pFRS2-J85_k2*pFRS2_pTrkA_endo)
J98_k=6.3E-4Reaction: Crk_C3G_pFRS2_pTrkA => Crk_C3G_pFRS2_pTrkA_endo, Rate Law: c1*J98_k*Crk_C3G_pFRS2_pTrkA
J16_k1=0.5; J16_k2=0.2Reaction: c_Cbl + Shc_dpEGFR => Shc_dpEGFR_c_Cbl, Rate Law: c1*(J16_k1*c_Cbl*Shc_dpEGFR-J16_k2*Shc_dpEGFR_c_Cbl)
J144_k1=6.25; J144_k2=0.8Reaction: B_Raf_Ras_GTP + MEK => B_Raf_Ras_GTP_MEK, Rate Law: c1*(J144_k1*B_Raf_Ras_GTP*MEK-J144_k2*B_Raf_Ras_GTP_MEK)
J86_k=2.0Reaction: FRS2_pTrkA_endo => pFRS2_pTrkA_endo, Rate Law: c1*J86_k*FRS2_pTrkA_endo
J4_k2=0.0168; J4_k1=0.03Reaction: Grb2 + pSOS => pSOS_Grb2, Rate Law: c1*(J4_k1*Grb2*pSOS-J4_k2*pSOS_Grb2)
J49_k2=0.01; J49_k1=0.12Reaction: pDok + RasGAP => pDok_RasGAP, Rate Law: c1*(J49_k1*pDok*RasGAP-J49_k2*pDok_RasGAP)
J155_k=0.5Reaction: c_Raf_Ras_GTP_pMEK_ERK => c_Raf_Ras_GTP + ppMEK_ERK, Rate Law: c1*J155_k*c_Raf_Ras_GTP_pMEK_ERK
J145_k1=6.25; J145_k2=0.8Reaction: B_Raf_Ras_GTP + pMEK => B_Raf_Ras_GTP_pMEK, Rate Law: c1*(J145_k1*B_Raf_Ras_GTP*pMEK-J145_k2*B_Raf_Ras_GTP_pMEK)
J76_k2=0.2; J76_k1=10.0Reaction: pShc + pTrkA => pShc_pTrkA, Rate Law: c1*(J76_k1*pShc*pTrkA-J76_k2*pShc_pTrkA)
J115_k1=10.0; J115_k2=0.2Reaction: Shc + dpEGFR_c_Cbl => Shc_dpEGFR_c_Cbl, Rate Law: c1*(J115_k1*Shc*dpEGFR_c_Cbl-J115_k2*Shc_dpEGFR_c_Cbl)
J156_k=0.2Reaction: B_Raf_Ras_GTP_MEK => B_Raf_Ras_GTP + pMEK, Rate Law: c1*J156_k*B_Raf_Ras_GTP_MEK
J117_k2=0.2; J117_k1=10.0Reaction: pShc_dpEGFR_c_Cbl + SOS_Grb2 => Grb2_SOS_pShc_dpEGFR_c_Cbl, Rate Law: c1*(J117_k1*pShc_dpEGFR_c_Cbl*SOS_Grb2-J117_k2*Grb2_SOS_pShc_dpEGFR_c_Cbl)
J36_k1=1.0; J36_k2=0.2Reaction: L_dpEGFR + pFRS2 => pFRS2_dpEGFR, Rate Law: c1*(J36_k1*L_dpEGFR*pFRS2-J36_k2*pFRS2_dpEGFR)
J75_k2=0.2; J75_k1=10.0Reaction: Shc + pTrkA => Shc_pTrkA, Rate Law: c1*(J75_k1*Shc*pTrkA-J75_k2*Shc_pTrkA)
J118_k1=1.0; J118_k2=0.2Reaction: dpEGFR_c_Cbl + FRS2 => FRS2_dpEGFR_c_Cbl, Rate Law: c1*(J118_k1*dpEGFR_c_Cbl*FRS2-J118_k2*FRS2_dpEGFR_c_Cbl)
J41_k=0.05Reaction: pFRS2_dpEGFR_c_Cbl => pFRS2_dpEGFR_c_Cbl_ubiq, Rate Law: c1*J41_k*pFRS2_dpEGFR_c_Cbl
J7_k2=0.2; J7_k1=10.0Reaction: L_dpEGFR + pShc => pShc_dpEGFR, Rate Law: c1*(J7_k1*L_dpEGFR*pShc-J7_k2*pShc_dpEGFR)
J66_k=1.667E-4Reaction: Ras_GTP => Ras_GDP, Rate Law: c1*J66_k*Ras_GTP

States:

NameDescription
L EGFR dimer[Pro-epidermal growth factor; Receptor protein-tyrosine kinase]
RasGAP[IPR011575]
EGFR[Receptor protein-tyrosine kinase]
pFRS2[Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b; Phosphoprotein]
pSOS[Son of sevenless 1]
Grb2 SOS pShc dpEGFR[Receptor protein-tyrosine kinase; Son of sevenless 1; SHC-transforming protein 1; Growth factor receptor-bound protein 2]
Shc dpEGFR[SHC-transforming protein 1; Pro-epidermal growth factor; Receptor protein-tyrosine kinase]
Shc dpEGFR c Cbl[SHC-transforming protein 1; E3 ubiquitin-protein ligase CBL-B; Receptor protein-tyrosine kinase]
pFRS2 dpEGFR c Cbl[Receptor protein-tyrosine kinase; Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b; E3 ubiquitin-protein ligase CBL-B]
c Cbl[E3 ubiquitin-protein ligase CBL-B]
L NGFR[High affinity nerve growth factor receptor; Beta-nerve growth factor]
ppMEK ERK[Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
SOS[Son of sevenless 1]
Crk C3G pFRS2 pTrkA endo[High affinity nerve growth factor receptor; C3G protein; Adapter molecule crk; Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b]
Crk C3G[Adapter molecule crk; C3G protein]
c Raf Ras GTP pMEK ERK[Mitogen-activated protein kinase 1; Dual specificity mitogen-activated protein kinase kinase 1; RAF proto-oncogene serine/threonine-protein kinase; IPR003577]
pFRS2 dpEGFR c Cbl ubiq[Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b; E3 ubiquitin-protein ligase CBL-B; Receptor protein-tyrosine kinase]
B Raf Rap1 GTP MEK[GTP; Dual specificity mitogen-activated protein kinase kinase 1; V-raf murine sarcoma viral oncogene B1-like protein; Ras-related protein Rap-1b]
Ras GDP[GDP; IPR003577]
pTrkA[High affinity nerve growth factor receptor]
SOS Grb2[Son of sevenless 1; Growth factor receptor-bound protein 2]
Dok[Docking protein 1; IPR002404]
FRS2 dpEGFR c Cbl[Receptor protein-tyrosine kinase; Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b; E3 ubiquitin-protein ligase CBL-B]
B Raf Ras GTP pMEK[Dual specificity mitogen-activated protein kinase kinase 1; V-raf murine sarcoma viral oncogene B1-like protein; IPR003577]
L dpEGFR[Receptor protein-tyrosine kinase; Pro-epidermal growth factor]
B Raf Rap1 GTP[GTP; Ras-related protein Rap-1b; V-raf murine sarcoma viral oncogene B1-like protein]
pShc pTrkA[High affinity nerve growth factor receptor; SHC-transforming protein 1]
Shc pTrkA endo[High affinity nerve growth factor receptor; SHC-transforming protein 1]
B Raf[V-raf murine sarcoma viral oncogene B1-like protein]
L EGFR[Pro-epidermal growth factor; Receptor protein-tyrosine kinase]
c Raf Ras GTP MEK ERK[Mitogen-activated protein kinase 1; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; IPR003577]
pMEK[Dual specificity mitogen-activated protein kinase kinase 1]
Shc pTrkA[High affinity nerve growth factor receptor; SHC-transforming protein 1]
pShc dpEGFR c Cbl[E3 ubiquitin-protein ligase CBL-B; SHC-transforming protein 1; Receptor protein-tyrosine kinase]
pFRS2 pTrkA endo[High affinity nerve growth factor receptor; Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b]
FRS2 pTrkA endo[Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b; High affinity nerve growth factor receptor]
Grb2[Growth factor receptor-bound protein 2]
ppERK[Mitogen-activated protein kinase 1]
B Raf Rap1 GTP pMEK[GTP; Dual specificity mitogen-activated protein kinase kinase 1; Ras-related protein Rap-1b; V-raf murine sarcoma viral oncogene B1-like protein]
B Raf Ras GTP MEK[Dual specificity mitogen-activated protein kinase kinase 1; V-raf murine sarcoma viral oncogene B1-like protein; IPR003577]
FRS2 dpEGFR[Receptor protein-tyrosine kinase; Fibroblast growth factor receptor substrate 2Fibroblast growth factor receptor substrate 2 (Predicted), isoform CRA_b]
dppERK[Mitogen-activated protein kinase 1]
ERK[Mitogen-activated protein kinase 1]
c Raf[RAF proto-oncogene serine/threonine-protein kinase]

Sass2009 - Approach to an α-synuclein-based BST model of Parkinson's disease: BIOMD0000000575v0.0.1

Sass2009 - Approach to an α-synuclein-based BST model of Parkinson's diseaseThis model is described in the article: [A…

Details

This paper presents a detailed systems model of Parkinson's disease (PD), developed utilizing a pragmatic application of biochemical systems theory (BST) intended to assist experimentalists in the study of system behavior. This approach utilizes relative values as a reasonable initial estimate for BST and provides a theoretical means of applying numerical solutions to qualitative and semi-quantitative understandings of cellular pathways and mechanisms. The approach allows for the simulation of human disease through its ability to organize and integrate existing information about metabolic pathways without having a full quantitative description of those pathways, so that hypotheses about individual processes may be tested in a systems environment. Incorporating this method, the PD model describes alpha-synuclein aggregation as mediated by dopamine metabolism, the ubiquitin-proteasome system, and lysosomal degradation, allowing for the examination of dynamic pathway interactions and the evaluation of possible toxic mechanisms in the aggregation process. Four system perturbations: elevated alpha-synuclein aggregation, impaired dopamine packaging, increased neurotoxins, and alpha-synuclein overexpression, were analyzed for correlation to qualitative PD system hypotheses present in the literature, with the model demonstrating a high level of agreement with these hypotheses. Additionally, various PD treatment methods, including levadopa and monoamine oxidase inhibition (MAOI) therapy, were applied to the disease models to examine their effects on the system. Future additions and refinements to the model may further the understanding of the emergent behaviors of the disease, helping in the identification of system sensitivities and possible therapeutic targets. link: http://identifiers.org/pubmed/19136028

Parameters:

NameDescription
g3679 = 1.0; k36 = 0.05; g3677 = 1.0Reaction: Autophagosome_0 => Fragments; Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Autophagosome_0, Lysosome_0, Rate Law: k36*Autophagosome_0^g3679*Lysosome_0^g3677
g156 = 1.0; k15 = 0.2; g1545 = 1.0; g1544 = 1.0Reaction: Dopamine + Vesicle_0 => V_DA; VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Dopamine, Vesicle_0, VMAT2, Rate Law: k15*Dopamine^g156*Vesicle_0^g1544*VMAT2^g1545
k27f = 0.05; g27f15 = 1.0; g27f68 = 1.0; g27f16 = 1.0Reaction: Ub_E1 + UbcH8ub2 => E1 + UbcH8ub3; ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Ub_E1, UbcH8ub2, ATP, Rate Law: Neuronal_cytosol*k27f*Ub_E1^g27f16*UbcH8ub2^g27f68*ATP^g27f15
g3830 = 1.0; g3815 = 1.0; g3812 = 1.0; g3878 = 1.0; k38 = 0.7Reaction: UCH_L1_asyn_ub4 => Fragments + UCH_L1 + Ubiquitin; Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, UCH_L1_asyn_ub4, Proteasome_0, ATP, UCH_L1, Rate Law: Neuronal_cytosol*k38*UCH_L1_asyn_ub4^g3878*Proteasome_0^g3812*ATP^g3815*UCH_L1^g3830
k24 = 1.0; g2463 = 1.0; g2464 = 1.0Reaction: GSSG => GSH; Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, GSSG, Gluta_red, Rate Law: Neuronal_cytosol*k24*GSSG^g2463*Gluta_red^g2464
k37 = 0.05; g3773 = 1.0; g3770 = 1.0Reaction: UbcH8ub4 + asyn_UCH_L1 => UCH_L1_asyn_ub4 + UbcH8; UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, UbcH8ub4, asyn_UCH_L1, Rate Law: Neuronal_cytosol*k37*UbcH8ub4^g3770*asyn_UCH_L1^g3773
k57 = 0.005; g5762 = 1.0; g5710 = 1.0Reaction: DA_quinone + GSH => DA_GSH; DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, DA_quinone, GSH, Rate Law: Neuronal_cytosol*k57*DA_quinone^g5710*GSH^g5762
g4782 = 1.0; k47 = 0.03; g4777 = 1.0Reaction: Hsc70_Protofibril => Hsc70 + Fragments; Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Hsc70_Protofibril, Lysosome_0, Rate Law: Neuronal_cytosol*k47*Hsc70_Protofibril^g4782*Lysosome_0^g4777
k116 = 0.5; g11642 = 1.0; g116118 = 1.0Reaction: Neuromelanin + Neurotoxins => Neuromelanin_ntox_Fe3; Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Neuromelanin, Neurotoxins, Rate Law: Neuronal_cytosol*k116*Neuromelanin^g116118*Neurotoxins^g11642
g5280 = 1.0; g523 = 1.0; k52 = 0.05Reaction: Fibril + Preautophagosome_membrane => Autophagosome_0; Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Fibril, Preautophagosome_membrane, Rate Law: k52*Fibril^g523*Preautophagosome_membrane^g5280
k2 = 0.01; g22 = 1.0Reaction: Protofibril => Fibril; Protofibril, Protofibril, Protofibril, Protofibril, Protofibril, Protofibril, Protofibril, Protofibril, Protofibril, Rate Law: Neuronal_cytosol*k2*Protofibril^g22
g10051 = 1.0; g100115 = 1.0; g10037 = 1.0; k100 = 0.005Reaction: L_Dopa + O2_0 + Cysteine => Neuromelanin + H2O2 + CO2; L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, L_Dopa, O2_0, Cysteine, Rate Law: Neuronal_cytosol*k100*L_Dopa^g10037*O2_0^g10051*Cysteine^g100115
g919 = 1.0; g920 = 1.0; k9 = 0.001Reaction: Parkin + Synphilin_1 => Parkin_synphilin_1; Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Parkin, Synphilin_1, Rate Law: Neuronal_cytosol*k9*Parkin^g919*Synphilin_1^g920
k26f = 0.05; g26f18 = 1.0; g26f16 = 1.0; g26f15 = 1.0Reaction: Ub_E1 + UbcH8_Ub => E1 + UbcH8ub2; ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Ub_E1, UbcH8_Ub, ATP, Rate Law: Neuronal_cytosol*k26f*Ub_E1^g26f16*UbcH8_Ub^g26f18*ATP^g26f15
g3173 = 1.0; k31 = 0.05; g3172 = 1.0Reaction: UbcH13_Uev1a_ub + asyn_UCH_L1 => UbcH13_Uev1a + UCH_L1 + asyn_ub; UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, UbcH13_Uev1a_ub, asyn_UCH_L1, Rate Law: Neuronal_cytosol*k31*UbcH13_Uev1a_ub^g3172*asyn_UCH_L1^g3173
g5687 = 1.0; g5686 = 1.0; k56 = 0.05Reaction: O2 => H2O2 + O2_0; SOD, O2, SOD, O2, SOD, O2, SOD, O2, SOD, O2, SOD, O2, SOD, O2, SOD, O2, SOD, O2, SOD, Rate Law: Neuronal_cytosol*k56*O2^g5686*SOD^g5687
g27r30 = 1.0; k27r = 0.005; g27r69 = 1.0Reaction: UbcH8ub3 => UbcH8 + Ubiquitin; UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, UbcH8ub3, UCH_L1, Rate Law: Neuronal_cytosol*k27r*UbcH8ub3^g27r69*UCH_L1^g27r30
g26r68 = 1.0; g26r30 = 1.0; k26r = 0.005Reaction: UbcH8ub2 => UbcH8 + Ubiquitin; UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, UbcH8ub2, UCH_L1, Rate Law: Neuronal_cytosol*k26r*UbcH8ub2^g26r68*UCH_L1^g26r30
k11 = 0.05; g1124 = 1.0; g1170 = 1.0Reaction: Parkin_sub + UbcH8ub4 => Parkin_sub_ub4 + UbcH8; Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Parkin_sub, UbcH8ub4, Rate Law: Neuronal_cytosol*k11*Parkin_sub^g1124*UbcH8ub4^g1170
k1 = 0.03; g11 = 1.0Reaction: Alpha_synuclein => Protofibril; Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Alpha_synuclein, Rate Law: Neuronal_cytosol*k1*Alpha_synuclein^g11
k3 = 0.007; g23 = 1.0; g326 = 1.0Reaction: Fibril + Parkin_synphilin_1_ub => Lewy_body; Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Fibril, Parkin_synphilin_1_ub, Rate Law: Neuronal_cytosol*k3*Fibril^g23*Parkin_synphilin_1_ub^g326
g352 = 1.0; k35 = 0.001; g3576 = 1.0Reaction: Protofibril + Protofibril_Ub => Fibril; Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Protofibril, Protofibril_Ub, Rate Law: Neuronal_cytosol*k35*Protofibril^g352*Protofibril_Ub^g3576
g1744 = 1.0; g1742 = 1.0; k17 = 1.0E-4Reaction: Neurotoxins + Vesicle_0 => V_ntox_ba; Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Neurotoxins, Vesicle_0, Rate Law: k17*Neurotoxins^g1742*Vesicle_0^g1744
k43 = 0.05; g431 = 1.0; g4384 = 1.0Reaction: Alpha_synuclein + Hsc70 => Hsc70_asyn; Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Alpha_synuclein, Hsc70, Rate Law: Neuronal_cytosol*k43*Alpha_synuclein^g431*Hsc70^g4384
g717 = 1.0; g716 = 1.0; k7 = 0.03; g715 = 1.0Reaction: Ub_E1 + UbcH8 => E1 + UbcH8_Ub; ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Ub_E1, UbcH8, ATP, Rate Law: Neuronal_cytosol*k7*Ub_E1^g716*UbcH8^g717*ATP^g715
g2259 = 1.0; g229 = 1.0; k22 = 0.5Reaction: H2O2 => H2O + O2_0; Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, H2O2, Catalase, Rate Law: Neuronal_cytosol*k22*H2O2^g229*Catalase^g2259
k13 = 0.1; g1336 = 1.0; g1335 = 1.0; g1351 = 1.0Reaction: L_Tyr + O2_0 => L_Dopa; TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, L_Tyr, O2_0, TH, Rate Law: Neuronal_cytosol*k13*L_Tyr^g1336*O2_0^g1351*TH^g1335
k14 = 3.0; g1467 = 1.0; g1437 = 1.0Reaction: L_Dopa => Dopamine + CO2; DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, L_Dopa, DDC, Rate Law: Neuronal_cytosol*k14*L_Dopa^g1437*DDC^g1467
g301 = 1.0; g3030 = 1.0; k30 = 0.001Reaction: Alpha_synuclein + UCH_L1 => asyn_UCH_L1; Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Alpha_synuclein, UCH_L1, Rate Law: Neuronal_cytosol*k30*Alpha_synuclein^g301*UCH_L1^g3030
k34 = 0.05; g3472 = 1.0; g3475 = 1.0Reaction: UbcH13_Uev1a_ub + Protofibril_UCH_L1 => UbcH13_Uev1a + UCH_L1 + Protofibril_Ub; UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, UbcH13_Uev1a_ub, Protofibril_UCH_L1, Rate Law: Neuronal_cytosol*k34*UbcH13_Uev1a_ub^g3472*Protofibril_UCH_L1^g3475
g2065 = 1.0; k20 = 0.1; g209 = 1.0Reaction: H2O2 + Fe2 => Fe3 + OH_radical + OH; H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, H2O2, Fe2, Rate Law: Neuronal_cytosol*k20*H2O2^g209*Fe2^g2065
g4677 = 1.0; g4681 = 1.0; k46 = 0.03Reaction: Hsc70_asyn => Hsc70 + Fragments; Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Hsc70_asyn, Lysosome_0, Rate Law: Neuronal_cytosol*k46*Hsc70_asyn^g4681*Lysosome_0^g4677
g821 = 1.0; k8 = 0.001; g819 = 1.0Reaction: Parkin + Substrate => Parkin_sub; Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Parkin, Substrate, Rate Law: Neuronal_cytosol*k8*Parkin^g819*Substrate^g821
k54 = 0.005; g5410 = 1.0; g5419 = 1.0Reaction: DA_quinone + Parkin => DA_S_parkin; DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, DA_quinone, Parkin, Rate Law: Neuronal_cytosol*k54*DA_quinone^g5410*Parkin^g5419
g1960 = 1.0; g196 = 1.0; k19 = 0.01; g1953 = 1.0; g1951 = 1.0Reaction: Dopamine + O2_0 + H2O => NH3 + DOPAL + H2O2; MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Dopamine, O2_0, H2O, MAO, Rate Law: Neuronal_cytosol*k19*Dopamine^g196*O2_0^g1951*H2O^g1960*MAO^g1953
k28f = 0.05; g28f16 = 1.0; g28f69 = 1.0; g28f15 = 1.0Reaction: Ub_E1 + UbcH8ub3 => E1 + UbcH8ub4; ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Ub_E1, UbcH8ub3, ATP, Rate Law: Neuronal_cytosol*k28f*Ub_E1^g28f16*UbcH8ub3^g28f69*ATP^g28f15
k101 = 0.005; g101115 = 1.0; g10151 = 1.0; g10136 = 1.0Reaction: L_Tyr + O2_0 + Cysteine => Neuromelanin + H2O2 + CO2; L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, L_Tyr, O2_0, Cysteine, Rate Law: Neuronal_cytosol*k101*L_Tyr^g10136*O2_0^g10151*Cysteine^g101115
g2915 = 1.0; g2971 = 1.0; g2916 = 1.0; k29 = 0.05Reaction: Ub_E1 + UbcH13_Uev1a => E1 + UbcH13_Uev1a_ub; ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Ub_E1, UbcH13_Uev1a, ATP, Rate Law: Neuronal_cytosol*k29*Ub_E1^g2916*UbcH13_Uev1a^g2971*ATP^g2915
g115118 = 1.0; g11565 = 1.0; k115 = 0.5Reaction: Fe3 + Neuromelanin => Neuromelanin_ntox_Fe3; Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Fe3, Neuromelanin, Rate Law: Neuronal_cytosol*k115*Fe3^g11565*Neuromelanin^g115118
k21 = 0.1; g2166 = 1.0Reaction: Fe3 => Fe2; Fe3, Fe3, Fe3, Fe3, Fe3, Fe3, Fe3, Fe3, Fe3, Rate Law: Neuronal_cytosol*k21*Fe3^g2166
g615 = 1.0; g613 = 1.0; k6 = 0.5; g614 = 1.0Reaction: Ubiquitin + E1 => Ub_E1; ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Ubiquitin, E1, ATP, Rate Law: Neuronal_cytosol*k6*Ubiquitin^g613*E1^g614*ATP^g615
k10 = 0.05; g1072 = 1.0; g1025 = 1.0Reaction: Parkin_synphilin_1 + UbcH13_Uev1a_ub => Parkin_synphilin_1_ub + UbcH13_Uev1a; Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Parkin_synphilin_1, UbcH13_Uev1a_ub, Rate Law: Neuronal_cytosol*k10*Parkin_synphilin_1^g1025*UbcH13_Uev1a_ub^g1072
g4584 = 1.0; k45 = 0.04; g453 = 1.0Reaction: Fibril + Hsc70 => Hsc70_fibril; Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Fibril, Hsc70, Rate Law: Neuronal_cytosol*k45*Fibril^g453*Hsc70^g4584
g412 = 1.0; k4 = 0.9; g427 = 1.0; g430 = 1.0; g415 = 1.0Reaction: Parkin_sub_ub4 => Parkin + Fragments + Ubiquitin; Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Parkin_sub_ub4, Proteasome_0, ATP, UCH_L1, Rate Law: Neuronal_cytosol*k4*Parkin_sub_ub4^g427*Proteasome_0^g412*ATP^g415*UCH_L1^g430
k32 = 0.001; g321 = 1.0; g3274 = 1.0Reaction: Alpha_synuclein + asyn_ub => Protofibril; Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Alpha_synuclein, asyn_ub, Rate Law: Neuronal_cytosol*k32*Alpha_synuclein^g321*asyn_ub^g3274
g5380 = 1.0; g534 = 1.0; k53 = 0.05Reaction: Lewy_body + Preautophagosome_membrane => Autophagosome_0; Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Lewy_body, Preautophagosome_membrane, Rate Law: k53*Lewy_body^g534*Preautophagosome_membrane^g5380
g556 = 1.0; g5586 = 1.0; k55 = 0.05Reaction: Dopamine + O2 => H2O2 + DA_quinone; Dopamine, O2, Dopamine, O2, Dopamine, O2, Dopamine, O2, Dopamine, O2, Dopamine, O2, Dopamine, O2, Dopamine, O2, Dopamine, O2, Rate Law: Neuronal_cytosol*k55*Dopamine^g556*O2^g5586
g3330 = 1.0; g332 = 1.0; k33 = 0.001Reaction: Protofibril + UCH_L1 => Protofibril_UCH_L1; Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Protofibril, UCH_L1, Rate Law: Neuronal_cytosol*k33*Protofibril^g332*UCH_L1^g3330
k48 = 0.03; g4883 = 1.0; g4877 = 1.0Reaction: Hsc70_fibril => Hsc70 + Fragments; Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Hsc70_fibril, Lysosome_0, Rate Law: Neuronal_cytosol*k48*Hsc70_fibril^g4883*Lysosome_0^g4877
g10251 = 1.0; g10210 = 1.0; k102 = 0.005; g102115 = 1.0Reaction: DA_quinone + O2_0 + Cysteine => Neuromelanin + CO2; DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, DA_quinone, O2_0, Cysteine, Rate Law: Neuronal_cytosol*k102*DA_quinone^g10210*O2_0^g10251*Cysteine^g102115
k16 = 1.0E-4; g1644 = 1.0; g1643 = 1.0Reaction: Bioamines + Vesicle_0 => V_ntox_ba; Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Bioamines, Vesicle_0, Rate Law: k16*Bioamines^g1643*Vesicle_0^g1644
k18 = 0.02; g186 = 1.0; g1851 = 1.0Reaction: Dopamine + O2_0 => DA_quinone + O2; Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Dopamine, O2_0, Rate Law: Neuronal_cytosol*k18*Dopamine^g186*O2_0^g1851
g2556 = 0.25; g2552 = 1.0; g2555 = 0.3; k25 = 0.05Reaction: DOPAL + NAD => DOPAC + NADH; ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, DOPAL, NAD, ALDH, Rate Law: Neuronal_cytosol*k25*DOPAL^g2552*NAD^g2556*ALDH^g2555
k51 = 0.05; g512 = 1.0; g5180 = 1.0Reaction: Protofibril + Preautophagosome_membrane => Autophagosome_0; Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Protofibril, Preautophagosome_membrane, Rate Law: k51*Protofibril^g512*Preautophagosome_membrane^g5180
g501 = 1.0; g5080 = 1.0; k50 = 0.05Reaction: Alpha_synuclein + Preautophagosome_membrane => Autophagosome_0; Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Alpha_synuclein, Preautophagosome_membrane, Rate Law: k50*Alpha_synuclein^g501*Preautophagosome_membrane^g5080
g2361 = 1.0; k23 = 0.5; g239 = 1.0; g2362 = 1.0Reaction: H2O2 + GSH => H2O + GSSG; Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, H2O2, GSH, Gluta_per, Rate Law: Neuronal_cytosol*k23*H2O2^g239*GSH^g2362*Gluta_per^g2361
g4484 = 1.0; k44 = 0.045; g442 = 1.0Reaction: Protofibril + Hsc70 => Hsc70_Protofibril; Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Protofibril, Hsc70, Rate Law: Neuronal_cytosol*k44*Protofibril^g442*Hsc70^g4484

States:

NameDescription
Neuromelanin[5,6-dihydroxyindole; polymer]
Substrate[SBO:0000015]
UbcH13 Uev1a[Ubiquitin-conjugating enzyme E2 N; Ubiquitin-conjugating enzyme E2 variant 1]
UCH L1 asyn ub4[Polyubiquitin-B; Ubiquitin carboxyl-terminal hydrolase isozyme L1; Alpha-synuclein]
Neurotoxins[neurotoxin]
asyn ub[Polyubiquitin-B; Alpha-synuclein]
Fragments[inactive; peptide]
Dopamine[dopamine]
Ub E1[Polyubiquitin-B; Ubiquitin-like modifier-activating enzyme 1]
UbcH8ub3[Polyubiquitin-B; Ubiquitin/ISG15-conjugating enzyme E2 L6]
Lewy body[Alpha-synuclein; Lewy body]
UbcH8[Ubiquitin/ISG15-conjugating enzyme E2 L6]
DOPAC[(3,4-dihydroxyphenyl)acetic acid]
E1[Ubiquitin-like modifier-activating enzyme 1]
Protofibril Ub[Polyubiquitin-B; Alpha-synuclein]
Ubiquitin[Polyubiquitin-B]
UbcH8ub2[Polyubiquitin-B; Ubiquitin/ISG15-conjugating enzyme E2 L6]
DOPAL[3,4-dihydroxyphenylacetaldehyde]
GSH[glutathione]
Hsc70 asyn[Alpha-synuclein; Heat shock cognate 71 kDa protein]
Parkin sub[E3 ubiquitin-protein ligase parkin; SBO:0000015]
Bioamines[amine; biological role]
DA quinone[dopamine; quinone]
Parkin synphilin 1[E3 ubiquitin-protein ligase parkin; Synphilin-1]
Protofibril[Alpha-synuclein]
UbcH8ub4[Polyubiquitin-B; Ubiquitin/ISG15-conjugating enzyme E2 L6]
UCH L1[Ubiquitin carboxyl-terminal hydrolase isozyme L1]
OH radical[hydroxyl]
DA GSH[dopamine; glutathione]
UbcH8 Ub[Polyubiquitin-B; Ubiquitin/ISG15-conjugating enzyme E2 L6]
Alpha synuclein[Alpha-synuclein]
Neuromelanin ntox Fe3[polymer; 5,6-dihydroxyindole; iron(3+); neurotoxin]
L Tyr[L-tyrosine]
CO2[carbon dioxide]
Parkin[E3 ubiquitin-protein ligase parkin]
Synphilin 1[Synphilin-1]
H2O2[hydrogen peroxide]
Fe3[iron(3+)]
L Dopa[L-dopa]
Protofibril UCH L1[Ubiquitin carboxyl-terminal hydrolase isozyme L1; Alpha-synuclein]
Fibril[Alpha-synuclein; supramolecular fiber]

Saucerman2003_CardiacMyocyte_BetaAdrenergic: MODEL1006230118v0.0.1

This a model from the article: Modeling beta-adrenergic control of cardiac myocyte contractility in silico. Saucerma…

Details

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology. link: http://identifiers.org/pubmed/12972422

Saucerman2006_PKA: BIOMD0000000165v0.0.1

The model reproduces Fig 2B of the paper. Model successfully tested on MathSBML To the extent possible under law, all c…

Details

Compartmentation and dynamics of cAMP and PKA signaling are important determinants of specificity among cAMP's myriad cellular roles. Both cardiac inotropy and the progression of heart disease are affected by spatiotemporal variations in cAMP/PKA signaling, yet the dynamic patterns of PKA-mediated phosphorylation that influence differential responses to agonists have not been characterized. We performed live-cell imaging and systems modeling of PKA-mediated phosphorylation in neonatal cardiac myocytes in response to G-protein coupled receptor stimuli and UV photolysis of "caged" cAMP. cAMP accumulation was rate-limiting in PKA-mediated phosphorylation downstream of the beta-adrenergic receptor. Prostaglandin E1 stimulated higher PKA activity in the cytosol than at the sarcolemma, whereas isoproterenol triggered faster sarcolemmal responses than cytosolic, likely due to restricted cAMP diffusion from submembrane compartments. Localized UV photolysis of caged cAMP triggered gradients of PKA-mediated phosphorylation, enhanced by phosphodiesterase activity and PKA-mediated buffering of cAMP. These findings indicate that combining live-cell FRET imaging and mechanistic computational models can provide quantitative understanding of spatiotemporal signaling. link: http://identifiers.org/pubmed/16905651

Parameters:

NameDescription
Kr=7000.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: PP_cell + AKARp_cell => PP_AKARp_cell, Rate Law: (Kf*PP_cell*AKARp_cell+(-Kr*PP_AKARp_cell))*cell
k_reassoc=1210.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: Gsbg_cell + Gsa_gdp_cell => Gs_cell, Rate Law: k_reassoc*Gsa_gdp_cell*Gsbg_cell*cell
k_barkm=0.0022 s^(-1)Reaction: b1AR_S464_cell => L_b1AR_cell, Rate Law: k_barkm*b1AR_S464_cell*cell
kpka_akar=54.0 s^(-1)Reaction: PKAC_AKAR_cell => AKARp_cell + PKAC_cell, Rate Law: kpka_akar*PKAC_AKAR_cell*cell
Kd=0.535714 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: b1AR_Gs_cell + L_cell => L_b1AR_Gs_cell, Rate Law: (Kf*b1AR_Gs_cell*L_cell+(-Kd*L_b1AR_Gs_cell))*cell
Kf=4375.0 s^(-1); Kr=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: A2RC_cell => A2R_cell + PKAC_cell, Rate Law: (Kf*A2RC_cell+(-Kr*A2R_cell*PKAC_cell))*cell
Kr=9140.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: RC_cell + cAMP_cell => ARC_cell, Rate Law: (Kf*RC_cell*cAMP_cell+(-Kr*ARC_cell))*cell
ar_for_add_propranolol = 0.0Reaction: => Propranolol_cell, Rate Law: ar_for_add_propranolol*cell
kcat_PP_AKARp=8.5 s^(-1)Reaction: PP_AKARp_cell => PP_cell + AKAR_cell, Rate Law: kcat_PP_AKARp*PP_AKARp_cell*cell
ar_for_add_Ligand = 0.0Reaction: => L_cell, Rate Law: ar_for_add_Ligand*cell
Kr=400.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: Gsa_gtp_cell + AC_cell => GsAC_cell, Rate Law: (Kf*Gsa_gtp_cell*AC_cell+(-Kr*GsAC_cell))*cell
Vmax_cAMP_synthesis_FskAC = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=860.0 0.001*dimensionless*m^(-3)*molReaction: ATP_cell => cAMP_cell; FskAC_cell, Rate Law: Vmax_cAMP_synthesis_FskAC*ATP_cell*1/(Km+ATP_cell)*cell
khyd=0.8 s^(-1)Reaction: Gsa_gtp_cell => Gsa_gdp_cell, Rate Law: khyd*Gsa_gtp_cell*cell
Kr=860000.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: AC_cell + Fsk_cell => FskAC_cell, Rate Law: (Kf*AC_cell*Fsk_cell+(-Kr*FskAC_cell))*cell
kpde=5.0 s^(-1)Reaction: PDEcAMP_cell => PDE_cell, Rate Law: kpde*PDEcAMP_cell*cell
Kr=62.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: Gs_cell + L_b1AR_cell => L_b1AR_Gs_cell, Rate Law: (Kf*Gs_cell*L_b1AR_cell+(-Kr*L_b1AR_Gs_cell))*cell
Kr=33000.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: b1AR_cell + Gs_cell => b1AR_Gs_cell, Rate Law: (Kf*b1AR_cell*Gs_cell+(-Kr*b1AR_Gs_cell))*cell
k_gact=16.0 s^(-1)Reaction: L_b1AR_Gs_cell => Gsa_gtp_cell + Gsbg_cell + L_b1AR_cell, Rate Law: k_gact*L_b1AR_Gs_cell*cell
Kr=8.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: Propranolol_cell + b1AR_cell => b1ARinhib_cell, Rate Law: (Kf*Propranolol_cell*b1AR_cell+(-Kr*b1ARinhib_cell))*cell
Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr=21000.0 s^(-1)Reaction: AKAR_cell + PKAC_cell => PKAC_AKAR_cell, Rate Law: (Kf*AKAR_cell*PKAC_cell+(-Kr*PKAC_AKAR_cell))*cell
kphot=0.1 1000*dimensionless*m^3*mol^(-1)*s^(-1); light_cAMP_photolysis = NaN 0.001*dimensionless*m^(-3)*molReaction: DMNB_cAMP_cell => cAMP_cell; light_spot_cell, Rate Law: kphot*light_cAMP_photolysis*DMNB_cAMP_cell*cell
Kr=0.2 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: PKAC_cell + PKI_cell => PKAC_PKI_cell, Rate Law: (Kf*PKAC_cell*PKI_cell+(-Kr*PKAC_PKI_cell))*cell
Kf_inhibit_PDE = NaN 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr_inhibit_PDE = NaN s^(-1)Reaction: PDE_cell + IBMX_cell => PDE_IBMX_cell, Rate Law: (Kf_inhibit_PDE*PDE_cell*IBMX_cell+(-Kr_inhibit_PDE*PDE_IBMX_cell))*cell
Vmax_cAMP_synthesis_GsAC = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=315.0 0.001*dimensionless*m^(-3)*molReaction: ATP_cell => cAMP_cell; GsAC_cell, Rate Law: Vmax_cAMP_synthesis_GsAC*ATP_cell*1/(Km+ATP_cell)*cell
Kr=285.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: L_cell + b1AR_cell => L_b1AR_cell, Rate Law: (Kf*L_cell*b1AR_cell+(-Kr*L_b1AR_cell))*cell
k_barkp=0.0011 s^(-1)Reaction: L_b1AR_cell => b1AR_S464_cell; L_b1AR_Gs_cell, Rate Law: k_barkp*(L_b1AR_cell+L_b1AR_Gs_cell)*cell
Kr=1640.0 s^(-1); Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: ARC_cell + cAMP_cell => A2RC_cell, Rate Law: (Kf*ARC_cell*cAMP_cell+(-Kr*A2RC_cell))*cell
Kf=1000.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr=1300.0 s^(-1)Reaction: PDE_cell + cAMP_cell => PDEcAMP_cell, Rate Law: (Kf*PDE_cell*cAMP_cell+(-Kr*PDEcAMP_cell))*cell
kpkam=0.0022 s^(-1); kpkap=0.0036 1000*dimensionless*m^3*mol^(-1)*s^(-1)Reaction: b1AR_cell => b1AR_p_cell; PKAC_cell, L_b1AR_Gs_cell, L_b1AR_cell, Rate Law: (kpkap*PKAC_cell*(L_b1AR_Gs_cell+b1AR_cell+L_b1AR_cell)+(-kpkam*b1AR_p_cell))*cell

States:

NameDescription
Gsbg cell[G-protein beta/gamma-subunit complex]
PDE IBMX cellPDE_IBMX
AC cell[Adenylate cyclase type 1]
ATP cell[ATP; ATP]
PKI cellPKI
AKAR cellAKAR
L b1AR cell[Beta-1 adrenergic receptor]
PP cellPP
ARC cellARC
Propranolol cellPropranolol
b1AR p cell[Beta-1 adrenergic receptor]
PKAC PKI cellPKAC_PKI
GsAC cell[Adenylate cyclase type 1; Guanine nucleotide-binding protein G(s) subunit alpha isoforms short]
AKARp cellAKARp
PKAC cellPKAC
A2R cellA2R
PDE cell[Calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1A]
IBMX cellIBMX
DMNB cAMP cell[3',5'-cyclic AMP; 3',5'-Cyclic AMP]
Gsa gdp cell[GDP; IPR001019; IPR001019; GDP]
RC cellRC
b1AR Gs cell[Guanine nucleotide-binding protein G(s) subunit alpha isoforms short; Beta-1 adrenergic receptor]
PKAC AKAR cell[cAMP-dependent protein kinase catalytic subunit alpha]
b1ARinhib cellb1ARinhib
cAMP cell[3',5'-cyclic AMP; 3',5'-Cyclic AMP]
Fsk cellFsk
FskAC cellFskAC
b1AR cell[Beta-1 adrenergic receptor]
PP AKARp cellPP_AKARp
L cellL
Gsa gtp cell[GTP; IPR001019; IPR001019; GTP]
L b1AR Gs cell[Guanine nucleotide-binding protein G(s) subunit alpha isoforms short; Beta-1 adrenergic receptor]
A2RC cellA2RC
Gs cell[Guanine nucleotide-binding protein G(s) subunit alpha isoforms short]
PDEcAMP cellPDEcAMP
b1AR S464 cell[Beta-1 adrenergic receptor]

Scaramellini1997 - Two-receptor:One-transducer (2R1T) model for analysis of interactions between agonists: BIOMD0000001008v0.0.1

The two-receptor:one-transducerm odel (Leff, 1987) is here extended to analyze interactions between agonistsd isplaying…

Details

The two-receptor:one-transducer model (Leff, 1987) is here extended to analyze interactions between agonists displaying E[A] curves of different shapes, by incorporating slope factors into the separate and common parts of the transduction pathway. Interactions were modelled as the effect of one agonist, at fixed concentration, on the curve to the other. A variety of patterns of position and slope changes are predicted. These do not depend on the shape of the control curve, rather, they depend on the slope factors in the separate and common pathways. The following specific predictions are made: (1) when the common pathway is steep, curves undergo potentiation and flattening; (2) when the common pathway is flat, curves undergo right-shift and steepening; (3) when the common pathway is hyperbolic, curves undergo right-shift, with no slope change; (4) when the slope depends on the separate pathways, curves only undergo right-shift with no change in slope. The model provides a sound basis for classifying agonist interactions and for detecting additional, synergistic or antagonistic properties. This analysis indicates that methods based on dose-additivity or independence are less reliable for these purposes. The model provides a practical test, based on slope changes, to detect and quantify additional properties. link: http://identifiers.org/pubmed/9253753

Schaber2006_Pheromone_Starvation_Crosstalk: BIOMD0000000237v0.0.1

This a model from the article: A modelling approach to quantify dynamic crosstalk between the pheromone and the star…

Details

Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other. link: http://identifiers.org/pubmed/16884493

Parameters:

NameDescription
k8 = 0.1Reaction: Ste11Ubi => p, Rate Law: compartment*k8*Ste11Ubi
k20 = 1.0Reaction: s => PREP; Ste12P, Rate Law: compartment*k20*Ste12P
k31 = 1.0Reaction: Ste12P => Ste12, Rate Law: compartment*k31*Ste12P
k7 = 10.0Reaction: Ste5 + Ste5Ste11GbgP => Gbg + Ste11Ubi, Rate Law: compartment*k7*Ste5Ste11GbgP
k1 = 0.01Reaction: Ste5 + Ste11 => Ste5Ste11, Rate Law: compartment*k1*Ste5*Ste11
k22 = 1.0Reaction: Kss1 + Ste12TeSte5 => Ste12TeSte5Kss1, Rate Law: compartment*k22*Kss1*Ste12TeSte5
k26 = 0.1Reaction: Fus3PP => Fus3, Rate Law: compartment*k26*Fus3PP
k3 = 1.0Reaction: Ste5Ste11Gbg + Fus3 => Ste5Ste11GbgFus3, Rate Law: compartment*k3*Ste5Ste11Gbg*Fus3
k25 = 1.0Reaction: s => FREP; Ste12TeSte5P, Rate Law: compartment*k25*Ste12TeSte5P
k12 = 1.0Reaction: Ste5Ste11GbgP + Kss1 => Ste5Ste11GbgKss1P, Rate Law: compartment*k12*Ste5Ste11GbgP*Kss1
k15 = 0.1; k30 = 0.1Reaction: Kss1 => Kss1PP; Ste11P, Ste11Ubi, Rate Law: compartment*(k15*Kss1*Ste11P+k30*Kss1*Ste11Ubi)
k5 = 1.0Reaction: Ste5Ste11GbgFus3P => Fus3PP + Ste5Ste11GbgP, Rate Law: compartment*k5*Ste5Ste11GbgFus3P
k21 = 1.0Reaction: Ste12TeSte5Kss1 => Kss1 + Ste12TeSte5, Rate Law: compartment*k21*Ste12TeSte5Kss1
k9 = 1.0Reaction: Ste5Ste11Gbg + Kss1 => Ste5Ste11GbgKss1, Rate Law: compartment*k9*Ste5Ste11Gbg*Kss1
k6 = 1.0Reaction: Fus3 + Ste5Ste11GbgP => Ste5Ste11GbgFus3P, Rate Law: compartment*k6*Fus3*Ste5Ste11GbgP
k32 = 1.0Reaction: PREP => p, Rate Law: compartment*k32*PREP
k14 = 0.1Reaction: Ste11P => Ste11, Rate Law: compartment*k14*Ste11P
k33 = 1.0Reaction: Ste12TeSte5P => Ste12TeSte5, Rate Law: compartment*k33*Ste12TeSte5P
k11 = 1.0Reaction: Ste5Ste11GbgKss1P => Ste5Ste11GbgP + Kss1PP, Rate Law: compartment*k11*Ste5Ste11GbgKss1P
k16 = 0.1; k28 = 0.01Reaction: Kss1PP => Kss1; Fus3PP, Rate Law: compartment*(k16*Kss1PP+k28*Kss1PP*Fus3PP)
k23 = 1.0Reaction: Ste12TeSte5 => Ste12TeSte5P; Kss1PP, Rate Law: compartment*k23*Ste12TeSte5*Kss1PP
k27 = 1.0Reaction: Ste5Ste11 => Ste5 + Ste11, Rate Law: compartment*k27*Ste5Ste11
k10 = 1.0Reaction: Ste5Ste11GbgKss1 => Ste5Ste11GbgKss1P, Rate Law: compartment*k10*Ste5Ste11GbgKss1
k4 = 1.0Reaction: Ste5Ste11GbgFus3 => Ste5Ste11GbgFus3P, Rate Law: compartment*k4*Ste5Ste11GbgFus3
k29 = 0.01; k19 = 1.0Reaction: Ste12 => Ste12P; Fus3PP, Kss1PP, Rate Law: compartment*(k19*Ste12*Fus3PP+k29*Ste12*Kss1PP)
k24 = 0.01Reaction: Ste12TeSte5 => p; Fus3PP, Rate Law: compartment*k24*Ste12TeSte5*Fus3PP
k34 = 1.0Reaction: FREP => p, Rate Law: compartment*k34*FREP
beta = NaN; k13 = 1.0Reaction: Ste11 => Ste11P, Rate Law: compartment*k13*Ste11*beta
k18 = 10.0Reaction: Kss1 + Ste12 => Ste12Kss1, Rate Law: compartment*k18*Kss1*Ste12
k17 = 1.0Reaction: Ste12Kss1 => Kss1 + Ste12, Rate Law: compartment*k17*Ste12Kss1
k2 = 0.01; alpha = NaNReaction: Ste5Ste11 + Gbg => Ste5Ste11Gbg, Rate Law: compartment*k2*Ste5Ste11*Gbg*alpha

States:

NameDescription
Ste11Ubi[Serine/threonine-protein kinase STE11; Ubiquitin-60S ribosomal protein L40Ubiquitin-60S ribosomal protein L40Ubiquitin-40S ribosomal protein S31Polyubiquitin]
Ste5Ste11GbgFus3P[Protein STE5; Serine/threonine-protein kinase STE11; G protein beta subunit; Heterotrimeric G protein gamma subunit GPG1; Mitogen-activated protein kinase FUS3]
Ste12Kss1[Mitogen-activated protein kinase KSS1; Protein STE12]
Ste5Ste11GbgP[Protein STE5; Serine/threonine-protein kinase STE11; G protein beta subunit; Heterotrimeric G protein gamma subunit GPG1]
Kss1[Mitogen-activated protein kinase KSS1]
Ste5Ste11GbgKss1P[Protein STE5; Serine/threonine-protein kinase STE11; G protein beta subunit; Heterotrimeric G protein gamma subunit GPG1; Mitogen-activated protein kinase KSS1]
Fus3[Mitogen-activated protein kinase FUS3]
Ste12TeSte5[Protein STE5; Protein STE12]
Ste5[Protein STE5; MAP-kinase scaffold activity]
Ste12[Protein STE12]
Ste12P[Protein STE12]
Ste5Ste11[Serine/threonine-protein kinase STE11; Protein STE5]
PREP[response to pheromone]
ss
Ste11[Serine/threonine-protein kinase STE11; MAP kinase kinase kinase activity]
Ste12TeSte5Kss1[Protein STE5; Mitogen-activated protein kinase KSS1; Protein STE12]
Fus3PP[Mitogen-activated protein kinase FUS3; MAP kinase activity]
Ste5Ste11Gbg[Heterotrimeric G protein gamma subunit GPG1; G protein beta subunit; Protein STE5; Serine/threonine-protein kinase STE11]
Gbg[G protein beta subunit; Heterotrimeric G protein gamma subunit GPG1]
Ste12TeSte5P[Protein STE5; Protein STE12]
Ste5Ste11GbgFus3[Protein STE5; Serine/threonine-protein kinase STE11; G protein beta subunit; Heterotrimeric G protein gamma subunit GPG1; Mitogen-activated protein kinase FUS3]
Kss1PP[Mitogen-activated protein kinase KSS1]
FREP[invasive growth in response to pheromone]
Ste11P[Serine/threonine-protein kinase STE11]
Ste5Ste11GbgKss1[Protein STE5; Serine/threonine-protein kinase STE11; G protein beta subunit; Heterotrimeric G protein gamma subunit GPG1; Mitogen-activated protein kinase KSS1]
pp

Schaber2012 - Hog pathway in yeast: BIOMD0000000429v0.0.1

Schaber2012 - Hog pathway in yeastThe high osmolarity glycerol (HOG) pathway in the yeast Saccharomyces cerevisiae is on…

Details

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms. link: http://identifiers.org/pubmed/23149687

Parameters:

NameDescription
parameter_79 = 0.00226722Reaction: species_11 => species_10 + species_4; species_11, Rate Law: parameter_79*species_11
parameter_82 = 0.00459138; parameter_56 = 0.0036065403549782; parameter_81 = 2.0793; parameter_80 = 0.297524; parameter_57 = 1.0Reaction: species_4 + species_10 => species_11; species_12, species_4, species_10, species_12, Rate Law: compartment_1*parameter_57*parameter_82*parameter_56*species_4/compartment_1*species_10/compartment_1/(1+(species_12/compartment_1/parameter_80)^parameter_81)
parameter_39 = 7.09644965005112Reaction: species_8 => ; species_8, Rate Law: parameter_39*species_8
parameter_87 = 46.8363; parameter_88 = 0.420741; parameter_86 = 680.818Reaction: => species_1; species_7, species_12, species_7, species_12, Rate Law: compartment_1*parameter_86*species_7/compartment_1*(1+parameter_87*species_12/compartment_1)/(parameter_88+species_7/compartment_1*(1+parameter_87*species_12/compartment_1))
parameter_63 = 0.500000000000001; parameter_71 = 1.0; parameter_83 = 0.00529124Reaction: species_14 => species_15; species_14, Rate Law: compartment_4*parameter_71*parameter_83*parameter_63*species_14/compartment_4
parameter_78 = 0.506878; parameter_77 = 18.1824Reaction: => species_8; species_12, species_12, Rate Law: compartment_1*parameter_77*species_12/compartment_1/(parameter_78+species_12/compartment_1)
parameter_69 = 1.0Reaction: species_12 = parameter_69*species_3/compartment_1*compartment_1, Rate Law: missing
parameter_35 = 6.78688610600496E-5Reaction: species_7 => ; species_7, Rate Law: parameter_35*species_7
parameter_75 = 0.075474; parameter_73 = 0.00940584; parameter_58 = 1.0; parameter_74 = 0.345701; parameter_22 = 7.10539561053171E-4Reaction: species_4 => species_5; species_12, species_4, species_12, Rate Law: compartment_1*parameter_58*parameter_75*parameter_22*species_4/compartment_1/(1+(species_12/compartment_1/parameter_73)^parameter_74)
parameter_26 = 1.78587Reaction: species_3 => species_9; species_6, species_6, species_3, Rate Law: compartment_1*parameter_26*species_6/compartment_1*species_3/compartment_1
parameter_71 = 1.0; parameter_84 = 0.0811033; parameter_65 = 0.00320327093093651; parameter_85 = 0.628719Reaction: species_15 => species_14; species_12, species_15, species_12, Rate Law: compartment_4*parameter_71*parameter_65*species_15/compartment_4/(1+(species_12/compartment_1/parameter_84)^parameter_85)
parameter_72 = 0.607124Reaction: species_5 => species_4; species_6, species_6, species_5, Rate Law: compartment_1*parameter_72*species_6/compartment_1*species_5/compartment_1
parameter_27 = 4.28194136809108E-4; parameter_28 = 0.5; parameter_16 = 65.6342903668733Reaction: species_1 => species_13; species_13, species_1, Rate Law: parameter_28*parameter_27*parameter_16*(species_1/compartment_1-species_13/compartment_2)
parameter_25 = 42.6396538263077; parameter_41 = 48.0003902091319Reaction: species_2 => species_9; species_5, species_11, species_5, species_2, species_11, Rate Law: compartment_1*(parameter_25*species_5/compartment_1*species_2/compartment_1+parameter_41*species_11/compartment_1*species_2/compartment_1)
parameter_76 = 9.06781E-5Reaction: => species_7; species_8, species_8, Rate Law: compartment_1*parameter_76*species_8/compartment_1

States:

NameDescription
species 9[Mitogen-activated protein kinase HOG1; Phosphoprotein]
species 2[Mitogen-activated protein kinase HOG1; S000004103]
species 10[High osmolarity signaling protein SHO1; S000000920]
species 11[High osmolarity signaling protein SHO1; MAP kinase kinase PBS2]
species 1[glycerol]
species 4[S000003664]
species 14[Glycerol uptake/efflux facilitator protein; S000003966]
species 3[Mitogen-activated protein kinase HOG1; Phosphoprotein]
species 8[messenger RNA]
species 12[Mitogen-activated protein kinase HOG1]
species 7[protein]
species 5[MAP kinase kinase PBS2]
species 15[Glycerol uptake/efflux facilitator protein; S000003966]
species 13[glycerol]

Schattler2016 - Dynamical properties of a minimally parameterized mathematical model for metronomic chemotherapy: MODEL2002030001v0.0.1

A minimally parameterized mathematical model for low-dose metronomic chemotherapy is formulated that takes into account…

Details

A minimally parameterized mathematical model for low-dose metronomic chemotherapy is formulated that takes into account angiogenic signaling between the tumor and its vasculature and tumor inhibiting effects of tumor-immune system interactions. The dynamical equations combine a model for tumor development under angiogenic signaling formulated by Hahnfeldt et al. with a model for tumor-immune system interactions by Stepanova. The dynamical properties of the model are analyzed. Depending on the parameter values, the system encompasses a variety of medically realistic scenarios that range from cases when (i) low-dose metronomic chemotherapy is able to eradicate the tumor (all trajectories converge to a tumor-free equilibrium point) to situations when (ii) tumor dormancy is induced (a unique, globally asymptotically stable benign equilibrium point exists) to (iii) multi-stable situations that have both persistent benign and malignant behaviors separated by the stable manifold of an unstable equilibrium point and finally to (iv) situations when tumor growth cannot be overcome by low-dose metronomic chemotherapy. The model forms a basis for a more general study of chemotherapy when the main components of a tumor's microenvironment are taken into account. link: http://identifiers.org/pubmed/26089097

Scheidel2016 - In Silico Knockout Studies of Xenophagy Capturing of Salmonella: MODEL1904150001v0.0.1

Xenophagy, also known as antibacterial autophagy, is a process of capturing and eliminating cytosolic pathogens, like Sa…

Details

The degradation of cytosol-invading pathogens by autophagy, a process known as xenophagy, is an important mechanism of the innate immune system. Inside the host, Salmonella Typhimurium invades epithelial cells and resides within a specialized intracellular compartment, the Salmonella-containing vacuole. A fraction of these bacteria does not persist inside the vacuole and enters the host cytosol. Salmonella Typhimurium that invades the host cytosol becomes a target of the autophagy machinery for degradation. The xenophagy pathway has recently been discovered, and the exact molecular processes are not entirely characterized. Complete kinetic data for each molecular process is not available, so far. We developed a mathematical model of the xenophagy pathway to investigate this key defense mechanism. In this paper, we present a Petri net model of Salmonella xenophagy in epithelial cells. The model is based on functional information derived from literature data. It comprises the molecular mechanism of galectin-8-dependent and ubiquitin-dependent autophagy, including regulatory processes, like nutrient-dependent regulation of autophagy and TBK1-dependent activation of the autophagy receptor, OPTN. To model the activation of TBK1, we proposed a new mechanism of TBK1 activation, suggesting a spatial and temporal regulation of this process. Using standard Petri net analysis techniques, we found basic functional modules, which describe different pathways of the autophagic capture of Salmonella and reflect the basic dynamics of the system. To verify the model, we performed in silico knockout experiments. We introduced a new concept of knockout analysis to systematically compute and visualize the results, using an in silico knockout matrix. The results of the in silico knockout analyses were consistent with published experimental results and provide a basis for future investigations of the Salmonella xenophagy pathway. link: http://identifiers.org/pubmed/27906974

Scheper1999_CircClock: BIOMD0000000024v0.0.1

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedic…

Details

A mathematical model for the intracellular circadian rhythm generator has been studied, based on a negative feedback of protein products on the transcription rate of their genes. The study is an attempt at examining minimal but biologically realistic requirements for a negative molecular feedback loop involving considerably faster reactions, to produce (slow) circadian oscillations. The model included mRNA and protein production and degradation, along with a negative feedback of the proteins upon mRNA production. The protein production process was described solely by its total duration and a nonlinear term, whereas also the feedback included nonlinear interactions among protein molecules. This system was found to produce robust oscillations in protein and mRNA levels over a wide range of parameter values. Oscillations were slow, with periods much longer than the time constants of any of the individual system parameters. Circadian oscillations were obtained for realistic values of the parameters. The system was readily entrainable to external periodic perturbations. Two distinct classes of phase response curves were found, viz. with or without a time domain within the circadian cycle in which external perturbations fail to induce a phase shift ("dead zone"). The delay and nonlinearity in the protein production and the cooperativity in the negative feedback (Hill coefficient) were for this model found to be necessary and sufficient to generate robust circadian oscillations. The similarities between model outcomes and empirical findings establish that circadian rhythmicity at the cellular level can plausibly emerge from interactions among molecular systems which are not in themselves rhythmic. link: http://identifiers.org/pubmed/9870936

Parameters:

NameDescription
k=1.0; rM=1.0; n=2.0Reaction: EmptySet => M; P, Rate Law: compartment_0000004*rM/(1+(P/k)^n)
qP=0.21Reaction: P => EmptySet, Rate Law: compartment_0000004*qP*P
qM=0.21Reaction: M => EmptySet, Rate Law: compartment_0000004*qM*M
parameter_0000009=4.0; rP=1.0; m=3.0Reaction: EmptySet => P; M, Rate Law: compartment_0000004*rP*delay(M, parameter_0000009)^m

States:

NameDescription
M[messenger RNA; RNA]
P[Protein; protein polypeptide chain]

Schilling2000- Genome-scale metabolic network of Haemophilus influenzae (iCS400): MODEL1507180053v0.0.1

Schilling2000- Genome-scale metabolic network of Haemophilus influenzae (iCS400)This model is described in the article:…

Details

The annotated full DNA sequence is becoming available for a growing number of organisms. This information along with additional biochemical and strain-specific data can be used to define metabolic genotypes and reconstruct cellular metabolic networks. The first free-living organism for which the entire genomic sequence was established was Haemophilus influenzae. Its metabolic network is reconstructed herein and contains 461 reactions operating on 367 intracellular and 84 extracellular metabolites. With the metabolic reaction network established, it becomes necessary to determine its underlying pathway structure as defined by the set of extreme pathways. The H. influenzae metabolic network was subdivided into six subsystems and the extreme pathways determined for each subsystem based on stoichiometric, thermodynamic, and systems-specific constraints. Positive linear combinations of these pathways can be taken to determine the extreme pathways for the complete system. Since these pathways span the capabilities of the full system, they could be used to address a number of important physiological questions. First, they were used to reconcile and curate the sequence annotation by identifying reactions whose function was not supported in any of the extreme pathways. Second, they were used to predict gene products that should be co-regulated and perhaps co-expressed. Third, they were used to determine the composition of the minimal substrate requirements needed to support the production of 51 required metabolic products such as amino acids, nucleotides, phospholipids, etc. Fourth, sets of critical gene deletions from core metabolism were determined in the presence of the minimal substrate conditions and in more complete conditions reflecting the environmental niche of H. influenzae in the human host. In the former case, 11 genes were determined to be critical while six remained critical under the latter conditions. This study represents an important milestone in theoretical biology, namely the establishment of the first extreme pathway structure of a whole genome. link: http://identifiers.org/pubmed/10716908

Schilling2002 - Genome-scale metabolic network of Helicobacter pylori (iCS291): MODEL1507180037v0.0.1

Schilling2002 - Genome-scale metabolic network of Helicobacter pylori (iCS291)This model is described in the article: […

Details

A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes. link: http://identifiers.org/pubmed/12142428

Schilling2009 - ERK distributive: BIOMD0000000270v0.0.1

Schilling2009 - ERK distributive This model has been exported from [PottersWheel](http://www.potterswheel.de) on 200…

Details

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions. link: http://identifiers.org/pubmed/20029368

Parameters:

NameDescription
SOS_recruitment_by_pEpoR = 0.10271 second order rate constantReaction: SOS => mSOS; pEpoR, Rate Law: SOS_recruitment_by_pEpoR*SOS*pEpoR*cell
First_MEK1_phosphorylation_by_pRaf = 0.687193 second order rate constantReaction: MEK1 => pMEK1; pRaf, Rate Law: First_MEK1_phosphorylation_by_pRaf*MEK1*pRaf*cell
EpoR_phosphorylation_by_pJAK2 = 3.15714 second order rate constantReaction: EpoR => pEpoR; pJAK2, Rate Law: EpoR_phosphorylation_by_pJAK2*EpoR*pJAK2*cell
Second_MEK1_phosphorylation_by_pRaf = 667.957 second order rate constantReaction: pMEK1 => ppMEK1; pRaf, Rate Law: Second_MEK1_phosphorylation_by_pRaf*pMEK1*pRaf*cell
SHP1_activation_by_pEpoR = 0.408408 second order rate constantReaction: SHP1 => mSHP1; pEpoR, Rate Law: SHP1_activation_by_pEpoR*SHP1*pEpoR*cell
Second_ERK2_phosphorylation_by_ppMEK = 53.0816 second order rate constantReaction: pERK2 => ppERK2; ppMEK2, Rate Law: Second_ERK2_phosphorylation_by_ppMEK*pERK2*ppMEK2*cell
Second_ERK_dephosphorylation = 3.00453 per minuteReaction: pERK2 => ERK2, Rate Law: Second_ERK_dephosphorylation*pERK2*cell
First_ERK2_phosphorylation_by_ppMEK = 2.44361 second order rate constantReaction: ERK2 => pERK2; ppMEK1, Rate Law: First_ERK2_phosphorylation_by_ppMEK*ERK2*ppMEK1*cell
Second_MEK_dephosphorylation = 0.0732724 per minuteReaction: pMEK2 => MEK2, Rate Law: Second_MEK_dephosphorylation*pMEK2*cell
pRaf_dephosphorylation = 0.374228 per minuteReaction: pRaf => Raf, Rate Law: pRaf_dephosphorylation*pRaf*cell
actSHP1_deactivation = 0.0248773 per minuteReaction: actSHP1 => SHP1, Rate Law: actSHP1_deactivation*actSHP1*cell
pJAK2_dephosphorylation_by_actSHP1 = 0.368384 second order rate constantReaction: pJAK2 => JAK2; actSHP1, Rate Law: pJAK2_dephosphorylation_by_actSHP1*pJAK2*actSHP1*cell
mSOS_induced_Raf_phosphorylation = 0.144515 second order rate constantReaction: Raf => pRaf; mSOS, Rate Law: mSOS_induced_Raf_phosphorylation*Raf*mSOS*cell
SHP1_delay = 0.408408 per minuteReaction: Delay01_mSHP1 => Delay02_mSHP1, Rate Law: SHP1_delay*Delay01_mSHP1*cell
pSOS_dephosphorylation = 0.124944 per minuteReaction: pSOS => SOS, Rate Law: pSOS_dephosphorylation*pSOS*cell
First_MEK_dephosphorylation = 0.130937 per minuteReaction: ppMEK1 => pMEK1, Rate Law: First_MEK_dephosphorylation*ppMEK1*cell
First_ERK1_phosphorylation_by_ppMEK = 2.4927 second order rate constantReaction: ERK1 => pERK1; ppMEK2, Rate Law: First_ERK1_phosphorylation_by_ppMEK*ERK1*ppMEK2*cell
mSOS_release_from_membrane = 15.5956 per minuteReaction: mSOS => SOS, Rate Law: mSOS_release_from_membrane*mSOS*cell
pEpoR_dephosphorylation_by_actSHP1 = 1.19995 second order rate constantReaction: pEpoR => EpoR; actSHP1, Rate Law: pEpoR_dephosphorylation_by_actSHP1*pEpoR*actSHP1*cell
ppERK_neg_feedback_on_mSOS = 5122.68 second order rate constantReaction: mSOS => pSOS; ppERK1, Rate Law: ppERK_neg_feedback_on_mSOS*mSOS*ppERK1*cell
JAK2_phosphorylation_by_Epo = 0.0122149 per min per (Uml)Reaction: JAK2 => pJAK2; Epo, Rate Law: JAK2_phosphorylation_by_Epo*JAK2*Epo*cell
First_ERK_dephosphorylation = 39.0886 per minuteReaction: ppERK1 => pERK1, Rate Law: First_ERK_dephosphorylation*ppERK1*cell
First_MEK2_phosphorylation_by_pRaf = 3.11919 second order rate constantReaction: MEK2 => pMEK2; pRaf, Rate Law: First_MEK2_phosphorylation_by_pRaf*MEK2*pRaf*cell
Second_ERK1_phosphorylation_by_ppMEK = 59.5251 second order rate constantReaction: pERK1 => ppERK1; ppMEK2, Rate Law: Second_ERK1_phosphorylation_by_ppMEK*pERK1*ppMEK2*cell
Second_MEK2_phosphorylation_by_pRaf = 215.158 second order rate constantReaction: pMEK2 => ppMEK2; pRaf, Rate Law: Second_MEK2_phosphorylation_by_pRaf*pMEK2*pRaf*cell

States:

NameDescription
mSHP1[Tyrosine-protein phosphatase non-receptor type 6; extrinsic component of plasma membrane]
Delay04 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
ppMEK1[urn:miriam:mod:MOD%3A00048; urn:miriam:mod:MOD%3A00047; MAP kinase kinase kinase activity; Phosphoprotein; Dual specificity mitogen-activated protein kinase kinase 1]
Delay01 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
pERK1[urn:miriam:mod:MOD%3A00048; Mitogen-activated protein kinase 3; Phosphoprotein]
Delay05 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
pSOS[Son of sevenless homolog 1; Phosphoprotein]
Delay06 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
SHP1[IPR000387; Tyrosine-protein phosphatase non-receptor type 6]
MEK2[Dual specificity mitogen-activated protein kinase kinase 2]
pJAK2[urn:miriam:mod:MOD%3A00048; Phosphoprotein; REACT_24029; protein tyrosine kinase activity; Tyrosine-protein kinase JAK2]
MEK1[Dual specificity mitogen-activated protein kinase kinase 1]
Delay03 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
mSOS[Son of sevenless homolog 1; extrinsic component of plasma membrane; guanyl-nucleotide exchange factor activity]
JAK2[Tyrosine-protein kinase JAK2]
Delay08 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
Delay02 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
EpoR[Erythropoietin receptor]
ppERK1[urn:miriam:mod:MOD%3A00047; urn:miriam:mod:MOD%3A00048; Phosphoprotein; MAP kinase activity; Mitogen-activated protein kinase 3]
pMEK1[Phosphoprotein; Dual specificity mitogen-activated protein kinase kinase 1]
ppERK2[urn:miriam:mod:MOD%3A00047; urn:miriam:mod:MOD%3A00048; Mitogen-activated protein kinase 1; MAP kinase activity; Phosphoprotein]
pMEK2[Phosphoprotein; Dual specificity mitogen-activated protein kinase kinase 2]
SOS[Son of sevenless homolog 1]
actSHP1[Tyrosine-protein phosphatase non-receptor type 6; protein tyrosine phosphatase activity]
ppMEK2[Phosphoprotein; Dual specificity mitogen-activated protein kinase kinase 2; urn:miriam:mod:MOD%3A00048; urn:miriam:mod:MOD%3A00047; MAP kinase kinase kinase activity]
pERK2[urn:miriam:mod:MOD%3A00048; Phosphoprotein; Mitogen-activated protein kinase 1]
Delay07 mSHP1[Tyrosine-protein phosphatase non-receptor type 6]
ERK1[Mitogen-activated protein kinase 3]
Raf[RAF proto-oncogene serine/threonine-protein kinase]
pEpoR[Phosphoprotein; Erythropoietin receptor; urn:miriam:mod:MOD%3A00048]
ERK2[Mitogen-activated protein kinase 1]
pRaf[RAF proto-oncogene serine/threonine-protein kinase; Phosphoprotein; urn:miriam:mod:MOD%3A00046; p-S259,S621-RAF1 [cytosol]; MAP kinase kinase kinase activity]

Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes: BIOMD0000000493v0.0.1

Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytesMathematical model describing the mechanism of…

Details

Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation. link: http://identifiers.org/pubmed/21198133

Parameters:

NameDescription
kP = 0.1Reaction: P =>, Rate Law: kP*P
kC = 0.1Reaction: C =>, Rate Law: kC*C
mO = 1.0; cOP = 0.5; zO = 0.0; cOO = 0.1; bO = 1.0; aO = 0.1; n = 2.0; cOC = 0.1Reaction: => O; P, C, Rate Law: (aO*O^n+bO+zO)/(mO+cOC*C^n+cOP*P^n+cOO*O^n)
kO = 0.1Reaction: O =>, Rate Law: kO*O
mC = 1.0; cCO = 0.1; cCP = 0.5; aC = 0.1; bC = 1.0; n = 2.0; zC = 0.0; cCC = 0.1Reaction: => C; P, O, Rate Law: (aC*C^n+bC+zC)/(mC+cCO*O^n+cCP*P^n+cCC*C^n)
zD = 0.0; bP = 0.5; aP = 0.2; mP = 10.0; n = 2.0; cPP = 0.1Reaction: => P, Rate Law: (aP*P^n+bP)/(mP+zD+cPP*P^n)

States:

NameDescription
C[chondrogenic cell]
P[hematopoietic stem cell]
O[osteogenic cell]

Schliemann2011_TNF_ProAntiApoptosis: BIOMD0000000407v0.0.1

This model is from the article: Heterogeneity Reduces Sensitivity of Cell Death for TNF-Stimuli Schliemann M, Bullin…

Details

BACKGROUND: Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor κB (NF-κB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner. RESULTS: This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-κB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability. CONCLUSIONS: Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-κB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity. link: http://identifiers.org/pubmed/22204418

Parameters:

NameDescription
ka_84=5.0E-5 s^(-1)Reaction: XIAP_Casp3 => XIAP, Rate Law: ka_84*XIAP_Casp3
ka_23=0.0118534 amol^(-2)*s^(-1)Reaction: FADD + TNFRCint2 => TNFRCint3, Rate Law: ka_23*FADD^2*TNFRCint2
kd_88=0.001 s^(-1); ka_88=0.520833 amol^(-1)*s^(-1)Reaction: BAR + Casp8 => BAR_Casp8, Rate Law: ka_88*BAR*Casp8-kd_88*BAR_Casp8
ka_82=0.625 amol^(-1)*s^(-1); kd_82=0.001 s^(-1)Reaction: XIAP + Casp3 => XIAP_Casp3, Rate Law: ka_82*XIAP*Casp3-kd_82*XIAP_Casp3
ka_48=4.70498E-4 s^(-1)Reaction: A20_mRNA =>, Rate Law: ka_48*A20_mRNA
ka_42=1.0E-4 s^(-1)Reaction: NFkB_N =>, Rate Law: ka_42*NFkB_N
ka_41=1.0E-4 s^(-1)Reaction: IkBa_NFkB =>, Rate Law: ka_41*IkBa_NFkB
ka_66=3.33333E-5 s^(-1)Reaction: => FLIP_mRNA; NFkB_N, Rate Law: ka_66*NFkB_N
ka_33=0.00976562 amol^(-2)*s^(-1)Reaction: RIP + TRAF2 + TNFRC2_FLIP_pCasp8 => TNFRC2_FLIP_pCasp8_RIP_TRAF2, Rate Law: ka_33*RIP*TRAF2*TNFRC2_FLIP_pCasp8
ka_25=0.3125 amol^(-1)*s^(-1)Reaction: TNFRC2 + FLIP => TNFRC2_FLIP, Rate Law: ka_25*TNFRC2*FLIP
ka_62=3.78788E-5 s^(-1)Reaction: => A20_mRNA; NFkB_N, Rate Law: ka_62*NFkB_N
kd_69=6.17284E-5 s^(-1); ka_69=4.93827E-5 amol*s^(-1)Reaction: => pCasp3, Rate Law: ka_69-kd_69*pCasp3
ka_80=0.009375 amol^(-1)*s^(-1)Reaction: pCasp6 => Casp6; Casp3, Rate Law: ka_80*pCasp6*Casp3
ka_18=0.00953471 amol^(-1)*s^(-1); kd_18=6.60377E-5 s^(-1)Reaction: TNFR_E + TNF_E => TNF_TNFR_E, Rate Law: ka_18*TNFR_E*TNF_E-kd_18*TNF_TNFR_E
ka_7=3.0944E-5 amol*s^(-1); kd_7=1.0E-4 s^(-1)Reaction: => FADD, Rate Law: ka_7-kd_7*FADD
ka_24=0.1135 s^(-1)Reaction: TNFRCint3 => TNFRC2, Rate Law: ka_24*TNFRCint3
ka_53=0.00625 amol^(-1)*s^(-1)Reaction: TNFRC1 => TRAF2 + TNF_TNFR_TRADD; A20, Rate Law: ka_53*TNFRC1*A20
ka_22=0.001135 s^(-1)Reaction: TNFRCint1 => RIP + TRAF2 + TNFRCint2, Rate Law: ka_22*TNFRCint1
ka_9=0.02352 s^(-1)Reaction: TNF_TNFR_TRADD =>, Rate Law: ka_9*TNF_TNFR_TRADD
ka_30=0.3125 amol^(-1)*s^(-1)Reaction: FLIP + TNFRC2_pCasp8 => TNFRC2_FLIP_pCasp8, Rate Law: ka_30*FLIP*TNFRC2_pCasp8
ka_12=5.6E-5 s^(-1)Reaction: TNFRC2_FLIP =>, Rate Law: ka_12*TNFRC2_FLIP
ka_87=0.1875 amol^(-1)*s^(-1)Reaction: PARP => cPARP; Casp3, Rate Law: ka_87*Casp3*PARP
ka_19=0.00427827 amol^(-1)*s^(-1)Reaction: TNF_TNFR_E + TRADD => TNF_TNFR_TRADD, Rate Law: ka_19*TNF_TNFR_E*TRADD
ka_47=0.0115517 s^(-1)Reaction: PIkBa =>, Rate Law: ka_47*PIkBa
ka_79=0.015625 amol^(-1)*s^(-1)Reaction: pCasp3 => Casp3; Casp8, Rate Law: ka_79*pCasp3*Casp8
ka_64=3.33333E-5 s^(-1)Reaction: => XIAP_mRNA; NFkB_N, Rate Law: ka_64*NFkB_N
ka_26=0.3125 amol^(-1)*s^(-1)Reaction: FLIP + TNFRC2_FLIP => TNFRC2_FLIP_FLIP, Rate Law: ka_26*FLIP*TNFRC2_FLIP
ka_43=3.94201E-4 s^(-1)Reaction: IkBa_mRNA =>, Rate Law: ka_43*IkBa_mRNA
ka_16=5.6E-5 s^(-1)Reaction: TNFRC2_FLIP_pCasp8 =>, Rate Law: ka_16*TNFRC2_FLIP_pCasp8
ka_31=0.3125 amol^(-1)*s^(-1)Reaction: TNFRC2_FLIP + pCasp8 => TNFRC2_FLIP_pCasp8, Rate Law: ka_31*TNFRC2_FLIP*pCasp8
ka_63=0.0151515 s^(-1)Reaction: => A20; A20_mRNA, Rate Law: ka_63*A20_mRNA
ka_59=0.005 s^(-1); kd_59=0.00257576 s^(-1)Reaction: IkBa => IkBa_N, Rate Law: ka_59*IkBa-kd_59*IkBa_N
kd_75=5.78704E-6 s^(-1); ka_75=1.66603E-6 amol*s^(-1)Reaction: => BAR, Rate Law: ka_75-kd_75*BAR
ka_46=1.0E-4 s^(-1)Reaction: IkBa_NFkB_N =>, Rate Law: ka_46*IkBa_NFkB_N
ka_3=5.6E-5 s^(-1)Reaction: TNFR_E =>, Rate Law: ka_3*TNFR_E
ka_65=0.0506061 s^(-1)Reaction: => XIAP; XIAP_mRNA, Rate Law: ka_65*XIAP_mRNA
ka_70=3.95062E-6 amol*s^(-1); kd_70=6.17284E-5 s^(-1)Reaction: => pCasp6, Rate Law: ka_70-kd_70*pCasp6
ka_27=0.03125 amol^(-1)*s^(-1)Reaction: TNFRC2 + pCasp8 => TNFRC2_pCasp8, Rate Law: ka_27*TNFRC2*pCasp8
ka_35=6.4E-5 amol*s^(-1); kd_35=1.0E-4 s^(-1)Reaction: => IKK, Rate Law: ka_35-kd_35*IKK
ka_28=0.03125 amol^(-1)*s^(-1)Reaction: TNFRC2_pCasp8 + pCasp8 => TNFRC2_pCasp8_pCasp8, Rate Law: ka_28*TNFRC2_pCasp8*pCasp8
kd_36=1.0E-4 s^(-1); ka_36=1.6E-6 amol*s^(-1)Reaction: => NFkB, Rate Law: ka_36-kd_36*NFkB
ka_29=0.45 s^(-1)Reaction: TNFRC2_pCasp8_pCasp8 => TNFRC2 + Casp8, Rate Law: ka_29*TNFRC2_pCasp8_pCasp8
ka_1=0.001 s^(-1)Reaction: TNFR => TNFR_E, Rate Law: ka_1*TNFR
ka_11=5.6E-5 s^(-1)Reaction: TNFRC2 =>, Rate Law: ka_11*TNFRC2
ka_5=2.9344E-5 amol*s^(-1); kd_5=1.0E-4 s^(-1)Reaction: => TRADD, Rate Law: ka_5-kd_5*TRADD
ka_55=0.104167 amol^(-1)*s^(-1)Reaction: IkBa_NFkB => NFkB + PIkBa; IKKa, Rate Law: ka_55*IKKa*IkBa_NFkB
ka_49=1.04931E-4 s^(-1)Reaction: XIAP_mRNA =>, Rate Law: ka_49*XIAP_mRNA
ka_54=1.25 amol^(-1)*s^(-1)Reaction: NFkB + IkBa => IkBa_NFkB, Rate Law: ka_54*NFkB*IkBa
ka_56=0.0125 s^(-1)Reaction: NFkB => NFkB_N, Rate Law: ka_56*NFkB
ka_52=0.1 s^(-1)Reaction: IKKa => IKK, Rate Law: ka_52*IKKa
ka_32=0.3 s^(-1)Reaction: TNFRC2_FLIP_pCasp8 => TNFRC2 + Casp8, Rate Law: ka_32*TNFRC2_FLIP_pCasp8
ka_38=7.72256E-4 amol*s^(-1); kd_38=1.0E-4 s^(-1)Reaction: => XIAP, Rate Law: ka_38-kd_38*XIAP
ka_86=0.15625 amol^(-1)*s^(-1)Reaction: FLIP => ; Casp3, Rate Law: ka_86*FLIP*Casp3
ka_71=5.78704E-5 s^(-1)Reaction: Casp8 =>, Rate Law: ka_71*Casp8
ka_58=0.0606061 s^(-1)Reaction: => IkBa; IkBa_mRNA, Rate Law: ka_58*IkBa_mRNA
ka_61=0.0151515 s^(-1)Reaction: IkBa_NFkB_N => IkBa_NFkB, Rate Law: ka_61*IkBa_NFkB_N
ka_44=0.00154022 s^(-1)Reaction: IkBa =>, Rate Law: ka_44*IkBa
ka_83=1.875 amol^(-1)*s^(-1)Reaction: XIAP => ; Casp3, Rate Law: ka_83*XIAP*Casp3
ka_34=0.03125 amol^(-1)*s^(-1)Reaction: IKK => IKKa; TNFRC2_FLIP_pCasp8_RIP_TRAF2, Rate Law: ka_34*TNFRC2_FLIP_pCasp8_RIP_TRAF2*IKK
ka_68=1.97531E-4 amol*s^(-1); kd_68=6.17284E-5 s^(-1)Reaction: => pCasp8, Rate Law: ka_68-kd_68*pCasp8
ka_81=0.0015625 amol^(-1)*s^(-1)Reaction: pCasp8 => Casp8; Casp6, Rate Law: ka_81*pCasp8*Casp6
ka_50=1.65744E-4 s^(-1)Reaction: FLIP_mRNA =>, Rate Law: ka_50*FLIP_mRNA
ka_21=0.001135 s^(-1)Reaction: TNFRC1 => TNFRCint1, Rate Law: ka_21*TNFRC1
ka_17=5.6E-5 s^(-1)Reaction: TNFRC2_FLIP_pCasp8_RIP_TRAF2 =>, Rate Law: ka_17*TNFRC2_FLIP_pCasp8_RIP_TRAF2
ka_51=93.75 amol^(-1)*s^(-1)Reaction: IKK => IKKa; TNFRC1, Rate Law: ka_51*TNFRC1*IKK
ka_20=0.0976562 amol^(-2)*s^(-1)Reaction: RIP + TRAF2 + TNF_TNFR_TRADD => TNFRC1, Rate Law: ka_20*RIP*TRAF2*TNF_TNFR_TRADD
ka_15=5.6E-5 s^(-1)Reaction: TNFRC2_pCasp8_pCasp8 =>, Rate Law: ka_15*TNFRC2_pCasp8_pCasp8
ka_67=0.00687273 s^(-1)Reaction: => FLIP; FLIP_mRNA, Rate Law: ka_67*FLIP_mRNA
kd_37=1.0E-4 s^(-1); ka_37=2.24902E-6 amol*s^(-1)Reaction: => FLIP, Rate Law: ka_37-kd_37*FLIP
ka_40=1.0E-4 s^(-1)Reaction: IKKa =>, Rate Law: ka_40*IKKa
ka_39=9.6E-6 amol*s^(-1); kd_39=1.0E-4 s^(-1)Reaction: => A20, Rate Law: ka_39-kd_39*A20
ka_14=5.6E-5 s^(-1)Reaction: TNFRC2_pCasp8 =>, Rate Law: ka_14*TNFRC2_pCasp8

States:

NameDescription
TNFR[Tumor necrosis factor receptor superfamily member 1A]
NFkB N[Nuclear factor NF-kappa-B p105 subunit]
TNFRC2[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein]
TRAF2[TNF receptor-associated factor 2]
XIAP[E3 ubiquitin-protein ligase XIAP]
FLIP mRNA[CFLAR-201]
FADD[FAS-associated death domain protein]
TNFRC2 FLIP[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein; CASP8 and FADD-like apoptosis regulator]
IkBa NFkB[NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit]
TNFRC2 FLIP pCasp8[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein; CASP8 and FADD-like apoptosis regulator; Caspase-8]
A20[Tumor necrosis factor alpha-induced protein 3]
IkBa NFkB N[NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit]
TNFRC1[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; TNF receptor-associated factor 2; Receptor-interacting serine/threonine-protein kinase 1]
IKK[Inhibitor of nuclear factor kappa-B kinase subunit alpha]
TRADD[Tumor necrosis factor receptor type 1-associated DEATH domain protein]
pCasp8[Caspase-8]
IkBa mRNA[NFKBIA-201]
FLIP[CASP8 and FADD-like apoptosis regulator]
TNFRC2 FLIP FLIP[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein; CASP8 and FADD-like apoptosis regulator]
PARP[Poly [ADP-ribose] polymerase 1]
TNF TNFR E[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor]
BAR[Bifunctional apoptosis regulator]
pCasp3[Caspase-3]
A20 mRNA[TNFAIP3-201]
XIAP mRNA[XIAP-202]
TNF E[Tumor necrosis factor]
TNFRCint3[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein]
IKKa[Inhibitor of nuclear factor kappa-B kinase subunit alpha]
TNFRC2 FLIP pCasp8 RIP TRAF2[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein; CASP8 and FADD-like apoptosis regulator; Caspase-8; Receptor-interacting serine/threonine-protein kinase 1; TNF receptor-associated factor 2]
PIkBa[NF-kappa-B inhibitor alpha]
IkBa[NF-kappa-B inhibitor alpha]
TNFR E[Tumor necrosis factor receptor superfamily member 1A]
TNFRCint2[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein]
TNFRCint1[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; TNF receptor-associated factor 2; Receptor-interacting serine/threonine-protein kinase 1]
NFkB[Nuclear factor NF-kappa-B p105 subunit]
cPARP[Poly [ADP-ribose] polymerase 1]
TNFRC2 pCasp8 pCasp8[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein; Caspase-8]
TNFRC2 pCasp8[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein; FAS-associated death domain protein; Caspase-8]
pCasp6[Caspase-6]
TNF TNFR TRADD[Tumor necrosis factor receptor superfamily member 1A; Tumor necrosis factor; Tumor necrosis factor receptor type 1-associated DEATH domain protein]
Casp8[Caspase-8]

Schlosser2000_GlucoseInsulinFeedback_BetaCells: MODEL1006230045v0.0.1

This a model from the article: Modeling insulin kinetics: responses to a single oral glucose administration or ambulat…

Details

Increasing concerns that environmental contaminants may disrupt the endocrine system require development of mathematical tools to predict the potential for such compounds to significantly alter human endocrine function. The endocrine system is largely self-regulating, compensating for moderate changes in dietary phytoestrogens (e.g., in soy products) and normal variations in physiology. However, severe changes in dietary or oral exposures or in health status (e.g., anorexia), can completely disrupt the menstrual cycle in women. Thus, risk assessment tools should account for normal regulation and its limits. We present a mathematical model for the synthesis and release of luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in women as a function of estrogen, progesterone, and inhibin blood levels. The model reproduces the time courses of LH and FSH during the menstrual cycle and correctly predicts observed effects of administered estrogen and progesterone on LH and FSH during clinical studies. The model should be useful for predicting effects of hormonally active substances, both in the pharmaceutical sciences and in toxicology and risk assessment. link: http://identifiers.org/pubmed/11035997

Schmierer2010_FIH_Ankyrins: BIOMD0000000300v0.0.1

This a model from the article: Hypoxia-dependent sequestration of an oxygen sensor by a widespread structural motif…

Details

The activity of the heterodimeric transcription factor hypoxia inducible factor (HIF) is regulated by the post-translational, oxygen-dependent hydroxylation of its α-subunit by members of the prolyl hydroxylase domain (PHD or EGLN)-family and by factor inhibiting HIF (FIH). PHD-dependent hydroxylation targets HIFα for rapid proteasomal degradation; FIH-catalysed asparaginyl-hydroxylation of the C-terminal transactivation domain (CAD) of HIFα suppresses the CAD-dependent subset of the extensive transcriptional responses induced by HIF. FIH can also hydroxylate ankyrin-repeat domain (ARD) proteins, a large group of proteins which are functionally unrelated but share common structural features. Competition by ARD proteins for FIH is hypothesised to affect FIH activity towards HIFα; however the extent of this competition and its effect on the HIF-dependent hypoxic response are unknown.To analyse if and in which way the FIH/ARD protein interaction affects HIF-activity, we created a rate equation model. Our model predicts that an oxygen-regulated sequestration of FIH by ARD proteins significantly shapes the input/output characteristics of the HIF system. The FIH/ARD protein interaction is predicted to create an oxygen threshold for HIFα CAD-hydroxylation and to significantly sharpen the signal/response curves, which not only focuses HIFα CAD-hydroxylation into a defined range of oxygen tensions, but also makes the response ultrasensitive to varying oxygen tensions. Our model further suggests that the hydroxylation status of the ARD protein pool can encode the strength and the duration of a hypoxic episode, which may allow cells to memorise these features for a certain time period after reoxygenation.The FIH/ARD protein interaction has the potential to contribute to oxygen-range finding, can sensitise the response to changes in oxygen levels, and can provide a memory of the strength and the duration of a hypoxic episode. These emergent properties are predicted to significantly shape the characteristics of HIF activity in animal cells. We argue that the FIH/ARD interaction should be taken into account in studies of the effect of pharmacological inhibition of the HIF-hydroxylases and propose that the interaction of a signalling sensor with a large group of proteins might be a general mechanism for the regulation of signalling pathways. link: http://identifiers.org/pubmed/20955552

Parameters:

NameDescription
parameter_14 = 0.2 dimensionlessReaction: species_3 =>, Rate Law: compartment_1*parameter_14*species_3
parameter_17 = 1.0 dimensionlessReaction: species_1 =>, Rate Law: compartment_1*parameter_17*species_1
parameter_4 = 1.0 dimensionlessReaction: species_10 = 0.5*(((species_1-species_8)-parameter_4)+(((parameter_4-species_1)+species_8)^2+4*species_1*parameter_4)^(0.5)), Rate Law: missing
parameter_7 = 101.0 dimensionless; parameter_1 = 0.33 dimensionless; parameter_13 = 500.0 dimensionlessReaction: species_2 => ; species_7, species_11, species_9, Rate Law: compartment_1*species_2*parameter_13*species_7*species_11/(parameter_1+species_11)/(parameter_7+species_7+species_9)
parameter_7 = 101.0 dimensionlessReaction: species_9 = 0.5*(((species_2-species_7)-parameter_7)+(((parameter_7-species_2)+species_7)^2+4*species_2*parameter_7)^(0.5)), Rate Law: missing
parameter_5 = 0.3 dimensionlessReaction: species_14 = species_1/(parameter_5+species_1), Rate Law: missing
parameter_16 = 20.0 dimensionlessReaction: => species_3, Rate Law: compartment_1*parameter_16
parameter_7 = 101.0 dimensionless; parameter_2 = 1.0 dimensionlessReaction: species_12 = (parameter_2+species_9)/(parameter_7+species_9), Rate Law: missing
parameter_1 = 0.33 dimensionless; parameter_9 = 1.0 dimensionless; parameter_13 = 500.0 dimensionless; parameter_6 = 0.0 dimensionlessReaction: species_3 => ; species_7, species_11, species_5, Rate Law: compartment_1*species_3*parameter_13*species_7*species_11/(parameter_1+species_11)/(parameter_9+species_3+parameter_6*(species_5-species_3))
parameter_8 = 500.0 dimensionless; parameter_4 = 1.0 dimensionlessReaction: species_2 => ; species_8, species_11, species_10, Rate Law: compartment_1*species_2*parameter_8*species_8*species_11/(1+species_11)/(parameter_4+species_8+species_10)
parameter_18 = 1.0 dimensionlessReaction: => species_1, Rate Law: compartment_1*parameter_18

States:

NameDescription
species 9HF
species 2[Endothelial PAS domain-containing protein 1; Hypoxia-inducible factor 1-alpha; Hypoxia-inducible factor 3-alpha]
species 6[Ankyrin-1]
species 10HP
species 1[Endothelial PAS domain-containing protein 1; Hypoxia-inducible factor 1-alpha; Hypoxia-inducible factor 3-alpha]
species 4[Hypoxia-inducible factor 1-alpha; Endothelial PAS domain-containing protein 1; Hypoxia-inducible factor 3-alpha]
species 16A for plotting
species 14NAD
species 3[Ankyrin-1]
species 12FIHfree
species 15CADOH
species 13CAD

Schmierer_2008_Smad_Tgfb: BIOMD0000000173v0.0.1

This sbml file describes the RECI model from: "Mathematical modeling identifies Smad nucleocytoplasmic…

Details

TGF-beta-induced Smad signal transduction from the membrane into the nucleus is not linear and unidirectional, but rather a dynamic network that couples Smad phosphorylation and dephosphorylation through continuous nucleocytoplasmic shuttling of Smads. To understand the quantitative behavior of this network, we have developed a tightly constrained computational model, exploiting the interplay between mathematical modeling and experimental strategies. The model simultaneously reproduces four distinct datasets with excellent accuracy and provides mechanistic insights into how the network operates. We use the model to make predictions about the outcome of fluorescence recovery after photobleaching experiments and the behavior of a functionally impaired Smad2 mutant, which we then verify experimentally. Successful model performance strongly supports the hypothesis of a dynamic maintenance of Smad nuclear accumulation during active signaling. The presented work establishes Smad nucleocytoplasmic shuttling as a dynamic network that flexibly transmits quantitative features of the extracellular TGF-beta signal, such as its duration and intensity, into the nucleus. link: http://identifiers.org/pubmed/18443295

Parameters:

NameDescription
k_TGFb = 0.07423555020288 pernMpersecondReaction: R + TGFb_c => R_act, Rate Law: cytosol*k_TGFb*R*TGFb_c
kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecondReaction: pS2_n + S4_n => S24_n, Rate Law: nucleus*(kon*pS2_n*S4_n-koff*S24_n)
koff_SB = 100.0 persecond; kon_SB = 0.146422317103884 pernMpersecondReaction: R_act + SB => R_inact, Rate Law: cytosol*(kon_SB*R_act*SB-koff_SB*R_inact)
kin_CIF = 3.36347821E-14 litrepersecondReaction: S24_c => S24_n, Rate Law: kin_CIF*S24_c
kin = 5.93E-15 litrepersecondReaction: S4_c => S4_n, Rate Law: kin*S4_c-kin*S4_n
kdephos = 0.00656639 pernMpersecondReaction: pS2_n + PPase => S2_n + PPase, Rate Law: nucleus*kdephos*pS2_n*PPase
kphos = 4.037081673984E-4 pernMpersecondReaction: R_act + S2_c => R_act + pS2_c, Rate Law: cytosol*kphos*R_act*S2_c
SB_add = 10000.0 nM; t_SB = 2700.0 Predefined unit time; SB_0 = 0.0 nMReaction: SB = piecewise(SB_add, time > t_SB, SB_0), Rate Law: missing
kex = 1.26E-14 litrepersecond; kin = 5.93E-15 litrepersecondReaction: pG_c => pG_n, Rate Law: kin*pG_c-kex*pG_n

States:

NameDescription
G4 c[protein complex; Mothers against decapentaplegic homolog 2; Mothers against decapentaplegic homolog 4; IPR000786; Phosphoprotein]
G n[Mothers against decapentaplegic homolog 2; IPR000786]
S22 n[protein complex; Mothers against decapentaplegic homolog 2; Phosphoprotein]
G4 n[Mothers against decapentaplegic homolog 2; Mothers against decapentaplegic homolog 4; protein complex; IPR000786; Phosphoprotein]
PPase[Protein phosphatase 1A; phosphoprotein phosphatase activity]
S2 c[Mothers against decapentaplegic homolog 2]
TGFb c[Transforming growth factor beta-1; Transforming growth factor beta-2; Transforming growth factor beta-3]
pS2 c[Mothers against decapentaplegic homolog 2; Phosphoprotein]
pG c[Mothers against decapentaplegic homolog 2; Phosphoprotein; IPR000786]
R inact[TGF-beta receptor type-1; Receptor protein serine/threonine kinase; Activin receptor type-1C; TGF-beta receptor type-2]
G c[Mothers against decapentaplegic homolog 2; IPR000786]
pG n[Mothers against decapentaplegic homolog 2; Phosphoprotein; IPR000786]
SB[protein serine/threonine kinase inhibitor activity]
G2 n[protein complex; Mothers against decapentaplegic homolog 2; IPR000786; Phosphoprotein]
S22 c[protein complex; Mothers against decapentaplegic homolog 2; Phosphoprotein]
S24 c[protein complex; Mothers against decapentaplegic homolog 2; Mothers against decapentaplegic homolog 4; Phosphoprotein]
S4 c[Mothers against decapentaplegic homolog 4]
GG n[Mothers against decapentaplegic homolog 2; protein complex; IPR000786; Phosphoprotein]
pS2 n[Mothers against decapentaplegic homolog 2; Phosphoprotein]
G2 c[protein complex; Mothers against decapentaplegic homolog 2; IPR000786; Phosphoprotein]
R act[TGF-beta receptor type-1; Activin receptor type-1C; Receptor protein serine/threonine kinase; TGF-beta receptor type-2]
GG c[protein complex; Mothers against decapentaplegic homolog 2; IPR000786; Phosphoprotein]
S24 n[protein complex; Mothers against decapentaplegic homolog 2; Mothers against decapentaplegic homolog 4; Phosphoprotein]
S2 n[Mothers against decapentaplegic homolog 2]
S4 n[Mothers against decapentaplegic homolog 4]
R[TGF-beta receptor type-1; Receptor protein serine/threonine kinase; Activin receptor type-1C; TGF-beta receptor type-2]

Schmitz2014 - RNA triplex formation: BIOMD0000000530v0.0.1

Schmitz2014 - RNA triplex formationThe model is parameterized using the parameters for gene CCDC3 from Supplementary Tab…

Details

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/. link: http://identifiers.org/pubmed/24875477

Parameters:

NameDescription
k1=1.0Reaction: species_2 => ; species_2, Rate Law: compartment_1*k1*species_2
k1=4.5298E-4Reaction: species_1 + species_2 => species_4; species_1, species_2, Rate Law: compartment_1*k1*species_1*species_2
k_syn_miRNA_1=1.0Reaction: => species_2; species_8, species_8, Rate Law: compartment_1*k_syn_miRNA_1*species_8
k_syn_mRNA=1.0Reaction: => species_1; species_7, species_7, Rate Law: compartment_1*k_syn_mRNA*species_7
k1=0.187796Reaction: species_6 => species_2 + species_3 + species_1; species_6, Rate Law: compartment_1*k1*species_6
k_syn_prot=1.0Reaction: => species_10; species_1, species_1, Rate Law: compartment_1*k_syn_prot*species_1
k1=0.241033Reaction: species_5 => species_3 + species_1; species_5, Rate Law: compartment_1*k1*species_5
k1=1.30837E-5Reaction: species_1 + species_3 => species_5; species_1, species_3, Rate Law: compartment_1*k1*species_1*species_3
k1=0.249955Reaction: species_4 => species_2 + species_1; species_4, Rate Law: compartment_1*k1*species_4
k1=0.999534Reaction: species_1 + species_2 + species_3 => species_6; species_1, species_2, species_3, Rate Law: compartment_1*k1*species_1*species_2*species_3
k_syn_miRNA_2=1.0Reaction: => species_3; species_9, species_9, Rate Law: compartment_1*k_syn_miRNA_2*species_9

States:

NameDescription
species 2[MI0003575; SBO:0000316]
species 6[CCDC3; MI0003575; MI0000476]
species 10[CCDC3]
species 3[MI0000476; SBO:0000316]
species 1[CCDC3; messenger RNA]
species 4[CCDC3; MI0003575]
species 5[CCDC3; MI0000476]

Schneider2006_CardiacContractionModel: MODEL7907879432v0.0.1

This a model from the article: Mechanism of the Frank-Starling law--a simulation study with a novel cardiac muscle con…

Details

A stretch-induced increase of active tension is one of the most important properties of the heart, known as the Frank-Starling law. Although a variation of myofilament Ca(2+) sensitivity with sarcomere length (SL) change was found to be involved, the underlying molecular mechanisms are not fully clarified. Some recent experimental studies indicate that a reduction of the lattice spacing between thin and thick filaments, through the increase of passive tension caused by the sarcomeric protein titin with an increase in SL within the physiological range, promotes formation of force-generating crossbridges (Xbs). However, the mechanism by which the Xb concentration determines the degree of cooperativity for a given SL has so far evaded experimental elucidation. In this simulation study, a novel, rather simple molecular-based cardiac contraction model, appropriate for integration into a ventricular cell model, was designed, being the first model to introduce experimental data on titin-based radial tension to account for the SL-dependent modulation of the interfilament lattice spacing and to include a conformational change of troponin I (TnI). Simulation results for the isometric twitch contraction time course, the length-tension and the force-[Ca(2+)] relationships are comparable to experimental data. A complete potential Frank-Starling mechanism was analyzed by this simulation study. The SL-dependent modulation of the myosin binding rate through titin's passive tension determines the Xb concentration which then alters the degree of positive cooperativity affecting the rate of the TnI conformation change and causing the Hill coefficient to be SL-dependent. link: http://identifiers.org/pubmed/16860336

Schoeberl2002 - EGF MAPK: BIOMD0000000019v0.0.1

Schoeberl2002 - EGF MAPK Computational model that offers an integrated quantitative, dynamic, and topological represent…

Details

We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos. link: http://identifiers.org/pubmed/11923843

Parameters:

NameDescription
kr56 = 36.0 permin; k56 = 0.00145 peritemperminReaction: x59 + x60 => x61, Rate Law: k56*x59*x60-kr56*x61
k50 = 2.5E-5 peritempermin; kr50 = 30.0 perminReaction: x53 + x75 => x79, Rate Law: k50*x53*x75-kr50*x79
k18 = 0.0015 peritempermin; kr18 = 78.0 perminReaction: x26 + x66 => x67, Rate Law: k18*x26*x66-kr18*x67
k25 = 0.001 peritempermin; kr25 = 1.284 perminReaction: x24 + x65 => x66, Rate Law: k25*x24*x65-kr25*x66
k48 = 0.00143 peritempermin; kr48 = 48.0 perminReaction: x77 + x53 => x78, Rate Law: k48*x77*x53-kr48*x78
k59 = 18.0 perminReaction: x85 => x55 + x60, Rate Law: k59*x85
kr32 = 2.4E-5 peritempermin; k32 = 6.0 perminReaction: x66 => x17 + x38, Rate Law: k32*x66-kr32*x17*x38
kr41 = 2.574 permin; k41 = 0.003 peritemperminReaction: x30 + x33 => x35, Rate Law: k41*x30*x33-kr41*x35
k20 = 2.1E-4 peritempermin; kr20 = 24.0 perminReaction: x35 + x43 => x37, Rate Law: k20*x35*x43-kr20*x37
kr4 = 0.0996 permin; k4 = 1.038E-5 peritemperminReaction: x36 + x12 => x93, Rate Law: k4*x36*x12-kr4*x93
k5 = NaN perminReaction: x93 => x9 + x67, Rate Law: k5*x93
k29 = 60.0 permin; kr29 = 7.0E-5 peritemperminReaction: x70 => x71 + x72, Rate Law: k29*x70-kr29*x71*x72
k57 = 16.2 perminReaction: x84 => x81 + x60, Rate Law: k57*x84
k47 = 174.0 perminReaction: x76 => x72 + x77, Rate Law: k47*x76
kr58 = 30.0 permin; k58 = 5.0E-4 peritemperminReaction: x60 + x81 => x85, Rate Law: k58*x60*x81-kr58*x85
k6 = 0.003 permin; kr6 = 0.3 perminReaction: x2 => x6, Rate Law: k6*x2-kr6*x6
kr2 = 6.0 permin; k2 = 0.001 peritemperminReaction: x10 => x11, Rate Law: k2*x10*x10-kr2*x11
k15 = 600000.0 perminReaction: x9 => x12, Rate Law: k15*x9
k10 = 3.25581 peritempermin; kr10 = 0.66 perminReaction: x6 + x16 => x10, Rate Law: k10*x6*x16-kr10*x10
k44 = 0.00111 peritempermin; kr44 = 1.0998 perminReaction: x47 + x72 => x74, Rate Law: k44*x47*x72-kr44*x74
k45 = 210.0 perminReaction: x74 => x75 + x72, Rate Law: k45*x74
kr3 = 0.6 permin; k3 = 60.0 perminReaction: x11 => x8, Rate Law: k3*x11-kr3*x8
k49 = 3.48 perminReaction: x78 => x75 + x53, Rate Law: k49*x78
k55 = 342.0 perminReaction: x82 => x83 + x77, Rate Law: k55*x82
k60 = 0.04002 perminReaction: x6 => x86, Rate Law: k60*x6
k19 = 30.0 permin; kr19 = 1.0E-5 peritemperminReaction: x36 => x35 + x28, Rate Law: k19*x36-kr19*x35*x28
k42 = 0.0071 peritempermin; kr42 = 12.0 perminReaction: x44 + x72 => x73, Rate Law: k42*x44*x72-kr42*x73
k53 = 960.0 perminReaction: x80 => x81 + x77, Rate Law: k53*x80
k52 = 0.00534 peritempermin; kr52 = 1.98 perminReaction: x77 + x81 => x82, Rate Law: k52*x77*x81-kr52*x82
k21 = 1.38 permin; kr21 = 2.2E-5 peritemperminReaction: x37 => x35 + x26, Rate Law: k21*x37-kr21*x35*x26

States:

NameDescription
x72Rafi*
x85ERKi-P-P'ase3i
x80ERKi-MEKi-PP
x67EGF-EGFRi*^2-GAP-Shc*-Grb2-Sos-Ras-GDP
x89EGF-EGFR*^2-GAP-Grb2-Sos-Ras-GDP-Prot
x86EGFRideg
x79MEKi-P-P'ase2i
x62ERK-P-P'ase3
Ras GTPt_Ras_GTP
x45Raf*
x66EGF-EGFRi*^2-GAP-Shc*-Grb2-Sos
x78MEKi-PP-P'ase2i
x75MEKi-P
x91EGF-EGFR*^2-GAP-Shc*-Grb2-Prot
x56ERK-MEK-PP
x59ERK-PP
x77MEKi-PP
x12Prot
x74MEKi-Rafi*
x44Phosphotase1
x61ERK-PP-P'ase3
Raf actt_Raf*
x47[Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase kinase 1Mitogen-activated protein kinase kinase 1, isoform CRA_acDNA FLJ76051, highly similar to Homo sapiens mitogen-activated protein kinase kinase 1 (MAP2K1), mRNA; 176872]
x9[AP-type membrane coat adaptor complex]
x8[Epidermal growth factor receptor; Pro-epidermal growth factor]
x69Rasi-GTP
x81ERKi-P
x35EGF-EGFR*^2-GAP-Shc*-Grb2-Sos
x43Ras-GTP*
x71Rasi-GTP*
x36EGF-EGFR*^2-GAP-Shc*-Grb2-Sos-Ras-GDP
x54MEK-P-P'ase2
x6[Epidermal growth factor receptor]
x87EGF-EGFRi*^2deg
x60Phosphotase3
x58ERK-P-MEK-PP
x84ERKi-PP-P'ase3i
SHC P tt_SHC_P_t
x88EGF-EGFR*^2-GAP-Grb2-Sos-Prot
x51MEK-PP
x11EGF-EGFRi^2
x94EGF-EGFR*^2-GAP-Shc*-Grb2-Sos-Ras-GTP-Prot
x53Phosphatase2
x37EGF-EGFR*^2-GAP-Shc*-Grb2-Sos-Ras-GTP
x63EGF-EGFRi*^2-GAP-Shc
x57ERK-P
x55[Mitogen-activated protein kinase 1; 176948]
x48MEK-Raf*
x82ERKi-P-MEKi-PP
x73Rafi*-P'ase
x52MEK-PP-P'ase2
x70Rafi-Rasi-GTP
x7[Ras GTPase-activating protein 1; Growth factor receptor-bound protein 2; Epidermal growth factor receptor; Pro-epidermal growth factor; AP-type membrane coat adaptor complex]
x49[Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase kinase 1Mitogen-activated protein kinase kinase 1, isoform CRA_acDNA FLJ76051, highly similar to Homo sapiens mitogen-activated protein kinase kinase 1 (MAP2K1), mRNA; Phosphoprotein; 176872]
x10[Pro-epidermal growth factor; Epidermal growth factor receptor]
x65EGF-EGFRi*^2-GAP-Shc*-Grb2
x76MEKi-P-Rafi*

Schokker2013 - A mathematical model representing cellular immune development and response to Salmonella of chicken intestinal tissue: BIOMD0000000895v0.0.1

This is a dynamic mathematical model describing the development of the cellular branch of the intestinal immune system o…

Details

The aim of this study was to create a dynamic mathematical model of the development of the cellular branch of the intestinal immune system of poultry during the first 42 days of life and of its response towards an oral infection with Salmonella enterica serovar Enteritidis. The system elements were grouped in five important classes consisting of intra- and extracellular S. Enteritidis bacteria, macrophages, CD4+, and CD8+ cells. Twelve model variables were described by ordinary differential equations, including 50 parameters. Parameter values were estimated from literature or from own immunohistochemistry data. The model described the immune development in non-infected birds with an average R² of 0.87. The model showed less accuracy in reproducing the immune response to S. Enteritidis infection, with an average R² of 0.51, although model response did follow observed trends in time. Evaluation of the model against independent data derived from several infection trials showed strong/significant deviations from observed values. Nevertheless, it was shown that the model could be used to simulate the effect of varying input parameters on system elements response, such as the number of immune cells at hatch. Model simulations allowed one to study the sensitivity of the model outcome for varying model inputs. The initial number of immune cells at hatch was shown to have a profound impact on the predicted development in the number of systemic S. Enteritidis bacteria after infection. The theoretical contribution of this work is the identification of responses in system elements of the developing intestinal immune system of poultry obtaining a mathematical representation which allows one to explore the relationships between these elements under contrasting environmental conditions during different stages of intestinal development. link: http://identifiers.org/pubmed/23603730

Parameters:

NameDescription
iMr = 0.1; cSeMr = 1.0Reaction: => Si; Mr, Rate Law: compartment*iMr*Mr*Si/(Si+cSeMr)
vrecMr = 1.0; kmrecMr = 1000.0Reaction: Mrrec => Mr; Se, Rate Law: compartment*Mrrec*vrecMr*Se/(Se+kmrecMr)
kSedCD4 = 4200.0; CD4n = 0.4; ndCD4 = 8.0Reaction: CD4 => ; Se, Rate Law: compartment*CD4n*CD4*Se^ndCD4/(Se^ndCD4+kSedCD4^ndCD4)
cc1CD8 = 1.3E7; gbCD8 = 1.44; k2CD8 = 4.7E7Reaction: => CD8, Rate Law: compartment*gbCD8*CD8*(1-CD8/cc1CD8)*CD8/(CD8+k2CD8)
drCD4 = 0.016Reaction: CD4 =>, Rate Law: compartment*drCD4*CD4
kSeMa = 2.6E-7Reaction: Ma + Se =>, Rate Law: compartment*kSeMa*Ma*Se
sCD8 = 430000.0Reaction: => CD8, Rate Law: compartment*sCD8
drSe = 27.8Reaction: Se =>, Rate Law: compartment*drSe*Se
lMi = 0.8; cCD4CD8 = 10.0Reaction: Si => ; CD4, CD8, Mi, Rate Law: compartment*lMi*(CD4+CD8/Mi)/(CD4+CD8/Mi+cCD4CD8)
sCD4 = 490000.0Reaction: => CD4, Rate Law: compartment*sCD4
aMr = 100.0; cSeMr = 1.0Reaction: Mr => Ma; Se, Rate Law: compartment*aMr*Mr*Se/(Se+cSeMr)
pSe = 35.0; ccSe = 500000.0Reaction: => Se, Rate Law: compartment*pSe*Se*(1-Se/ccSe)
drMi = 0.011Reaction: Mi =>, Rate Law: compartment*drMi*Mi
sMr = 300000.0Reaction: => Mr, Rate Law: compartment*sMr
apop = 0.7; N = 30.0; lMi = 0.8; cCD4CD8 = 10.0Reaction: Mi => ; CD4, CD8, Si, Rate Law: compartment*(1-apop*Si/(Si+N+Mi))*lMi*(CD4+CD8/Mi)/(CD4+CD8/Mi+cCD4CD8)
cdaMa = 3.0E7; daMa = 40.0Reaction: Ma => Mr; CD4, Rate Law: compartment*daMa*Ma*CD4/(CD4+cdaMa)
kSeCD4 = 1.0E-9Reaction: CD4 + Se =>, Rate Law: compartment*kSeCD4*CD4*Se
kmrecCD4 = 1.0; vrecCD4 = 100.0Reaction: CD4rec => CD4; Se, Rate Law: compartment*CD4rec*vrecCD4*Se/(Se+kmrecCD4)
gbCD4 = 0.19; cc1CD4 = 8.2E7; ngbCD4 = 2.0; k2CD4 = 8700000.0Reaction: => CD4, Rate Law: compartment*gbCD4*CD4*(1-CD4/cc1CD4)*CD4^ngbCD4/(CD4^ngbCD4+k2CD4^ngbCD4)
iMr = 0.1; cSeMri = 600000.0Reaction: Mr + Se => Mi, Rate Law: compartment*iMr*Mr*Se/(Se+cSeMri)
N = 30.0; pSi = 4.1Reaction: => Si; Mi, Rate Law: compartment*pSi*Si*(1-Si/(Si+N*Mi))
gbMr = 1.2; ccMr = 2.5E7; p1 = 0.65Reaction: => Mr, Rate Law: compartment*gbMr*Mr*(1-Mr/(ccMr-ccMr*p1))
drSi = 0.05Reaction: Si =>, Rate Law: compartment*drSi*Si
bMi = 0.4; N = 30.0; mMi = 2.0Reaction: Si + Mi => Se, Rate Law: compartment*bMi*Mi*Si^mMi/(Si^mMi+(N*Mi)^mMi)
drMr = 0.011Reaction: Mr =>, Rate Law: compartment*drMr*Mr
drMa = 0.08Reaction: Ma =>, Rate Law: compartment*drMa*Ma
drCD8 = 0.001Reaction: CD8 =>, Rate Law: compartment*drCD8*CD8
kSeMr = 5.0E-8Reaction: Mr + Se =>, Rate Law: compartment*kSeMr*Mr*Se
compCD8 = 0.85; kcompCD4 = 3.4E7; w1 = 1.0E-25; ncompCD4 = 0.5Reaction: CD8 => ; Se, CD4, Rate Law: compartment*compCD8*CD8*Se/(Se+w1)*CD4^ncompCD4/(CD4^ncompCD4+kcompCD4^ncompCD4)

States:

NameDescription
CD4[CD4-positive helper T cell]
Mrrec[C12558]
CD8[CD8-Positive T-Lymphocyte]
Si[C76380; C28217]
Ma[inflammatory macrophage]
Mi[C12558; infected cell]
Se[C76380; extracellular region]
Mr[CL:0000864]
CD4rec[CD4-positive helper T cell]

Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies: BIOMD0000000788v0.0.1

This model presents a general target-mediated drug disposition (TMDD) model for bispecific antibodies (BsAbs), which bin…

Details

Bispecific antibodies (BsAbs) bind to two different targets, and create two binary and one ternary complex (TC). These molecules have shown promise as immuno-oncology drugs, and the TC is considered the pharmacologically active species that drives their pharmacodynamic effect. Here, we have presented a general target-mediated drug disposition (TMDD) model for these BsAbs, which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters and, if necessary, reduced internalization parameters is presented. The model is further used to investigate the kinetics of BsAb and TC concentrations. Our analysis shows that larger doses of BsAbs may delay the build-up of the TC. Consequently, a method to compute the optimal dosing strategy of BsAbs, which will immediately create and maintain maximal possible TC concentration, is presented. link: http://identifiers.org/pubmed/30480383

Parameters:

NameDescription
k_deg_A = 0.1 1/msReaction: R_A =>, Rate Law: Central*k_deg_A*R_A
k_on_2 = 1.0 ml/(mol*s)Reaction: C_free + R_B => RC_B, Rate Law: Central*k_on_2*C_free*R_B
k_21 = 0.03 1/ms; V = 3.0 lReaction: => C_free; AP, Rate Law: k_21*AP/V
k_on_3 = 1.0 ml/(mol*s)Reaction: RC_A + R_B => RC_AB, Rate Law: Central*k_on_3*RC_A*R_B
k_off_3 = 0.01 1/msReaction: => RC_B + R_B; RC_AB, Rate Law: Central*k_off_3*RC_AB
k_12 = 0.1 1/msReaction: C_free =>, Rate Law: Central*k_12*C_free
k_el = 0.1 1/msReaction: C_free =>, Rate Law: Central*k_el*C_free
k_off_3 = 0.01 1/ms; k_off_4 = 0.01 1/ms; k_int_AB = 0.1 1/msReaction: RC_AB =>, Rate Law: Central*(k_off_3+k_off_4+k_int_AB)*RC_AB
k_int_B = 0.05 1/ms; k_off_2 = 0.01 1/msReaction: RC_B =>, Rate Law: Central*(k_off_2+k_int_B)*RC_B
k_12 = 0.1 1/ms; V = 3.0 lReaction: => AP; C_free, Rate Law: k_12*C_free*V
k_syn_A = 1.0 ml/(mol*s)Reaction: => R_A, Rate Law: Central*k_syn_A
k_off_1 = 0.01 1/msReaction: => C_free + R_A; RC_A, Rate Law: Central*k_off_1*RC_A
k_off_1 = 0.01 1/ms; k_int_A = 0.05 1/msReaction: RC_A =>, Rate Law: Central*(k_off_1+k_int_A)*RC_A
V = 3.0 l; k_a = 0.2 1/msReaction: => C_free; AD, Rate Law: k_a*AD*V
k_syn_B = 10.0 ml/(mol*s)Reaction: => R_B, Rate Law: Central*k_syn_B
k_21 = 0.03 1/msReaction: AP =>, Rate Law: Peripheral*k_21*AP
k_deg_B = 0.1 1/msReaction: R_B =>, Rate Law: Central*k_deg_B*R_B
k_off_2 = 0.01 1/msReaction: => C_free + R_B; RC_B, Rate Law: Central*k_off_2*RC_B
k_off_4 = 0.01 1/msReaction: => RC_A + R_A; RC_AB, Rate Law: Central*k_off_4*RC_AB
k_on_1 = 10.0 ml/(mol*s)Reaction: C_free + R_A => RC_A, Rate Law: Central*k_on_1*C_free*R_A
k_a = 0.2 1/msReaction: AD =>, Rate Law: Peripheral*k_a*AD
k_on_4 = 10.0 ml/(mol*s)Reaction: RC_B + R_A => RC_AB, Rate Law: Central*k_on_4*RC_B*R_A

States:

NameDescription
RC A[Receptor; Bispecific Monoclonal Antibody; macromolecular complex]
AP[Bispecific Antibody; Bispecific Monoclonal Antibody; peripheral blood]
C free[Bispecific Monoclonal Antibody]
RC AB[Receptor; Bispecific Monoclonal Antibody; macromolecular complex]
RC B[Receptor; Bispecific Monoclonal Antibody; macromolecular complex]
AD[Bispecific Antibody; Bispecific Monoclonal Antibody; Subcutaneous Route of Administration]
R B[Receptor]
R A[Receptor]

Schuetz2007_ecoli_FBA: MODEL4665428627v0.0.1

This is a stoichiometric map from the supplement of the publication: **Systematic evaluation of objective functions fo…

Details

To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict (13)C-determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states. link: http://identifiers.org/pubmed/17625511

Schulz2009_Th1_differentiation: BIOMD0000000215v0.0.1

This a model from the article: Sequential polarization and imprinting of type 1 T helper lymphocytes by interferon-g…

Details

Differentiation of naive T lymphocytes into type I T helper (Th1) cells requires interferon-gamma and interleukin-12. It is puzzling that interferon-gamma induces the Th1 transcription factor T-bet, whereas interleukin-12 mediates Th1 cell lineage differentiation. We use mathematical modeling to analyze the expression kinetics of T-bet, interferon-gamma, and the IL-12 receptor beta2 chain (IL-12Rbeta2) during Th1 cell differentiation, in the presence or absence of interleukin-12 or interferon-gamma signaling. We show that interferon-gamma induced initial T-bet expression, whereas IL-12Rbeta2 was repressed by T cell receptor (TCR) signaling. The termination of TCR signaling permitted upregulation of IL-12Rbeta2 by T-bet and interleukin-12 signaling that maintained T-bet expression. This late expression of T-bet, accompanied by the upregulation of the transcription factors Runx3 and Hlx, was required to imprint the Th cell for interferon-gamma re-expression. Thus initial polarization and subsequent imprinting of Th1 cells are mediated by interlinked, sequentially acting positive feedback loops of TCR-interferon-gamma-Stat1-T-bet and interleukin-12-Stat4-T-bet signaling. link: http://identifiers.org/pubmed/19409816

Parameters:

NameDescription
gamma_Tbet=1.0Reaction: Tbet_mRNA =>, Rate Law: compartment*gamma_Tbet*Tbet_mRNA
delta_Tbet=0.1Reaction: Tbet_Prot =>, Rate Law: compartment*delta_Tbet*Tbet_Prot
gamma_Rec=1.0Reaction: Rec_mRNA =>, Rate Law: compartment*gamma_Rec*Rec_mRNA
a1=0.044Reaction: => Tbet_mRNA, Rate Law: compartment*a1
gamma_IFN=1.0Reaction: Ifn_mRNA =>, Rate Law: compartment*gamma_IFN*Ifn_mRNA
b=100.0Reaction: => Tbet_Prot; Tbet_mRNA, Rate Law: compartment*b*Tbet_mRNA
K5=0.029; K7=0.014; K6=66.0; a5=3.7Reaction: => Ifn_mRNA; Tbet_Prot, Rec_Prot, Ag, Rate Law: compartment*a5*Tbet_Prot/(K5+Tbet_Prot)*Rec_Prot/(K6+Rec_Prot)*Ag/(K7+Ag)
delta_IFN=1.0Reaction: Ifn_Prot =>, Rate Law: compartment*delta_IFN*Ifn_Prot
a4=0.0028; K4=0.013Reaction: => Rec_mRNA; Tbet_Prot, Ag, Rate Law: compartment*a4*Tbet_Prot*K4/(K4+Ag)
K1=0.46; K2=2.1; a2=0.42Reaction: => Tbet_mRNA; Ag, Ifn_Prot, Rate Law: compartment*a2*Ag/(K1+Ag)*Ifn_Prot/(K2+Ifn_Prot)
a3=5.1E-4Reaction: => Tbet_mRNA; Rec_Prot, Rate Law: compartment*a3*Rec_Prot
delta_Rec=0.1Reaction: Rec_Prot =>, Rate Law: compartment*delta_Rec*Rec_Prot

States:

NameDescription
Ifn Prot[Interferon gamma]
Tbet Prot[T-box transcription factor TBX21]
Ag[positive regulation of T cell receptor signaling pathway]
Ifn mRNA[messenger RNA; RNA; Interferon gamma]
Rec mRNA[Interleukin-12 receptor subunit beta-2; messenger RNA; RNA]
Tbet mRNA[messenger RNA; RNA; T-box transcription factor TBX21]
Rec Prot[Interleukin-12 receptor subunit beta-2]

Schwarz2018-Cdk Activity Threshold Determines Passage through the Restriction Point: BIOMD0000000918v0.0.1

At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is…

Details

At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is passed when growth factor signaling initiates a positive feedback loop of Cdk activity. However, recent studies have cast doubt on this model by claiming R occurs prior to positive feedback activation in G1 or even before completion of the previous cell cycle. Here we reconcile these results and show that whereas many commonly used cell lines do not exhibit a G1 R, primary fibroblasts have a G1 R that is defined by a precise Cdk activity threshold and the activation of cell-cycle-dependent transcription. A simple threshold model, based solely on Cdk activity, predicted with more than 95% accuracy whether individual cells had passed R. That a single measurement accurately predicted cell fate shows that the state of complex regulatory networks can be assessed using a few critical protein activities. link: http://identifiers.org/pubmed/29351845

Parameters:

NameDescription
kR = 0.18; kP1 = 18.0; kP2 = 18.0; KCD = 0.92; KCE = 0.92; kDP = 3.6; dR = 0.06; kRE = 180.0; KRP = 0.01Reaction: => Rb; Phosphorylated_Rb, E2F, CycD, CycE, Rate Law: Cell*(((((kR+kDP*Phosphorylated_Rb/(KRP+Phosphorylated_Rb))-kRE*Rb*E2F)-kP1*CycD*Rb/(KCD+Rb))-kP2*CycE*Rb/(KCE+Rb))-dR*Rb)
kP1 = 18.0; kP2 = 18.0; dRP = 0.06; KCD = 0.92; KCE = 0.92Reaction: => Phosphorylated_Rb; CycD, Rb, CycE, Rb_E2F_complex, Rate Law: Cell*((kP1*CycD*Rb/(KCD+Rb)+kP2*CycE*Rb/(KCE+Rb_E2F_complex)+kP1*CycD*Rb_E2F_complex/(KCD+Rb_E2F_complex)+kP2*CycE*Rb_E2F_complex/(KCE+Rb_E2F_complex))-dRP*Phosphorylated_Rb)
kP1 = 18.0; dE = 0.25; kb = 0.003; kpfb = 4.0; kP2 = 18.0; KCD = 0.92; KCE = 0.92; kE = 0.4; KM = 0.15; KE = 0.15; kRE = 180.0Reaction: => E2F; Myc, CycD, Rb_E2F_complex, CycE, Rb, Rate Law: Cell*(((kE*(kpfb+Myc/(KM+Myc))*E2F/(KE+E2F)+kb*Myc/(KM+Myc)+kP1*CycD*Rb_E2F_complex/(KCD+Rb_E2F_complex)+kP2*CycE*Rb_E2F_complex/(KCE+Rb_E2F_complex))-dE*E2F)-kRE*Rb*E2F)
kP1 = 18.0; kP2 = 18.0; dRE = 0.03; KCD = 0.92; KCE = 0.92; kRE = 180.0Reaction: => Rb_E2F_complex; CycD, CycE, Rate Law: Cell*(((kRE-kP1*CycD*Rb_E2F_complex/(KCD+Rb_E2F_complex))+kP2*CycE*Rb_E2F_complex/(KCE+Rb_E2F_complex))-dRE*Rb_E2F_complex)
kM = 1.0; dM = 0.7; KS = 0.5Reaction: => Myc; serum, Rate Law: Cell*(kM*Myc/(KS+serum)-dM*Myc)
kCD = 0.03; KM = 0.15; kCDS = 0.45; dCD = 1.5; KS = 0.5Reaction: => CycD; Myc, serum, Rate Law: Cell*((kCD*Myc/(KM+Myc)+kCDS*serum/(KS+serum))-dCD*CycD)
kCE = 0.35; dCE = 1.5; KE = 0.15Reaction: => CycE; E2F, Rate Law: Cell*(kCE*E2F/(KE+E2F)-dCE*CycE)

States:

NameDescription
E2F[C129647]
CycE[C104197]
CycD[C104194]
Myc[C18538]
Rb[0016708]
Phosphorylated Rb[0016708; phosphorylated]
Rb E2F complex[Rb-E2F complex]

Schölzel2021 - Modelica implementation of the one-dimensional model of the rabbit atrioventricular node in Inada2009 (InaMo): MODEL2102090002v0.0.1

This project contains a reusable, reproducible, understandable, and extensible reimplementation of the one-dimensional m…

Details

One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model demonstrates that even otherwise rigorous studies can be hard to reproduce due to missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing rationales behind equations. Many of these issues seem similar to problems that have been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow is independent of the model and can be translated to SBML, CellML, and other languages. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. We found the additional design and documentation effort well justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature. link: http://identifiers.org/doi/10.1371/journal.pone.0254749

Sedaghat2002_InsulinSignalling_noFeedback: BIOMD0000000137v0.0.1

Model reproduces the various plots in Figure 6 and 7 of the paper. It was successfully tested on MathSBML. To the exten…

Details

We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-zeta is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling. link: http://identifiers.org/pubmed/12376338

Parameters:

NameDescription
kminus4prime = 2.1E-4; k4prime = 0.0021Reaction: x5 => x8, Rate Law: CellSurface*(k4prime*x5-kminus4prime*x8)
k6 = 0.461; PTP = 1.0Reaction: x8 => x6, Rate Law: Intracellular*k6*PTP*x8
k13 = 0.00696; kminus13 = 0.167; k13prime = 0.0Reaction: x20 => x21, Rate Law: Intracellular*((k13+k13prime)*x20-kminus13*x21)
k8 = 7.06E-4; kminus8 = 10.0Reaction: x11 + x10 => x12, Rate Law: Intracellular*(k8*x10*x11-kminus8*x12)
kminus14 = 0.001155Reaction: x20 =>, Rate Law: Intracellular*kminus14*x20
k14 = 0.11088Reaction: => x20, Rate Law: Intracellular*k14
kminus7 = 1.396; PTP = 1.0; k7 = 4.16; IRp = 897.0Reaction: x9 => x10; x4, x5, Rate Law: Intracellular*(k7*x9*(x4+x5)/IRp-kminus7*PTP*x10)
SHIP = 1.0; k10 = 2.961; kminus10 = 2.77Reaction: x15 => x13, Rate Law: Intracellular*(k10*x15-kminus10*SHIP*x13)
k3 = 2500.0Reaction: x3 => x5, Rate Law: CellSurface*k3*x3
k2 = 6.0E-8; kminus2 = 20.0Reaction: x5 => x4; x1, Rate Law: CellSurface*k2*x1*x5-kminus2*x4
kminus5 = 1.67E-18Reaction: x6 =>, Rate Law: Intracellular*kminus5*x6
k1 = 6.0E-8; kminus1 = 0.2Reaction: x2 => x3; x1, Rate Law: CellSurface*(k1*x1*x2-kminus1*x3)
PTP = 1.0; kminus3 = 0.2Reaction: x5 => x2, Rate Law: CellSurface*kminus3*PTP*x5
k12 = 0.0; kminus12 = 6.9315Reaction: x18 => x19, Rate Law: Intracellular*(k12*x18-kminus12*x19)
PTEN = 1.0; k9 = 0.0; kminus9 = 42.15Reaction: x14 => x13, Rate Law: Intracellular*(k9*x14-kminus9*PTEN*x13)
k11 = 0.0; kminus11 = 6.9315Reaction: x16 => x17, Rate Law: Intracellular*(k11*x16-kminus11*x17)
k5 = 0.0Reaction: => x6, Rate Law: Intracellular*k5
k4 = 3.3333334E-4; kminus4 = 0.003Reaction: x2 => x6, Rate Law: CellSurface*(k4*x2-kminus4*x6)

States:

NameDescription
x5[Insulin receptor]
x19[Protein kinase C iota type]
x16[RAC-gamma serine/threonine-protein kinase]
x4[Insulin receptor]
x6[Insulin receptor]
x2[Insulin receptor]
x14[1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate; 1-Phosphatidyl-D-myo-inositol 4,5-bisphosphate]
x20[Solute carrier family 2, facilitated glucose transporter member 4]
x17[RAC-gamma serine/threonine-protein kinase]
x3[Insulin receptor]
x18[Protein kinase C iota type]
x15[1-phosphatidyl-1D-myo-inositol 3,4-bisphosphate; 1-Phosphatidyl-1D-myo-inositol 3,4-bisphosphate]
x9[Insulin receptor substrate 1]
x8[Insulin receptor]
x7[Insulin receptor]
x13[1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate; Phosphatidylinositol-3,4,5-trisphosphate]
x11[Phosphoinositide 3-kinase regulatory subunit 5]
x21[Solute carrier family 2, facilitated glucose transporter member 4]
x10[Insulin receptor substrate 1]
x12[Insulin receptor substrate 1; Phosphoinositide 3-kinase regulatory subunit 5]

Segun2018 - model of breast cancer determing Chemotherapy and Keto diet response: MODEL2003050001v0.0.1

&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level2/version4&quot;&gt; &lt;body xmlns=&quot;http://www.w3.org/1…

Details

In this paper, a mathematical model of breast cancer governed by a system of ordinary differential equations in the presence of chemotherapy treatment and ketogenic diet is discussed. Several comprehensive mathematical analyses were carried out using a variety of analytical methods to study the stability of the breast cancer model. Also, sufficient conditions on parameter values to ensure cancer persistence in the absence of anti-cancer drugs, ketogenic diet, and cancer emission when anti-cancer drugs, immune-booster, and ketogenic diet are included were established. Furthermore, optimal control theory is applied to discover the optimal drug adjustment as an input control of the system therapies in order to minimize the number of cancerous cells by considering different controlled combinations of administering the chemotherapy agent and ketogenic diet using the popular Pontryagin’s maximum principle. Numerical simulations are presented to validate our theoretical results link: http://identifiers.org/doi/10.20944/preprints201802.0004.v1

Selvaggio2020 - Microenvironment control of hybrid Epithelial-Mesenchymal phenotypes: MODEL2004040001v0.0.1

Epithelial to Mesenchymal Transition (EMT) has been associated with cancer cell heterogeneity, plasticity and metastasis…

Details

Epithelial-to-mesenchymal transition (EMT) has been associated with cancer cell heterogeneity, plasticity, and metastasis. However, the extrinsic signals supervising these phenotypic transitions remain elusive. To assess how selected microenvironmental signals control cancer-associated phenotypes along the EMT continuum, we defined a logical model of the EMT cellular network that yields qualitative degrees of cell adhesions by adherens junctions and focal adhesions, two features affected during EMT. The model attractors recovered epithelial, mesenchymal, and hybrid phenotypes. Simulations showed that hybrid phenotypes may arise through independent molecular paths involving stringent extrinsic signals. Of particular interest, model predictions and their experimental validations indicated that: 1) stiffening of the ExtraCellular Matrix (ECM) was a prerequisite for cells overactivating FAKSRC to upregulate SNAIL and acquire a mesenchymal phenotype, and 2) FAKSRC inhibition of cell-cell contacts through the Receptor-type tyrosine-protein phosphatases kappa led to acquisition of a full mesenchymal, rather than a hybrid, phenotype. Altogether, these computational and experimental approaches allow assessment of critical microenvironmental signals controlling hybrid EMT phenotypes and indicate that EMT involves multiple molecular programs. link: http://identifiers.org/doi/10.1158/0008-5472.CAN-19-3147

Selvarasu2009 - Genome-scale metabolic network of Mus Musculus (iSS724): MODEL1507180042v0.0.1

Selvarasu2009 - Genome-scale metabolic network of Mus Musculus (iSS724)This model is described in the article: [Genome-…

Details

Genome-scale metabolic modeling has been successfully applied to a multitude of microbial systems, thus improving our understanding of their cellular metabolisms. Nevertheless, only a handful of works have been done for describing mammalian cells, particularly mouse, which is one of the important model organisms, providing various opportunities for both biomedical research and biotechnological applications. Presented herein is a genome-scale mouse metabolic model that was systematically reconstructed by improving and expanding the previous generic model based on integrated biochemical and genomic data of Mus musculus. The key features of the updated model include additional information on gene-protein-reaction association, and improved network connectivity through lipid, amino acid, carbohydrate and nucleotide biosynthetic pathways. After examining the model predictability both quantitatively and qualitatively using constraints-based flux analysis, the structural and functional characteristics of the mouse metabolism were investigated by evaluating network statistics/centrality, gene/metabolite essentiality and their correlation. The results revealed that overall mouse metabolic network is topologically dominated by highly connected and bridging metabolites, and functionally by lipid metabolism that most of essential genes and metabolites are from. The current in silico mouse model can be exploited for understanding and characterizing the cellular physiology, identifying potential cell engineering targets for the enhanced production of recombinant proteins and developing diseased state models for drug targeting. link: http://identifiers.org/pubmed/20024077

Sen2013 - Phospholipid Synthesis in P.knowlesi: BIOMD0000000495v0.0.1

Sen2013 - Phospholipid Synthesis in P.knowlesiThe model describes the multiple phospholipid synthetic pathways in Plasmo…

Details

BACKGROUND: Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species. RESULTS: Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step. CONCLUSION: We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance. link: http://identifiers.org/pubmed/24209716

Parameters:

NameDescription
mw961dacfa_f443_4814_ad6c_a27c04e74268 = 1.0780611108133E-6 mole/liter/minute; mw15ba24b5_7a87_479e_9be7_261b12cbdb63 = 1.22223738254533E-4 mole/literReaction: mw849ed3fd_87d9_44d2_9f3e_4d631b900d41 => mwcb834e43_dc57_45ae_9452_f4c10955caf1; mw849ed3fd_87d9_44d2_9f3e_4d631b900d41, mw849ed3fd_87d9_44d2_9f3e_4d631b900d41, Rate Law: mw961dacfa_f443_4814_ad6c_a27c04e74268*mw849ed3fd_87d9_44d2_9f3e_4d631b900d41/(mw15ba24b5_7a87_479e_9be7_261b12cbdb63+mw849ed3fd_87d9_44d2_9f3e_4d631b900d41)
mw284c519a_cc2b_4a98_99ce_5a4471af99e1 = 3.04072645117622E-5 mole/liter; mwff26437c_166b_4946_ad35_f13df6145780 = 5.55658410000431E-7 mole/liter/minuteReaction: mw812f63db_4cb0_40ad_b92b_9874be969dfe => mwcb834e43_dc57_45ae_9452_f4c10955caf1; mw812f63db_4cb0_40ad_b92b_9874be969dfe, mw812f63db_4cb0_40ad_b92b_9874be969dfe, Rate Law: mwff26437c_166b_4946_ad35_f13df6145780*mw812f63db_4cb0_40ad_b92b_9874be969dfe/(mw284c519a_cc2b_4a98_99ce_5a4471af99e1+mw812f63db_4cb0_40ad_b92b_9874be969dfe)
mw1a53a2cb_a3a7_40d7_ae07_4d93ad1123a3 = 0.00141678261342411 mole/liter/minute; mw4035a2c9_3cda_467c_83cc_8f9c2902abaf = 0.321125432799976 mole/literReaction: mwf166ad55_4ff0_49fb_95d2_b657ad7653d5 => mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5; mwf166ad55_4ff0_49fb_95d2_b657ad7653d5, mwf166ad55_4ff0_49fb_95d2_b657ad7653d5, Rate Law: mw1a53a2cb_a3a7_40d7_ae07_4d93ad1123a3*mwf166ad55_4ff0_49fb_95d2_b657ad7653d5/(mw4035a2c9_3cda_467c_83cc_8f9c2902abaf+mwf166ad55_4ff0_49fb_95d2_b657ad7653d5)
mw3046ca21_42a2_4a4b_89c4_9d6ca3d927c5 = 0.171122974053956 mole/liter; mw5ffad843_5f02_419d_ba99_6e1f9b7e6b7b = 8.99054709659885E-5 mole/liter/minuteReaction: mwf166ad55_4ff0_49fb_95d2_b657ad7653d5 => mwfcfaf604_14d4_47a6_b021_226d1fb5497c; mwf166ad55_4ff0_49fb_95d2_b657ad7653d5, mwf166ad55_4ff0_49fb_95d2_b657ad7653d5, Rate Law: mw5ffad843_5f02_419d_ba99_6e1f9b7e6b7b*mwf166ad55_4ff0_49fb_95d2_b657ad7653d5/(mw3046ca21_42a2_4a4b_89c4_9d6ca3d927c5+mwf166ad55_4ff0_49fb_95d2_b657ad7653d5)
mw231a5907_d1ee_4a43_83ab_abb72f19502c = 4.12788404046025E-7 mole/liter/minute; mwaf289d12_4291_4651_8bd1_82e321e476a4 = 3.10498877738431E-5 mole/literReaction: mwcb834e43_dc57_45ae_9452_f4c10955caf1 => mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5; mwcb834e43_dc57_45ae_9452_f4c10955caf1, mwcb834e43_dc57_45ae_9452_f4c10955caf1, Rate Law: mw231a5907_d1ee_4a43_83ab_abb72f19502c*mwcb834e43_dc57_45ae_9452_f4c10955caf1/(mwaf289d12_4291_4651_8bd1_82e321e476a4+mwcb834e43_dc57_45ae_9452_f4c10955caf1)
mw798d0b02_925e_471b_a372_526d681cc370 = 2.620389955953E-6 mole/liter/minute; mwd3807289_133c_4621_8087_366621f553d3 = 2.39591245105385E-5 mole/literReaction: mw15abaa48_d7d0_4845_ae04_c573d289d495 => mw8796c919_9251_4970_8f87_0bca9ecfeb5c; mw15abaa48_d7d0_4845_ae04_c573d289d495, mw15abaa48_d7d0_4845_ae04_c573d289d495, Rate Law: mw798d0b02_925e_471b_a372_526d681cc370*mw15abaa48_d7d0_4845_ae04_c573d289d495/(mwd3807289_133c_4621_8087_366621f553d3+mw15abaa48_d7d0_4845_ae04_c573d289d495)
mw5c4edb54_cfd9_43af_b70b_e9ff1b44dc55 = 1.08608492867695E-4 mole/liter; mw2439178f_a48f_4425_82f9_13267b917b85 = 8.62083015294042E-6 mole/liter/minuteReaction: mw8796c919_9251_4970_8f87_0bca9ecfeb5c => mw849ed3fd_87d9_44d2_9f3e_4d631b900d41; mw8796c919_9251_4970_8f87_0bca9ecfeb5c, mw8796c919_9251_4970_8f87_0bca9ecfeb5c, Rate Law: mw2439178f_a48f_4425_82f9_13267b917b85*mw8796c919_9251_4970_8f87_0bca9ecfeb5c/(mw5c4edb54_cfd9_43af_b70b_e9ff1b44dc55+mw8796c919_9251_4970_8f87_0bca9ecfeb5c)
mw7ce1b6a3_e65e_4aaa_9c32_aeefb420f0ea = 1.30568052867489E-6 mole/liter/minute; mw85485398_9f97_408c_bca6_90f0a8377eae = 7.96722533770371E-4 mole/literReaction: mw15abaa48_d7d0_4845_ae04_c573d289d495 => mwfcfaf604_14d4_47a6_b021_226d1fb5497c; mw15abaa48_d7d0_4845_ae04_c573d289d495, mw15abaa48_d7d0_4845_ae04_c573d289d495, Rate Law: mw7ce1b6a3_e65e_4aaa_9c32_aeefb420f0ea*mw15abaa48_d7d0_4845_ae04_c573d289d495/(mw85485398_9f97_408c_bca6_90f0a8377eae+mw15abaa48_d7d0_4845_ae04_c573d289d495)
mwff99ad6c_8951_4d58_a836_cf2d3d08ac86 = 1.32810241970949E-4 1/minute; mw2cd81e51_eb11_4e2c_b609_b2f802438a6b = 5.0E-4 1/minuteReaction: mw08818dfe_fb12_45cc_8c1d_d965f142d0ce => mw8796c919_9251_4970_8f87_0bca9ecfeb5c; mw08818dfe_fb12_45cc_8c1d_d965f142d0ce, mw8796c919_9251_4970_8f87_0bca9ecfeb5c, mw08818dfe_fb12_45cc_8c1d_d965f142d0ce, mw8796c919_9251_4970_8f87_0bca9ecfeb5c, Rate Law: mw2cd81e51_eb11_4e2c_b609_b2f802438a6b*mw08818dfe_fb12_45cc_8c1d_d965f142d0ce-mwff99ad6c_8951_4d58_a836_cf2d3d08ac86*mw8796c919_9251_4970_8f87_0bca9ecfeb5c
mwba0debe9_c575_4f5a_a980_e2b6857ff053 = 5.61352652271706E-6 mole/liter/minute; mwffba86ff_a560_401a_93d6_c0e30bf42c87 = 2.27368268903121E-4 mole/literReaction: mw849ed3fd_87d9_44d2_9f3e_4d631b900d41 => mwf166ad55_4ff0_49fb_95d2_b657ad7653d5; mw849ed3fd_87d9_44d2_9f3e_4d631b900d41, mw849ed3fd_87d9_44d2_9f3e_4d631b900d41, Rate Law: mwba0debe9_c575_4f5a_a980_e2b6857ff053*mw849ed3fd_87d9_44d2_9f3e_4d631b900d41/(mwffba86ff_a560_401a_93d6_c0e30bf42c87+mw849ed3fd_87d9_44d2_9f3e_4d631b900d41)
mwf7d1ff9f_1734_4232_9a96_037b31b193b0 = 6.97333029651601E-7 mole/liter/minute; mw7d57aa6b_1bfb_4472_b555_919263d9eaf9 = 3.76085190209901E-6 mole/literReaction: mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5 => mwfcfaf604_14d4_47a6_b021_226d1fb5497c; mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5, mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5, Rate Law: mwf7d1ff9f_1734_4232_9a96_037b31b193b0*mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5/(mw7d57aa6b_1bfb_4472_b555_919263d9eaf9+mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5)
mw9f56ecc5_c22b_4f8c_8b82_90e2a6d9e364 = 2.24518521682572E-6 mole/liter/minute; mw18bbabcb_d229_4d91_99f1_484f2ba8f020 = 2.03868171233541E-4 mole/literReaction: mwfcfaf604_14d4_47a6_b021_226d1fb5497c => mwf166ad55_4ff0_49fb_95d2_b657ad7653d5; mwfcfaf604_14d4_47a6_b021_226d1fb5497c, mwfcfaf604_14d4_47a6_b021_226d1fb5497c, Rate Law: mw9f56ecc5_c22b_4f8c_8b82_90e2a6d9e364*mwfcfaf604_14d4_47a6_b021_226d1fb5497c/(mw18bbabcb_d229_4d91_99f1_484f2ba8f020+mwfcfaf604_14d4_47a6_b021_226d1fb5497c)
mw371071cd_ec20_4517_acc1_08dfdc871e87 = 2.41308392167819E-5 mole/liter; mw87bb1238_3292_467e_bfe3_ff7f1e64a351 = 1.5662833197895E-6 mole/liter/minuteReaction: mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5 => ; mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5, mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5, Rate Law: mw87bb1238_3292_467e_bfe3_ff7f1e64a351*mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5/(mw371071cd_ec20_4517_acc1_08dfdc871e87+mwee54b5b4_b8c0_41df_8dda_5b160c5e10a5)
mw5b225cdc_783f_4a15_93db_e960a2398b8e = 1.53754224136353E-6 mole/liter/minute; mw27f524cb_75b3_401c_8533_99d6f27af654 = 2.03777063277265E-4 mole/literReaction: mwfcfaf604_14d4_47a6_b021_226d1fb5497c => ; mwfcfaf604_14d4_47a6_b021_226d1fb5497c, mwfcfaf604_14d4_47a6_b021_226d1fb5497c, Rate Law: mw5b225cdc_783f_4a15_93db_e960a2398b8e*mwfcfaf604_14d4_47a6_b021_226d1fb5497c/(mw27f524cb_75b3_401c_8533_99d6f27af654+mwfcfaf604_14d4_47a6_b021_226d1fb5497c)
mwee07eca4_0806_4cc3_a6ab_9226ee79be6c = 3.40936490738966E-6 mole/liter/minute; mw8f20c25d_9700_4822_b5f9_fe243e001091 = 3.62894258752347E-4 mole/literReaction: mw73259e20_240e_4f3a_b2e0_9ca248658898 => mw15abaa48_d7d0_4845_ae04_c573d289d495; mw73259e20_240e_4f3a_b2e0_9ca248658898, mw73259e20_240e_4f3a_b2e0_9ca248658898, Rate Law: mwee07eca4_0806_4cc3_a6ab_9226ee79be6c*mw73259e20_240e_4f3a_b2e0_9ca248658898/(mw8f20c25d_9700_4822_b5f9_fe243e001091+mw73259e20_240e_4f3a_b2e0_9ca248658898)
mwbf296afc_5e4f_4819_8028_06b20d7af7ca = 0.155164586398126 mole/liter; mwc623d82f_a94e_4460_9aed_444597a728c2 = 7.7375270429582E-4 mole/liter/minuteReaction: mwf166ad55_4ff0_49fb_95d2_b657ad7653d5 => ; mwfcfaf604_14d4_47a6_b021_226d1fb5497c, mwfcfaf604_14d4_47a6_b021_226d1fb5497c, Rate Law: mwc623d82f_a94e_4460_9aed_444597a728c2*mwfcfaf604_14d4_47a6_b021_226d1fb5497c/(mwbf296afc_5e4f_4819_8028_06b20d7af7ca+mwfcfaf604_14d4_47a6_b021_226d1fb5497c)
mwf5cecb8f_89f8_4fba_b39b_b517d0bef2ce = 1.02326862282225E-4 mole/liter; mw91e15e1e_c73e_4866_ab2b_8225a32b7610 = 2.32432741134546E-7 mole/liter/minuteReaction: mw919f8a86_e702_4b24_9cd7_adad694fcf9b => mw812f63db_4cb0_40ad_b92b_9874be969dfe; mw919f8a86_e702_4b24_9cd7_adad694fcf9b, mw919f8a86_e702_4b24_9cd7_adad694fcf9b, Rate Law: mw91e15e1e_c73e_4866_ab2b_8225a32b7610*mw919f8a86_e702_4b24_9cd7_adad694fcf9b/(mwf5cecb8f_89f8_4fba_b39b_b517d0bef2ce+mw919f8a86_e702_4b24_9cd7_adad694fcf9b)

States:

NameDescription
mw08818dfe fb12 45cc 8c1d d965f142d0ce[ethanolamine]
mw73259e20 240e 4f3a b2e0 9ca248658898[serine]
mw849ed3fd 87d9 44d2 9f3e 4d631b900d41[O-phosphoethanolamine]
mw812f63db 4cb0 40ad b92b 9874be969dfe[choline]
mwfcfaf604 14d4 47a6 b021 226d1fb5497c[phosphatidylserine O-18:0/0:0]
mw15abaa48 d7d0 4845 ae04 c573d289d495[serine]
mwee54b5b4 b8c0 41df 8dda 5b160c5e10a5[phosphatidylcholine(1+)]
mwcb834e43 dc57 45ae 9452 f4c10955caf1[phosphocholine]
mw8796c919 9251 4970 8f87 0bca9ecfeb5c[ethanolamine]
mw919f8a86 e702 4b24 9cd7 adad694fcf9b[choline]
mwf166ad55 4ff0 49fb 95d2 b657ad7653d5[phosphatidylethanolamine]

Sen2020 - Metabolic alteration in PBMCs associated with type 1 diabetes: MODEL1905270001v0.0.1

&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level2&quot;&gt; &lt;body xmlns=&quot;http://www.w3.org/1999/xhtml…

Details

AIMS/HYPOTHESIS:Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes. METHODS:In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D, n = 34), (2) seroconverted to ≥1 islet autoantibody without progressing to type 1 diabetes (P1Ab, n = 27) or (3) remained autoantibody negative during follow-up (CTRL, n = 10). RESULTS:During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression. CONCLUSIONS/INTERPRETATION:Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes. DATA AVAILABILITY:The GEMs for PBMCs have been submitted to BioModels (www.ebi.ac.uk/biomodels/), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/), under accession number MTBLS1015. link: http://identifiers.org/pubmed/32043185

Senger2008 - Genome-scale metabolic network of Clostridium acetobutylicum (iCac802): MODEL1507180027v0.0.1

Senger2008 - Genome-scale metabolic network of Clostridium acetobutylicum (iCac802)This model is described in the articl…

Details

A genome-scale metabolic network reconstruction for Clostridium acetobutylicum (ATCC 824) was carried out using a new semi-automated reverse engineering algorithm. The network consists of 422 intracellular metabolites involved in 552 reactions and includes 80 membrane transport reactions. The metabolic network illustrates the reliance of clostridia on the urea cycle, intracellular L-glutamate solute pools, and the acetylornithine transaminase for amino acid biosynthesis from the 2-oxoglutarate precursor. The semi-automated reverse engineering algorithm identified discrepancies in reaction network databases that are major obstacles for fully automated network-building algorithms. The proposed semi-automated approach allowed for the conservation of unique clostridial metabolic pathways, such as an incomplete TCA cycle. A thermodynamic analysis was used to determine the physiological conditions under which proposed pathways (e.g., reverse partial TCA cycle and reverse arginine biosynthesis pathway) are feasible. The reconstructed metabolic network was used to create a genome-scale model that correctly characterized the butyrate kinase knock-out and the asolventogenic M5 pSOL1 megaplasmid degenerate strains. Systematic gene knock-out simulations were performed to identify a set of genes encoding clostridial enzymes essential for growth in silico. link: http://identifiers.org/pubmed/18767192

Sengupta2015 - Knowledge base model of human energy pool network (HEPNet): BIOMD0000000579v0.0.1

Sengupta2015 - Knowledge base model of human energy pool network (HEPNet)This model is described in the article: [HEPNe…

Details

HEPNet is an electronic representation of metabolic reactions occurring within human cellular organization focusing on inflow and outflow of the energy currency ATP, GTP and other energy associated moieties. The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. The results also highlight that energy currencies play a minor role. However, the moiety creatine phosphate has a unique character, since it is a ready-made source of phosphoryl groups for the rapid synthesis of ATP from ADP. HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders. link: http://identifiers.org/pubmed/26053019

Parameters:

NameDescription
v1=1.0 substance; k1=0.157 substanceReaction: s182 + s334 => s183 + s190 + s329 + s237; s192, s182, Rate Law: v1*s182/(k1+s182)
k1=1.3 substance; v1=1.0 substanceReaction: s73 + s64 => s3 + s44; s31, s73, Rate Law: v1*s73/(k1+s73)
k1=34.5 substance; v1=1.0 substanceReaction: s72 + s355 => s80 + s351 + s361; s366, s72, Rate Law: v1*s72/(k1+s72)
k1=3.8E-4 substance; v1=1.0 substanceReaction: s9 + s355 => s50 + s351 + s361; s28, s9, Rate Law: v1*s9/(k1+s9)
k1=1.37 substance; v1=1.0 substanceReaction: s297 + s64 => s71; s298, s297, Rate Law: v1*s297/(k1+s297)
k1=0.04 substance; v1=1.0 substanceReaction: s234 + s334 => s181 + s185; s186, s333, s234, Rate Law: v1*s234/(k1+s234)
k1=100.0 substance; v1=1.0 substanceReaction: s70 + s347 => s72; s365, s70, Rate Law: v1*s70/(k1+s70)
k1=294.0 substance; v1=1.0 substanceReaction: s321 + s326 + s347 => s322 + s350; s327, s321, Rate Law: v1*s321/(k1+s321)
v1=1.0 substance; k1=18.2 substanceReaction: s181 + s64 => s182; s189, s333, s181, Rate Law: v1*s181/(k1+s181)
k1=0.1 substance; v1=1.0 substanceReaction: s253 + s63 => s195 + s46; s255, s253, Rate Law: v1*s253/(k1+s253)
k1=0.31 substance; v1=1.0 substanceReaction: s293 + s63 => s35 + s46; s294, s293, Rate Law: v1*s293/(k1+s293)
k1=0.09 substance; v1=1.0 substanceReaction: s6 + s352 => s7 + s345 + s350; s58, s6, Rate Law: v1*s6/(k1+s6)
k1=5.8 substance; v1=1.0 substanceReaction: s10 + s46 => s11 + s63; s45, s10, Rate Law: v1*s10/(k1+s10)
k1=16.0 substance; v1=1.0 substanceReaction: s66 + s350 => s65; s362, s66, Rate Law: v1*s66/(k1+s66)
v1=1.0 substance; k1=2900.0 substanceReaction: s4 + s347 => s52; s354, s4, Rate Law: v1*s4/(k1+s4)
k1=3.0 substance; v1=1.0 substanceReaction: s65 + s356 => s70 + s353; s364, s65, Rate Law: v1*s65/(k1+s65)
k1=0.069 substance; v1=1.0 substanceReaction: s302 + s301 => s335 + s300 + s329; s13, s302, Rate Law: v1*s302/(k1+s302)
k1=970.0 substance; v1=1.0 substanceReaction: s306 + s63 => s302 + s46; s305, s333, s306, Rate Law: v1*s306/(k1+s306)
k1=73.0 substance; v1=1.0 substanceReaction: s52 + s355 => s5 + s349 + s351 + s361; s56, s52, Rate Law: v1*s52/(k1+s52)
k1=0.58 substance; v1=1.0 substanceReaction: s286 + s63 => s285 + s46; s287, s286, Rate Law: v1*s286/(k1+s286)
v1=1.0 substance; k1=13.0 substanceReaction: s25 + s29 => s33 + s30; s41, s25, Rate Law: v1*s25/(k1+s25)
v1=1.0 substance; k1=0.08 substanceReaction: s197 => s198; s336, s203, s336, s250, s197, Rate Law: v1*s197/(k1+s197)
k1=300.0 substance; v1=1.0 substanceReaction: s74 + s46 => s8 + s63; s86, s333, s74, Rate Law: v1*s74/(k1+s74)
k1=33.0 substance; v1=1.0 substanceReaction: s74 + s67 + s329 => s35 + s44 + s93; s85, s74, Rate Law: v1*s74/(k1+s74)
k1=1.0 substanceReaction: s125 + s126 => s127 + s238; s123, s125, s126, Rate Law: s125*s126*k1
k1=10.0 substance; v1=1.0 substanceReaction: s284 + s64 => s71 + s286; s283, s284, Rate Law: v1*s284/(k1+s284)
k1=0.11 substance; v1=1.0 substanceReaction: s11 + s63 => s81 + s46; s90, s11, Rate Law: v1*s11/(k1+s11)
k1=0.013 substance; v1=1.0 substanceReaction: s40 + s347 => s9; s27, s40, Rate Law: v1*s40/(k1+s40)
v1=1.0 substance; k1=1.16 substanceReaction: s124 + s345 => s123; s400, s124, Rate Law: v1*s124/(k1+s124)
k1=9.6 substance; v1=1.0 substanceReaction: s123 => s124 + s47; s123, Rate Law: v1*s123/(k1+s123)
v1=1.0 substance; k1=0.048 substanceReaction: s71 + s63 => s234 + s46; s16, s71, Rate Law: v1*s71/(k1+s71)

States:

NameDescription
s351[NADH; NADH]
s297[alpha,alpha-Trehalose; alpha,alpha-trehalose]
s100C14 Ketoacyl-CoA
s105[2-trans-Dodecenoyl-CoA; trans-dodec-2-enoyl-CoA]
s197Glycogen Primer
s72[(3S)-3-Hydroxyacyl-CoA; (S)-3-hydroxyacyl-CoA]
s46[ADP; ADP]
s70[2-trans-Dodecenoyl-CoA; trans-dodec-2-enoyl-CoA]
s345[ATP; ATP]
s134C14 AcylCoA_cyt
s129C20car_ims
s292[Triacylglycerol; triglyceride]
s25[HCO3-; hydrogencarbonate; NH4+; ammonium]
s131C18car_ims
s348[ADP; ADP]
s182[6-Phospho-D-gluconate; 6-phospho-D-gluconic acid]
s101[Acyl-CoA; acyl-CoA]
s334[NADP+; NADP(+)]
s347[H2O; water]
s300[NADH; NADH]
s358[H+; hydron]
s381[NADH; NADH]
s125C22 AcylCoA_cyt
s83-PGA
s93[NAD+; NAD(+)]
s361[H+; hydron]
s47[AMP; AMP]
s104[(3S)-3-Hydroxyacyl-CoA; (S)-3-hydroxyacyl-CoA]
s370C18car_ims
s103C12 Ketoacyl-CoA
s136car_mat
s185NADPH
s355[NAD+; NAD(+)]
s98[2-trans-Dodecenoyl-CoA; trans-dodec-2-enoyl-CoA]
s389[H+; hydron]
s67[NADH; NADH]
s63[ATP; ATP]
s80C18 Ketoacyl-CoA
s4[cis-Aconitate; cis-aconitic acid]
s64[H2O; water]
s346[ADP; ADP]
s259[SPRR2E; Small proline-rich protein 2E]
s190[NADPH; NADPH]
s65[Acyl-CoA; acyl-CoA]
s322[(S)-3-Hydroxy-3-methylglutaryl-CoA; (3S)-3-hydroxy-3-methylglutaryl-CoA]

Shannon2004_VentricularMyocyte: MODEL7914464799v0.0.1

This a model from the article: A mathematical treatment of integrated Ca dynamics within the ventricular myocyte. Sh…

Details

We have developed a detailed mathematical model for Ca2+ handling and ionic currents in the rabbit ventricular myocyte. The objective was to develop a model that: 1), accurately reflects Ca-dependent Ca release; 2), uses realistic parameters, particularly those that concern Ca transport from the cytosol; 3), comes to steady state; 4), simulates basic excitation-contraction coupling phenomena; and 5), runs on a normal desktop computer. The model includes the following novel features: 1), the addition of a subsarcolemmal compartment to the other two commonly formulated cytosolic compartments (junctional and bulk) because ion channels in the membrane sense ion concentrations that differ from bulk; 2), the use of realistic cytosolic Ca buffering parameters; 3), a reversible sarcoplasmic reticulum (SR) Ca pump; 4), a scheme for Na-Ca exchange transport that is [Na]i dependent and allosterically regulated by [Ca]i; and 5), a practical model of SR Ca release including both inactivation/adaptation and SR Ca load dependence. The data describe normal electrical activity and Ca handling characteristics of the cardiac myocyte and the SR Ca load dependence of these processes. The model includes a realistic balance of Ca removal mechanisms (e.g., SR Ca pump versus Na-Ca exchange), and the phenomena of rest decay and frequency-dependent inotropy. A particular emphasis is placed upon reproducing the nonlinear dependence of gain and fractional SR Ca release upon SR Ca load. We conclude that this model is more robust than many previously existing models and reproduces many experimental results using parameters based largely on experimental measurements in myocytes. link: http://identifiers.org/pubmed/15347581

Shariatpanahi2018 - Mathematical modeling of tumor-induced immunosuppression by myeloid-derived suppressor cells: MODEL1909090003v0.0.1

This is a ODE-based mathematical model featuring equations describing the dynamics of tumor cells, cytotoxic T cells, na…

Details

Myeloid-derived suppressor cells (MDSCs) belong to immature myeloid cells that are generated and accumulated during the tumor development. MDSCs strongly suppress the anti-tumor immunity and provide conditions for tumor progression and metastasis. In this study, we present a mathematical model based on ordinary differential equations (ODE) to describe tumor-induced immunosuppression caused by MDSCs. The model consists of four equations and incorporates tumor cells, cytotoxic T cells (CTLs), natural killer (NK) cells and MDSCs. We also provide simulation models that evaluate or predict the effects of anti-MDSC drugs (e.g., l-arginine and 5-Fluorouracil (5-FU)) on the tumor growth and the restoration of anti-tumor immunity. The simulated results obtained using our model were in good agreement with the corresponding experimental findings on the expansion of splenic MDSCs, immunosuppressive effects of these cells at the tumor site and effectiveness of l-arginine and 5-FU on the re-establishment of antitumor immunity. Regarding this latter issue, our predictive simulation results demonstrated that intermittent therapy with low-dose 5-FU alone could eradicate the tumors irrespective of their origins and types. Furthermore, at the time of tumor eradication, the number of CTLs prevailed over that of cancer cells and the number of splenic MDSCs returned to the normal levels. Finally, our predictive simulation results also showed that the addition of l-arginine supplementation to the intermittent 5-FU therapy reduced the time of the tumor eradication and the number of iterations for 5-FU treatment. Thus, the present mathematical model provides important implications for designing new therapeutic strategies that aim to restore antitumor immunity by targeting MDSCs. link: http://identifiers.org/pubmed/29337259

Sharp2019 - AML: BIOMD0000000798v0.0.1

The paper describes a model of acute myeloid leukaemia. Created by COPASI 4.26 (Build 213) This model is described…

Details

Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corresponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin's Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available. link: http://identifiers.org/pubmed/30853393

Parameters:

NameDescription
y = 0.01 1; a = 0.015 1Reaction: L =>, Rate Law: bone_marrow*a*L/(y+L)
ut = 0.3 1Reaction: T =>, Rate Law: bone_marrow*ut*T
da = 0.44 1Reaction: A => D, Rate Law: bone_marrow*da*A
dl = 0.05 1Reaction: L => T, Rate Law: bone_marrow*dl*L
pl = 0.27 1; k2 = 1.0 1; Z2 = 0.1 1Reaction: => L, Rate Law: bone_marrow*pl*L*(k2-Z2)
k1 = 1.0 1; Z1 = 0.1 1; ps = 0.5 1Reaction: => S, Rate Law: bone_marrow*ps*S*(k1-Z1)
pa = 0.43 1; k2 = 1.0 1; Z2 = 0.1 1Reaction: => A, Rate Law: bone_marrow*pa*A*(k2-Z2)
ud = 0.275 1Reaction: D =>, Rate Law: bone_marrow*ud*D
ds = 0.14 1Reaction: S => A, Rate Law: bone_marrow*ds*S

States:

NameDescription
S[hematopoietic stem cell]
A[common myeloid progenitor]
T[lymphoma or leukaemia cell line]
D[cell]
L[stem cell]

Shaw 2018 - Yeast GPCR Ternary Complex Model: MODEL1901300001v0.0.1

SBML model exported from PottersWheel on 2019-01-17 12:49:15. This model was created via Matlab and automatically conve…

Details

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose response. Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites, and hormones relevant to human health. This work enables rational tuning of cell sensing while providing a framework to guide reprogramming of GPCR-based signaling in other systems. link: http://identifiers.org/pubmed/30955892

Shaw2019 - Digital biosensor model from Yeast: MODEL1901300002v0.0.1

SBML model exported from PottersWheel on 2018-06-29 21:50:11.

Details

G protein-coupled receptor (GPCR) signaling is the primary method eukaryotes use to respond to specific cues in their environment. However, the relationship between stimulus and response for each GPCR is difficult to predict due to diversity in natural signal transduction architecture and expression. Using genome engineering in yeast, we constructed an insulated, modular GPCR signal transduction system to study how the response to stimuli can be predictably tuned using synthetic tools. We delineated the contributions of a minimal set of key components via computational and experimental refactoring, identifying simple design principles for rationally tuning the dose response. Using five different GPCRs, we demonstrate how this enables cells and consortia to be engineered to respond to desired concentrations of peptides, metabolites, and hormones relevant to human health. This work enables rational tuning of cell sensing while providing a framework to guide reprogramming of GPCR-based signaling in other systems. link: http://identifiers.org/pubmed/30955892

Shen-Orr2002_FeedForward_AND_gate: BIOMD0000000316v0.0.1

This is the coherent feed forward loop with an AND-gate like control of the response operon described in the article:…

Details

Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks. link: http://identifiers.org/pubmed/11967538

Parameters:

NameDescription
Ty=0.5 dimensionless; Tz=0.5 dimensionlessReaction: => Z; X, Y, Rate Law: piecewise(1, X >= Ty, 0)*piecewise(1, Y >= Tz, 0)
a=1.0 dimensionlessReaction: Z =>, Rate Law: a*Z
Ty=0.5 dimensionlessReaction: => Y; X, Rate Law: piecewise(1, X >= Ty, 0)

States:

NameDescription
Y[protein; obsolete transcription activator activity]
Z[protein]

Shen-Orr2002_Single_Input_Module: BIOMD0000000317v0.0.1

This is the single input module, SIM, described in the article: **Network motifs in the transcriptional regulation netw…

Details

Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks. link: http://identifiers.org/pubmed/11967538

Parameters:

NameDescription
T2=0.5 dimensionlessReaction: => Z2; X, Rate Law: piecewise(1, X >= T2, 0)
FX = 0.0 dimensionlessReaction: X = FX-X, Rate Law: FX-X
T3=0.8 dimensionlessReaction: => Z3; X, Rate Law: piecewise(1, X >= T3, 0)
a=1.0 dimensionlessReaction: Z3 =>, Rate Law: a*Z3
T1=0.1 dimensionlessReaction: => Z1; X, Rate Law: piecewise(1, X >= T1, 0)

States:

NameDescription
X[protein; obsolete transcription activator activity]
Z2[protein]
Z3[protein]
Z1[protein]

Shi1993_Caffeine_pressor_tolerance: BIOMD0000000241v0.0.1

described in: **Pharmacokinetic-pharmacodynamic modeling of caffeine: Tolerance to pressor effects** Shi J, Benowit…

Details

We propose a parametric pharmacokinetic-pharmacodynamic model for caffeine that quantifies the development of tolerance to the pressor effect of the drug and characterizes the mean behavior and inter-individual variation of both pharmacokinetics and pressor effect. Our study in a small group of subjects indicates that acute tolerance develops to the pressor effect of caffeine and that both the pressor effect and tolerance occur after some time delay relative to changes in plasma caffeine concentration. The half-life of equilibration of effect with plasma caffeine concentration is about 20 minutes. The half-life of development and regression of tolerance is estimated to be about 1 hour, and the model suggests that tolerance, at its fullest, causes more than a 90% reduction of initial (nontolerant) effect. Whereas tolerance to the pressor effect of caffeine develops in habitual coffee drinkers, the pressor response is regained after relatively brief periods of abstinence. Because of the rapid development and regression of tolerance, the pressor response to caffeine depends on how much caffeine is consumed, the schedule of consumption, and the elimination half-life of caffeine. link: http://identifiers.org/pubmed/8422743

Parameters:

NameDescription
k_tol = 0.75 per_hourReaction: C_t = k_tol*(C_p-C_t), Rate Law: k_tol*(C_p-C_t)
k12 = 1.64 per_hour; k21 = 1.19 per_hourReaction: C_per = k12*C_p-k21*C_per, Rate Law: k12*C_p-k21*C_per
k10 = 0.34 per_hour; k12 = 1.64 per_hour; F = 0.984; k21 = 1.19 per_hour; V_C = 0.32 liter_per_kg; k_a = 12.0 per_hourReaction: C_p = ((k_a*F*X_gut/V_C-k12*C_p)+k21*C_per)-k10*C_p, Rate Law: ((k_a*F*X_gut/V_C-k12*C_p)+k21*C_per)-k10*C_p
k_eo = 2.03 per_hourReaction: C_e = k_eo*(C_p-C_e), Rate Law: k_eo*(C_p-C_e)
k_a = 12.0 per_hourReaction: X_gut = (-k_a)*X_gut, Rate Law: (-k_a)*X_gut

States:

NameDescription
C t[caffeine]
X gut[Caffeine; caffeine]
C p[caffeine; Caffeine]
C per[caffeine; Caffeine]
C e[caffeine]