SBMLBioModels: M - N
M
MODEL1203220000
— v0.0.1Created by The MathWorks, Inc. SimBiology tool, Version 3.3
Details
Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines. link: http://identifiers.org/pubmed/24432155
BIOMD0000000776
— v0.0.1The paper describes a model of resistance of cancer to chemotherapy. Created by COPASI 4.25 (Build 207) This model…
Details
The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times. link: http://identifiers.org/pubmed/19135065
Parameters:
Name | Description |
---|---|
N = 1.00002E10 1; Ninf = 2.0E12 1; b = 0.005928 1/d; C0 = 2.0 1 | Reaction: S =>, Rate Law: tme*(-b)*ln(N/Ninf)*C0*S |
N = 1.00002E10 1; Ninf = 2.0E12 1; b = 0.005928 1/d | Reaction: => R, Rate Law: tme*(-b)*ln(N/Ninf)*R |
N = 1.00002E10 1; Ninf = 2.0E12 1; t1 = 1.0E-6 1; t2 = 1.0E-6 1; b = 0.005928 1/d | Reaction: S => R, Rate Law: tme*(-b)*ln(N/Ninf)*(t1*S-t2*R) |
States:
Name | Description |
---|---|
S | [malignant cell] |
R | [malignant cell] |
BIOMD0000000315
— v0.0.1This is the model of the in vitro DNA oscillator called oligator with the optmized set of parameters described in the ar…
Details
Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules. link: http://identifiers.org/pubmed/21283142
Parameters:
Name | Description |
---|---|
k0d = 0.0294 nM_per_min; k0r = 3.43457943925 per_min | Reaction: T1 + alpha => alpha_T1, Rate Law: sample*(k0d*T1*alpha-k0r*alpha_T1) |
k26d = 1.7262 per_min | Reaction: Inh => empty, Rate Law: sample*k26d*Inh |
k17d = 0.0171 nM_per_min; k17r = 0.610714285714 per_min | Reaction: beta + T3_Inh => beta_T3_Inh, Rate Law: sample*(k17d*beta*T3_Inh-k17r*beta_T3_Inh) |
k9r = 0.0171 nM_per_min; k9d = 0.610714285714 per_min | Reaction: T2_beta => T2 + beta, Rate Law: sample*(k9d*T2_beta-k9r*T2*beta) |
k6d = 3.34 per_min | Reaction: alpha_alpha_T1 => alpha_T1_alpha, Rate Law: sample*k6d*alpha_alpha_T1 |
k19d = 5.566848 per_min | Reaction: beta_T3_Inh => Inh + beta_Inh_T3, Rate Law: sample*k19d*beta_T3_Inh |
k20d = 3.2064 per_min | Reaction: beta_Inh_T3 => beta_T3_Inh, Rate Law: sample*k20d*beta_Inh_T3 |
k10r = 0.0294 nM_per_min; k10d = 3.43457943925 per_min | Reaction: alpha_T2_beta => alpha + T2_beta, Rate Law: sample*(k10d*alpha_T2_beta-k10r*alpha*T2_beta) |
k3r = 0.0294 nM_per_min; k3d = 3.43457943925 per_min | Reaction: alpha_T1_alpha => alpha + T1_alpha, Rate Law: sample*(k3d*alpha_T1_alpha-k3r*alpha*T1_alpha) |
k5d = 11.8408 per_min | Reaction: alpha_T1_alpha => alpha + alpha_alpha_T1, Rate Law: sample*k5d*alpha_T1_alpha |
k18d = 17.024 per_min | Reaction: beta_T3 => beta_Inh_T3, Rate Law: sample*k18d*beta_T3 |
k2r = 0.0294 nM_per_min; k2d = 3.43457943925 per_min | Reaction: T1_alpha => T1 + alpha, Rate Law: sample*(k2d*T1_alpha-k2r*T1*alpha) |
k11d = 11.8408 per_min | Reaction: alpha_T2 => alpha_beta_T2, Rate Law: sample*k11d*alpha_T2 |
k23r = 0.021546 nM_per_min; k23d = 4.15391351351E-5 nM_per_min | Reaction: alpha + Inh_T1 => alpha_T1 + Inh, Rate Law: sample*(k23d*alpha*Inh_T1-k23r*alpha_T1*Inh) |
k21d = 0.027 nM_per_min; k21r = 0.00608108108108 per_min | Reaction: T1 + Inh => Inh_T1, Rate Law: sample*(k21d*T1*Inh-k21r*Inh_T1) |
k1r = 0.0294 nM_per_min; k1d = 3.43457943925 per_min | Reaction: alpha_T1_alpha => alpha + alpha_T1, Rate Law: sample*(k1d*alpha_T1_alpha-k1r*alpha*alpha_T1) |
k12d = 9.2239832 per_min | Reaction: alpha_T2_beta => beta + alpha_beta_T2, Rate Law: sample*k12d*alpha_T2_beta |
k14r = 0.610714285714 per_min; k14d = 0.0171 nM_per_min | Reaction: beta + T3 => beta_T3, Rate Law: sample*(k14d*beta*T3-k14r*beta_T3) |
k13d = 2.60186 per_min | Reaction: alpha_beta_T2 => alpha_T2_beta, Rate Law: sample*k13d*alpha_beta_T2 |
k25d = 0.485802 per_min | Reaction: beta => empty, Rate Law: sample*k25d*beta |
k16d = 0.027 nM_per_min; k16r = 0.00186296832954 per_min | Reaction: T3 + Inh => T3_Inh, Rate Law: sample*(k16d*T3*Inh-k16r*T3_Inh) |
k8r = 0.0171 nM_per_min; k8d = 0.610714285714 per_min | Reaction: alpha_T2_beta => alpha_T2 + beta, Rate Law: sample*(k8d*alpha_T2_beta-k8r*alpha_T2*beta) |
k4d = 15.2 per_min | Reaction: alpha_T1 => alpha_alpha_T1, Rate Law: sample*k4d*alpha_T1 |
k24d = 0.411 per_min | Reaction: alpha => empty, Rate Law: sample*k24d*alpha |
k7d = 0.0294 nM_per_min; k7r = 3.43457943925 per_min | Reaction: alpha + T2 => alpha_T2, Rate Law: sample*(k7d*alpha*T2-k7r*alpha_T2) |
k15r = 0.027 nM_per_min; k15d = 0.00186296832954 per_min | Reaction: beta_T3_Inh => beta_T3 + Inh, Rate Law: sample*(k15d*beta_T3_Inh-k15r*beta_T3*Inh) |
k22r = 4.15391351351E-5 nM_per_min; k22d = 0.021546 nM_per_min | Reaction: T1_alpha + Inh => alpha + Inh_T1, Rate Law: sample*(k22d*T1_alpha*Inh-k22r*alpha*Inh_T1) |
States:
Name | Description |
---|---|
T3 Inh | [deoxyribonucleic acid; DNA] |
Inh | [deoxyribonucleic acid; DNA] |
alpha T2 beta | [deoxyribonucleic acid; DNA] |
T1 alpha | [deoxyribonucleic acid; DNA] |
alpha | [deoxyribonucleic acid; DNA] |
alpha T2 | [deoxyribonucleic acid; DNA] |
Inh T1 | [deoxyribonucleic acid; DNA] |
alpha alpha T1 | [deoxyribonucleic acid; DNA] |
empty | Inh_T1 |
beta | [deoxyribonucleic acid; DNA] |
T3 | [deoxyribonucleic acid; DNA] |
alpha beta T2 | [deoxyribonucleic acid; DNA] |
beta T3 Inh | [deoxyribonucleic acid; DNA] |
beta T3 | [deoxyribonucleic acid; DNA] |
T1 | [deoxyribonucleic acid; DNA] |
beta Inh T3 | [deoxyribonucleic acid; DNA] |
T2 | [deoxyribonucleic acid; DNA] |
alpha T1 | [deoxyribonucleic acid; DNA] |
T2 beta | [deoxyribonucleic acid; DNA] |
alpha T1 alpha | [deoxyribonucleic acid; DNA] |
MODEL1507180008
— v0.0.1Montagud2010 - Genome-scale metabolic network of Synechocystis sp. PCC6803 (iSyn669)This model is described in the artic…
Details
BACKGROUND: Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform–an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. RESULTS: We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. CONCLUSIONS: iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories. link: http://identifiers.org/pubmed/21083885
BIOMD0000000191
— v0.0.1SBML creators: Armando Reyes-Palomares * , Raul Montañez *, Carlos Rodriguez-Caso +, Francisca Sanchez-Jimenez * , Migue…
Details
We use a modeling and simulation approach to carry out an in silico analysis of the metabolic pathways involving arginine as a precursor of nitric oxide or polyamines in aorta endothelial cells. Our model predicts conditions of physiological steady state, as well as the response of the system to changes in the control parameter, external arginine concentration. Metabolic flux control analysis allowed us to predict the values of flux control coefficients for all the transporters and enzymes included in the model. This analysis fulfills the flux control coefficient summation theorem and shows that both the low affinity transporter and arginase share the control of the fluxes through these metabolic pathways. link: http://identifiers.org/pubmed/17520329
Parameters:
Name | Description |
---|---|
Kmeffllat=847.0 microM; Vmaxefflhat=160.5 microMpermin; Kiornhat=360.0 microM; Kmhat=70.0 microM; Kmlat=847.0 microM; Vmaxeffllat=420.0 microMpermin | Reaction: ORN => ; ARGex, ARGin, Rate Law: cytosol*(Vmaxefflhat/(1+ARGex/Kmhat)*ORN/(Kiornhat*(1+ARGin/Kmhat)+ORN)+Vmaxeffllat/(1+ARGex/Kmlat)*ORN/(Kmeffllat*(1+ARGin/Kmlat)+ORN)) |
Vmaxodc=0.013 microMpermin; Kmodc=90.0 microM | Reaction: ORN =>, Rate Law: cytosol*Vmaxodc*ORN/(Kmodc+ORN) |
Kmnos1=16.0 microM; Vmaxnos1=1.33 microMpermin | Reaction: ARGin =>, Rate Law: cytosol*Vmaxnos1*ARGin/(Kmnos1+ARGin) |
Kmarg=1500.0 microM; Vmaxarg=110.0 microMpermin; Kioarg=1000.0 microM | Reaction: ARGin => ORN; ORN, Rate Law: cytosol*Vmaxarg*ARGin/(Kmarg*(1+ORN/Kioarg)+ARGin) |
Kiornhat=360.0 microM; Kmhat=70.0 microM; Kmlat=847.0 microM; Vmaxlat=420.0 microMpermin; Vmaxhat=160.5 microM | Reaction: ARGex => ARGin; ORN, Rate Law: extracellular*(ARGex/(Kmhat+ARGex)*Vmaxhat/(1+ORN/Kiornhat+ARGin/Kmhat)+ARGex/(Kmlat+ARGex)*Vmaxlat/(1+ORN/Kiornhat+ARGin/Kmlat)) |
States:
Name | Description |
---|---|
ORN | [L-ornithine; L-Ornithine] |
ARGin | [L-arginine; L-Arginine] |
ARGex | [L-arginine; L-Arginine] |
BIOMD0000000662
— v0.0.1Moore2004 - Chronic Myeloid Leukemic cells and T-lymphocytes interactionA mathematical model for the interaction of betw…
Details
In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome. link: http://identifiers.org/pubmed/15038986
Parameters:
Name | Description |
---|---|
gamma_e = 0.0077 0.0864*l/s | Reaction: T_cell_effector => Sink; CML, Rate Law: COMpartment*gamma_e*CML*T_cell_effector |
eta = 43.0 1/Ml; kn = 0.063 1/(0.0115741*ms) | Reaction: T_cell_naive => Sink; CML, Rate Law: COMpartment*kn*T_cell_naive*CML/(CML+eta) |
gamma_c = 0.047 0.0864*l/s | Reaction: CML => Sink; T_cell_effector, Rate Law: COMpartment*gamma_c*T_cell_effector*CML |
dc = 0.68 1/(0.0115741*ms) | Reaction: CML => Sink, Rate Law: COMpartment*dc*CML |
de = 0.12 1/(0.0115741*ms) | Reaction: T_cell_effector => Sink, Rate Law: COMpartment*de*T_cell_effector |
eta = 43.0 1/Ml; alpha_e = 0.53 1/(0.0115741*ms) | Reaction: Source => T_cell_effector; CML, Rate Law: COMpartment*alpha_e*T_cell_effector*CML/(CML+eta) |
rc = 0.23 1/(0.0115741*ms); Cmax = 190000.0 1/Ml | Reaction: Source => CML, Rate Law: COMpartment*rc*CML*ln(Cmax/CML) |
sn = 0.071 1/(11.5741*l*s) | Reaction: Source => T_cell_naive, Rate Law: COMpartment*sn*Source |
eta = 43.0 1/Ml; alpha_n = 0.56 1; kn = 0.063 1/(0.0115741*ms) | Reaction: Source => T_cell_effector; T_cell_naive, CML, Rate Law: COMpartment*alpha_n*kn*T_cell_naive*CML/(CML+eta) |
dn = 0.05 1/(0.0115741*ms) | Reaction: T_cell_naive => Sink, Rate Law: COMpartment*dn*T_cell_naive |
States:
Name | Description |
---|---|
T cell naive | [Naive T-Lymphocyte] |
Source | Source |
CML | [leukemia cell] |
T cell effector | [Effector T-Lymphocyte] |
Sink | Sink |
BIOMD0000000733
— v0.0.1Its a mathematical model depicting CML (chronic myelogenous leukemia) interaction with T cells and impact of T cell acti…
Details
In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome. link: http://identifiers.org/pubmed/15038986
Parameters:
Name | Description |
---|---|
Kn = 0.062 1/d; n = 720.0 mmol/l; An = 0.14 dimensionless; Ae = 0.98 1/d | Reaction: => eff_Tcells; naive_Tcells, tumor_cells, Rate Law: TumorMicroenvr*(An*Kn*naive_Tcells*tumor_cells/(tumor_cells+n)+Ae*eff_Tcells*tumor_cells/(tumor_cells+n)) |
gamma_E = 0.057 l/(mmol*d); De = 0.3 1/d | Reaction: eff_Tcells => ; tumor_cells, Rate Law: TumorMicroenvr*(De*eff_Tcells+gamma_E*tumor_cells*eff_Tcells) |
Sn = 0.37 mmol/(l*d) | Reaction: => naive_Tcells, Rate Law: TumorMicroenvr*Sn |
Dn = 0.23 1/d; Kn = 0.062 1/d; n = 720.0 mmol/l | Reaction: naive_Tcells => ; tumor_cells, Rate Law: TumorMicroenvr*(Dn*naive_Tcells+Kn*naive_Tcells*tumor_cells/(tumor_cells+n)) |
gamma_C = 0.0034 l/(mmol*d); Dc = 0.024 1/d | Reaction: tumor_cells => ; eff_Tcells, Rate Law: TumorMicroenvr*(Dc*tumor_cells-gamma_C*tumor_cells*eff_Tcells) |
Cmax = 230000.0 mmol/l; Rc = 0.0057 1/d | Reaction: => tumor_cells, Rate Law: TumorMicroenvr*Rc*tumor_cells*ln(Cmax/tumor_cells) |
States:
Name | Description |
---|---|
naive Tcells | [Naive T-Lymphocyte] |
tumor cells | [neoplasm] |
eff Tcells | [Effector T-Lymphocyte] |
MODEL1901210001
— v0.0.1This model is described within the paper: A G1 arrest due to proteostasis decline delimits replicative lifespan in yeast…
Details
Loss of proteostasis and cellular senescence are key hallmarks of aging, but direct cause-effect relationships are not well understood. We show that most yeast cells arrest in G1 before death with low nuclear levels of Cln3, a key G1 cyclin extremely sensitive to chaperone status. Chaperone availability is seriously compromised in aged cells, and the G1 arrest coincides with massive aggregation of a metastable chaperone-activity reporter. Moreover, G1-cyclin overexpression increases lifespan in a chaperone-dependent manner. As a key prediction of a model integrating autocatalytic protein aggregation and a minimal Start network, enforced protein aggregation causes a severe reduction in lifespan, an effect that is greatly alleviated by increased expression of specific chaperones or cyclin Cln3. Overall, our data show that proteostasis breakdown, by compromising chaperone activity and G1-cyclin function, causes an irreversible arrest in G1, configuring a molecular pathway postulating proteostasis decay as a key contributing effector of cell senescence. link: http://identifiers.org/pubmed/31518229
MODEL1508170000
— v0.0.1Morgan2016 - Dynamics of cholesterol metabolism and ageingThis model is described in the article: [Mathematically model…
Details
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the UK. This condition becomes increasingly prevalent during ageing; 34.1% and 29.8% of males and females respectively, over 75 years of age have an underlying cardiovascular problem. The dysregulation of cholesterol metabolism is inextricably correlated with cardiovascular health and for this reason low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C) are routinely used as biomarkers of CVD risk. The aim of this work was to use mathematical modelling to explore how cholesterol metabolism is affected by the ageing process. To do this we updated a previously published whole-body mathematical model of cholesterol metabolism to include an additional 96 mechanisms that are fundamental to this biological system. Additional mechanisms were added to cholesterol absorption, cholesterol synthesis, reverse cholesterol transport (RCT), bile acid synthesis, and their enterohepatic circulation. The sensitivity of the model was explored by the use of both local and global parameter scans. In addition, acute cholesterol feeding was used to explore the effectiveness of the regulatory mechanisms which are responsible for maintaining whole-body cholesterol balance. It was found that our model behaves as a hypo-responder to cholesterol feeding, while both the hepatic and intestinal pools of cholesterol increased significantly. The model was also used to explore the effects of ageing in tandem with three different cholesterol ester transfer protein (CETP) genotypes. Ageing in the presence of an atheroprotective CETP genotype, conferring low CETP activity, resulted in a 0.6% increase in LDL-C. In comparison, ageing with a genotype reflective of high CETP activity, resulted in a 1.6% increase in LDL-C. Thus, the model has illustrated the importance of CETP genotypes such as I405V, and their potential role in healthy ageing. link: http://identifiers.org/pubmed/27157786
BIOMD0000000406
— v0.0.1This model is from the article: Overexpression limits of fission yeast cell-cycle regulators in vivo and in silico.…
Details
Cellular systems are generally robust against fluctuations of intracellular parameters such as gene expression level. However, little is known about expression limits of genes required to halt cellular systems. In this study, using the fission yeast Schizosaccharomyces pombe, we developed a genetic 'tug-of-war' (gTOW) method to assess the overexpression limit of certain genes. Using gTOW, we determined copy number limits for 31 cell-cycle regulators; the limits varied from 1 to >100. Comparison with orthologs of the budding yeast Saccharomyces cerevisiae suggested the presence of a conserved fragile core in the eukaryotic cell cycle. Robustness profiles of networks regulating cytokinesis in both yeasts (septation-initiation network (SIN) and mitotic exit network (MEN)) were quite different, probably reflecting differences in their physiologic functions. Fragility in the regulation of GTPase spg1 was due to dosage imbalance against GTPase-activating protein (GAP) byr4. Using the gTOW data, we modified a mathematical model and successfully reproduced the robustness of the S. pombe cell cycle with the model. link: http://identifiers.org/pubmed/22146300
Parameters:
Name | Description |
---|---|
kini_dash2 = 10.0; kini_dash3 = 0.0; preRC = 0.0; kini_dash = 10.0 | Reaction: s89 => s90; s67, s56, s63, Rate Law: (kini_dash*s56+kini_dash2*s67+kini_dash3*s63)*preRC |
kscig = 0.002; kscig_dash = 0.04; Cdc10T = 1.0 | Reaction: s55 => s67; s71, Rate Law: kscig*Cdc10T+kscig_dash*s71 |
kpyp2 = 0.01; k25 = 0.0 | Reaction: s60 => s56; s83, s64, Rate Law: (kpyp2+k25)*s60 |
kpyp = 0.6; beta = 10.0; UDNA = 0.0; k25 = 0.0; k255 = 0.1 | Reaction: s153 => s149; s64, s83, Rate Law: k25*k255*s153+kpyp*s153/(1+beta*UDNA) |
kisrw_dash = 40.0; Puc1 = 1.0; kisrw_dash2 = 1.0; kisrw_dash4 = 4.0; kisrw = 1.5; Jisrw = 0.01; kisrw_dash3 = 4.0 | Reaction: s47 => s65; s56, s49, s75, s67, Rate Law: (kisrw+kisrw_dash*s67+kisrw_dash2*s56+kisrw_dash3*Puc1+kisrw_dash4*s75)*s47/(Jisrw+s47) |
kipre = 1.0; n = 4.0; kipre_dash = 1.0; kori = 125.0; Jipre = 0.01 | Reaction: s91 => s92; s67, s56, s63, Rate Law: kori/(1+((kipre*s56+kipre_dash*s67)/Jipre)^n)*s91 |
ksrum = 1.0 | Reaction: s52 => s166, Rate Law: ksrum |
Vdc18 = 0.0 | Reaction: s84 => s88; s130, Rate Law: Vdc18*s84 |
Vdrum = 0.0 | Reaction: s161 => s56 + s61; s4, Rate Law: Vdrum*s161 |
kdci1_dash = 5.0; kdci1 = 0.1; kdci1_dash2 = 0.2 | Reaction: s75 => s77; s48, s47, Rate Law: (kdci1+kdci1_dash*s48+kdci1_dash2*s47)*s75 |
kasrw = 1.25; kasrw_dash = 30.0; Jasrw = 0.01; Srw1T = 1.0 | Reaction: s65 => s47; s48, Rate Law: (kasrw+kasrw_dash*s48)*(Srw1T-s47)/(Jasrw+(Srw1T-s47)) |
kic10 = 0.01; Jic10 = 0.01; kic10_dash = 3.0 | Reaction: s71 => s70; s67, Rate Law: (kic10+kic10_dash*s67)*s71/(Jic10+s71) |
ksflp = 0.0015; ksflp_dash = 0.015 | Reaction: s78 => s81; s48, Rate Law: ksflp+ksflp_dash*s48 |
kac10 = 0.125; Jac10 = 0.01; Cdc10T = 1.0 | Reaction: s70 => s71, Rate Law: kac10*(Cdc10T-s71)/(Jac10+(Cdc10T-s71)) |
Vi25 = 0.3; UDNA = 0.0; Vi25_dash2 = 1.0; Ji25 = 0.03; Vi25_dash = 0.24 | Reaction: s83 => s82; s81, s157, Rate Law: (Vi25_dash+Vi25_dash2*s81+Vi25*UDNA)*s83/(Ji25+s83) |
lcm = 1.0; lcp = 3.0 | Reaction: s166 + s67 => s149, Rate Law: lcp*s67*s166-lcm*s149 |
kaie = 0.0975; kaie_dash = 0.05; Jaie = 0.01 | Reaction: s51 => s50; s75, s56, Rate Law: (kaie*s56+kaie_dash*s75)*(1-s50)/(Jaie+(1-s50)) |
Jawee = 0.04; Vawee_dash = 0.24; Wee1T = 1.0; Vawee_dash2 = 1.0 | Reaction: s79 => s80; s81, Rate Law: (Vawee_dash+Vawee_dash2*s81)*(Wee1T-s80)/(Jawee+(Wee1T-s80)) |
ksc18 = 0.005; ksc18_dash = 0.075; Cdc10T = 1.0 | Reaction: s85 => s84; s71, Rate Law: ksc18*((Cdc10T-s71)+s71)+ksc18_dash*s71 |
lp = 500.0; lm = 100.0 | Reaction: s56 + s166 => s161, Rate Law: lp*s56*s166-lm*s161 |
Cdc25T = 1.0; Ja25 = 0.03; Va25_dash2 = 1.0 | Reaction: s82 => s83; s56, Rate Law: Va25_dash2*s56*(Cdc25T-s83)/(Ja25+(Cdc25T-s83)) |
Vamik_dash = 0.75; Vamik = 0.25; Cdc10T = 1.0 | Reaction: s73 => s72; s71, Rate Law: Vamik*Cdc10T+Vamik_dash*s71 |
kdcig_dash = 1.0; kdcig = 0.02 | Reaction: s149 => s166 + s94; s48, Rate Law: (kdcig+kdcig_dash*s48)*s149 |
kscyc = 0.03 | Reaction: s57 => s56, Rate Law: kscyc |
kdflp = 0.1 | Reaction: s81 => s93, Rate Law: kdflp*s81 |
ksci1 = 0.0015 | Reaction: s76 => s75, Rate Law: ksci1 |
Vimik_dash3 = 0.25; Vimik_dash2 = 10.0; Vimik = 0.75; Vimik_dash = 10.0 | Reaction: s72 => s74; s67, s56, s60, Rate Law: (Vimik+Vimik_dash*s67+Vimik_dash2*s56+Vimik_dash3*s60)*s72 |
Jislp = 0.01; kislp = 0.2 | Reaction: s48 => s66, Rate Law: kislp*s48/(Jislp+s48) |
Jiie = 0.01; kiie = 0.04 | Reaction: s50 => s51, Rate Law: kiie*s50/(Jiie+s50) |
kmik_dash2 = 4.0 | Reaction: s149 => s153; s72, Rate Law: kmik_dash2*s72*s149 |
Vdcyc = 0.0 | Reaction: s161 => s166 + s46; s9, Rate Law: Vdcyc*s161 |
krepl = 2.0 | Reaction: s90 => s91, Rate Law: krepl*s90 |
kaslp = 1.0; Slp1T = 1.0; Jaslp = 0.01 | Reaction: s66 => s48; s50, Rate Law: kaslp*s50*(Slp1T-s48)/(Jaslp+(Slp1T-s48)) |
Viwee_dash2 = 1.0; Jiwee = 0.03; Viwee_dash = 0.0 | Reaction: s80 => s79; s56, Rate Law: (Viwee_dash+Viwee_dash2*s56)*s80/(Jiwee+s80) |
kmik_dash = 0.01; kwee = 0.0 | Reaction: s161 => s137; s80, s72, Rate Law: (kmik_dash*s72+kwee)*s161 |
States:
Name | Description |
---|---|
s78 | [mRNA cleavage and polyadenylation factor clp1] |
s76 | [G2/mitotic-specific cyclin cig1] |
s83 | [M-phase inducer phosphatase] |
s92 | [deoxyribonucleic acid] |
s57 | [G2/mitotic-specific cyclin cdc13] |
s153 | [Cyclin-dependent kinase inhibitor rum1; G2/mitotic-specific cyclin cig2] |
s50 | IE |
s93 | sa370_degraded |
s71 | [Start control protein cdc10] |
s47 | [WD repeat-containing protein srw1] |
s81 | [mRNA cleavage and polyadenylation factor clp1] |
s52 | [Cyclin-dependent kinase inhibitor rum1] |
s72 | [Mitosis inhibitor protein kinase mik1] |
s46 | sa4_degraded |
s77 | sa353_degraded |
s70 | [Start control protein cdc10] |
s89 | [nuclear pre-replicative complex] |
s51 | iIE |
s166 | [Cyclin-dependent kinase inhibitor rum1] |
s48 | [WD repeat-containing protein slp1] |
s67 | [G2/mitotic-specific cyclin cig2] |
s55 | [G2/mitotic-specific cyclin cig2] |
s84 | [Cell division control protein 18] |
s149 | [G2/mitotic-specific cyclin cig2; Cyclin-dependent kinase inhibitor rum1] |
s91 | [deoxyribonucleic acid] |
s80 | [Mitosis inhibitor protein kinase wee1] |
s75 | [G2/mitotic-specific cyclin cig1] |
s94 | sa44_degraded |
s73 | [Mitosis inhibitor protein kinase mik1] |
s161 | [Cyclin-dependent kinase inhibitor rum1; G2/mitotic-specific cyclin cdc13] |
s56 | [G2/mitotic-specific cyclin cdc13] |
s79 | [Mitosis inhibitor protein kinase wee1] |
s82 | [M-phase inducer phosphatase] |
s137 | [G2/mitotic-specific cyclin cdc13; Cyclin-dependent kinase inhibitor rum1] |
s74 | sa347_degraded |
s90 | [origin recognition complex] |
s88 | sa386_degraded |
s66 | [WD repeat-containing protein slp1] |
s85 | [Cell division control protein 18] |
s60 | [G2/mitotic-specific cyclin cdc13; Phosphoprotein] |
s65 | [WD repeat-containing protein srw1] |
BIOMD0000000324
— v0.0.1This is the full model (eq. 1 and 2) of the voltage oscillations in barnacle muscle fibers described in the article: Vo…
Details
Barnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. link: http://identifiers.org/pubmed/7260316
BIOMD0000000280
— v0.0.1This is the reduced model of the voltage oscillations in barnacle muscle fibers, generally known as the Morris-Lecar mod…
Details
Barnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. link: http://identifiers.org/pubmed/7260316
BIOMD0000000150
— v0.0.1Notes from the original DOCQS curator: In this version of the CDK2/Cyclin A complex activation there is discrepancy i…
Details
Eukaryotic cell cycle progression is controlled by the ordered action of cyclin-dependent kinases, activation of which occurs through the binding of the cyclin to the Cdk followed by phosphorylation of a conserved threonine in the T-loop of the Cdk by Cdk-activating kinase (CAK). Despite our understanding of the structural changes, which occur upon Cdk/cyclin formation and activation, little is known about the dynamics of the molecular events involved. We have characterized the mechanism of Cdk2/cyclin A complex formation and activation at the molecular and dynamic level by rapid kinetics and demonstrate here that it is a two-step process. The first step involves the rapid association between the PSTAIRE helix of Cdk2 and helices 3 and 5 of the cyclin to yield an intermediate complex in which the threonine in the T-loop is not accessible for phosphorylation. Additional contacts between the C-lobe of the Cdk and the N-terminal helix of the cyclin then induce the isomerization of the Cdk into a fully mature form by promoting the exposure of the T-loop for phosphorylation by CAK and the formation of the substrate binding site. This conformational change is selective for the cyclin partner. link: http://identifiers.org/pubmed/11959850
Parameters:
Name | Description |
---|---|
kf=0.813; kb=0.557 | Reaction: CDK2cycA => CDK2cycA_star_, Rate Law: kf*CDK2cycA*geometry-kb*CDK2cycA_star_*geometry |
kb=25.0; kf=1.9E7 | Reaction: Cdk2 + CyclinA => CDK2cycA, Rate Law: kf*Cdk2*CyclinA*geometry-kb*CDK2cycA*geometry |
States:
Name | Description |
---|---|
CyclinA | [IPR015453] |
CDK2cycA star | [Cyclin-dependent kinase 1; IPR015453] |
Cdk2 | [Cyclin-dependent kinase 1] |
CDK2cycA | [Cyclin-dependent kinase 1; IPR015453] |
BIOMD0000000567
— v0.0.1Morris2008 - Fitting protein aggregation data via F-W 2-step mechanismThis model is described in the article: [Fitting…
Details
The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/"Ockham's razor" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A –> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B –> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Saitô and co-workers. In particular, the kinetic equation for Saitô's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Saitô and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments. link: http://identifiers.org/pubmed/18247636
Parameters:
Name | Description |
---|---|
k1 = 4.0E-5 | Reaction: A => B; A, Rate Law: Brain*k1*A |
k2 = 1.57E-6; k1 = 4.0E-5; A0 = 184713.375796178 | Reaction: B = A0-(k1/k2+A0)/(1+k1/(k2*A0)*exp((k1+k2*A0)*time)), Rate Law: missing |
k2 = 1.57E-6 | Reaction: A + B => B; A, B, Rate Law: Brain*k2*A*B |
States:
Name | Description |
---|---|
B | [PR:P04156] |
A | [PR:P04156] |
BIOMD0000000566
— v0.0.1Morris2009 - α-Synuclein aggregation variable temperature and pHThis model is described in the article: [Alpha-synuclei…
Details
The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an "Ockham's razor"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A–>B, rate constant k(1)) and autocatalytic growth (A+B–>2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)xA (where k(2) is the rate constant for autocatalytic growth and A is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions. link: http://identifiers.org/pubmed/19101068
Parameters:
Name | Description |
---|---|
k1 = 8.0E-6; k2 = 0.034; A0 = 3.55 | Reaction: B = A0-(k1/k2+A0)/(1+k1/(k2*A0)*exp((k1+k2*A0)*time)), Rate Law: missing |
k1 = 8.0E-6 | Reaction: A => B; A, Rate Law: Brain*k1*A |
k2 = 0.034 | Reaction: A + B => B; A, B, Rate Law: Brain*k2*A*B |
States:
Name | Description |
---|---|
B | [Alpha-synuclein] |
A | [Alpha-synuclein] |
BIOMD0000000018
— v0.0.1Morrison1989 - Folate CycleThe model describes the folate cycle kinetics in breast cancer cells.This model is described…
Details
A mathematical description of polyglutamated folate kinetics for human breast carcinoma cells (MCF-7) has been formulated based upon experimental folate, methotrexate (MTX), purine, and pyrimidine pool sizes as well as reaction rate parameters obtained from intact MCF-7 cells and their enzyme isolates. The schema accounts for the interconversion of highly polyglutamated tetrahydrofolate, 5-methyl-FH4, 5-10-CH2FH4, dihydrofolate (FH2), 10-formyl-FH4 (FFH4), and 10-formyl-FH2 (FFH2), as well as formation and transport of the MTX polyglutamates. Inhibition mechanisms have been chosen to reproduce all observed non-, un-, and pure competition inhibition patterns. Steady state folate concentrations and thymidylate and purine synthesis rates in drug-free intact cells were used to determine normal folate Vmax values. The resulting average-cell folate model, examined for its ability to predict folate pool behavior following exposure to 1 microM MTX over 21 h, agreed well with the experiment, including a relative preservation of the FFH4 and CH2FH4 pools. The results depend strongly on thymidylate synthase (TS) reaction mechanism, especially the assumption that MTX di- and triglutamates inhibit TS synthesis as greatly in the intact cell as they do with purified enzyme. The effects of cell cycle dependence of TS and dihydrofolate reductase activities were also examined by introducing G- to S-phase activity ratios of these enzymes into the model. For activity ratios down to at least 5%, cell population averaged folate pools were only slightly affected, while CH2FH4 pools in S-phase cells were reduced to as little as 10% of control values. Significantly, these folate pool dynamics were indicated to arise from both direct inhibition by MTX polyglutamates as well as inhibition by elevated levels of polyglutamated FH2 and FFH2. link: http://identifiers.org/pubmed/2732237
Parameters:
Name | Description |
---|---|
hp=23.2 | Reaction: FH4 + HCHO => CH2FH4, Rate Law: cell*hp*FH4*HCHO |
Vm=4.65 | Reaction: MTX1 =>, Rate Law: cell*Vm*MTX1 |
Vm=0.42 | Reaction: MTX3b => MTX3 + DHFRf, Rate Law: cell*Vm*MTX3b |
Km2=100.0; Km1=100.0; Vm=4656.0 | Reaction: FGAR => AICAR; glutamine, Rate Law: cell*Vm*glutamine/Km1/(1+glutamine/Km1)*FGAR/Km2/(1+FGAR/Km2) |
Vm=0.118 | Reaction: MTX3 => MTX4, Rate Law: cell*Vm*MTX3 |
Vm=163000.0 | Reaction: MTX4 + DHFRf => MTX4b, Rate Law: cell*Vm*DHFRf*MTX4 |
Ki1=5.0; Ki24=31.0; Km1=4.9; Ki1f=1.0; Ki23=43.0; Vm=4126.0; Km2=52.0; Ki21=84.0; Ki22=60.0; Ki25=22.0 | Reaction: CHOFH4 + GAR => FGAR + FH4; FH2f, FFH2, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CHOFH4*GAR/(GAR*CHOFH4+CHOFH4*Km2+(GAR+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1+FFH2/Ki1f)) |
Vm=23100.0 | Reaction: MTX1 + DHFRf => MTX1b, Rate Law: cell*Vm*DHFRf*MTX1 |
Vm=44300.0 | Reaction: MTX2 + DHFRf => MTX2b, Rate Law: cell*Vm*DHFRf*MTX2 |
Km2=210.0; Vm=18330.0; Km1=1.7 | Reaction: FH4 + serine => CH2FH4, Rate Law: cell*Vm*serine/Km2/(1+serine/Km2)*FH4/Km1/(1+FH4/Km1) |
Vm=314000.0 | Reaction: MTX5 + DHFRf => MTX5b, Rate Law: cell*Vm*DHFRf*MTX5 |
Ki1=0.4; Vm=224.8; Ki21=59.0; Ki22=21.3; Ki24=2.77; Ki25=1.0; Km1=50.0; Km2=50.0; Ki23=7.68 | Reaction: CH2FH4 + NADPH => CH3FH4; FH2f, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CH2FH4*NADPH/(NADPH*CH2FH4+CH2FH4*Km2+(NADPH+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1)) |
Vm=0.129 | Reaction: MTX1 => MTX2, Rate Law: cell*Vm*MTX1 |
Vm=0.0 | Reaction: MTX2 =>, Rate Law: cell*Vm*MTX2 |
Vm=0.03 | Reaction: DHFRf => ; FH2b, Rate Law: Vm*cell*(DHFRf+FH2b) |
Vm=1.22E7; Km1=3200.0; Km2=10000.0 | Reaction: CH2FH4 => FH4; glycine, Rate Law: cell*Vm*glycine/Km2/(1+glycine/Km2)*CH2FH4/Km1/(1+CH2FH4/Km1) |
kter=2109.4 | Reaction: FH2f => FH4; FH2b, Rate Law: cell*kter*FH2b |
Vm=0.195 | Reaction: MTX2 => MTX1, Rate Law: cell*Vm*MTX2 |
Ki1=2.89; Ki22=31.5; Ki25=5.89; Ki23=2.33; Km2=24.0; Vm=31675.0; Km1=5.5; Ki1f=5.3; Ki24=3.61; Ki21=40.0 | Reaction: CHOFH4 + AICAR => FH4; FH2f, FFH2, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CHOFH4*AICAR/(AICAR*CHOFH4+CHOFH4*Km2+(AICAR+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1+FFH2/Ki1f)) |
Vm=0.369 | Reaction: MTX2 => MTX3, Rate Law: cell*Vm*MTX2 |
Ki1=2.89; Ki22=31.5; Ki25=5.89; Ki23=2.33; Km2=24.0; Vm=9503.0; Ki24=3.61; Km1=5.3; Ki21=40.0; Ki1f=5.5 | Reaction: FFH2 + AICAR => FH2f; FH2f, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*FFH2*AICAR/(AICAR*FFH2+FFH2*Km2+(AICAR+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1+FFH2/Ki1f)) |
Vm=85100.0 | Reaction: MTX3 + DHFRf => MTX3b, Rate Law: cell*Vm*DHFRf*MTX3 |
Vm=0.031 | Reaction: MTX4 => MTX3, Rate Law: cell*Vm*MTX4 |
Vm=65.0 | Reaction: FH2f => FFH2, Rate Law: cell*Vm*FH2f |
Vm=22600.0; Km1=125.0; Km2=2900.0 | Reaction: CH3FH4 + homocysteine => FH4, Rate Law: cell*Vm*homocysteine/Km2/(1+homocysteine/Km2)*CH3FH4/Km1/(1+CH3FH4/Km1) |
Vm=0.185 | Reaction: MTX4 => MTX5, Rate Law: cell*Vm*MTX4 |
Vm=0.191 | Reaction: MTX5 => MTX4, Rate Law: cell*Vm*MTX5 |
Km2=21.8; Km1=3.0; Vm=68500.0 | Reaction: CH2FH4 + NADP => CHOFH4, Rate Law: cell*Vm*CH2FH4/Km1/(1+CH2FH4/Km1)*NADP/Km2/(1+NADP/Km2) |
Ki1f=1.6; Ki24=0.065; Ki1=3.0; Ki25=0.047; Ki22=0.08; Vm=58.0; Km1=2.5; Ki21=13.0; Ki23=0.07; Km2=1.8 | Reaction: CH2FH4 + dUMP => FH2f; FH2f, FFH2, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CH2FH4*dUMP/(dUMP*CH2FH4*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1)+Km1*dUMP*(FFH2/Ki1f*MTX1/Ki21+(1+FFH2/Ki1f)*(1+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1))+Km1*Km2*(1+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1)) |
Km2=56.0; Vm=3600.0; Km1=230.0; Km3=1600.0 | Reaction: FH4 + formate + ATP => CHOFH4, Rate Law: cell*Vm/((1+Km1/FH4)*(1+Km2/ATP)*(1+Km3/formate)) |
Vm=82.2; Km=8.2 | Reaction: EMTX => MTX1, Rate Law: ext*Vm*EMTX/(Km+EMTX) |
hl=0.3 | Reaction: CH2FH4 => FH4 + HCHO, Rate Law: cell*hl*CH2FH4 |
Vm=0.025 | Reaction: MTX3 => MTX2, Rate Law: cell*Vm*MTX3 |
k0=0.0192; k1=0.04416 | Reaction: => DHFRf; EMTX, Rate Law: cell*(k0+k1*EMTX) |
Vm=0.063 | Reaction: MTX3 =>, Rate Law: cell*Vm*MTX3 |
States:
Name | Description |
---|---|
CH3FH4 | [5-methyltetrahydrofolic acid; 5-Methyltetrahydrofolate] |
FH4 | [5,6,7,8-tetrahydrofolic acid; Tetrahydrofolate] |
MTX4b | [Methotrexate] |
MTX3 | [Methotrexate] |
DHFRf | [Dihydrofolate reductase] |
AICAR | [AICA ribonucleotide; 1-(5'-Phosphoribosyl)-5-amino-4-imidazolecarboxamide] |
NADPH | [NADPH; NADPH] |
MTX5 | [Methotrexate] |
MTX2b | [Methotrexate] |
MTX1 | [Methotrexate] |
MTX4 | [Methotrexate] |
homocysteine | [homocysteine; Homocysteine] |
DHFRtot | [Dihydrofolate reductase] |
FGAR | [5'-Phosphoribosyl-N-formylglycinamide] |
GAR | [5'-Phosphoribosylglycinamide] |
CHOFH4 | [10-formyltetrahydrofolic acid; 10-Formyltetrahydrofolate] |
MTX2 | [Methotrexate] |
FFH2 | [10-formyldihydrofolic acid; 10-Formyldihydrofolate] |
MTX3b | [Methotrexate] |
FH2f | [dihydrofolic acid; Dihydrofolate] |
CH2FH4 | [(6R)-5,10-methylenetetrahydrofolate(2-); 5,10-Methylenetetrahydrofolate] |
MTX5b | [Methotrexate] |
MTX1b | [Methotrexate] |
HCHO | [formaldehyde; Formaldehyde] |
dUMP | [dUMP; dUMP] |
BIOMD0000000426
— v0.0.1Mosca2012 - Central Carbon Metabolism Regulated by AKTThe role of the PI3K/Akt/PKB signalling pathway in oncogenesis has…
Details
Signal transduction and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modeled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production, and nucleotide biosynthesis. We used a computational model to compare two metabolic states generated by two different levels of signaling through the PI3K/Akt/mTOR pathway: one of the two states represents the metabolism of a growing cancer cell characterized by aerobic glycolysis and cellular biosynthesis, while the other state represents the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism. Biochemical reactions that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism. link: http://identifiers.org/pubmed/23181020
Parameters:
Name | Description |
---|---|
Kapp=195172.0; Kadp=0.4; Katp=0.86; parameter_44 = 27.81; Kiatp=2.5; L=1.0; parameter_17 = 1000.0; Kfbp=4.0E-4; Kpyr=10.0; Kpep=0.014 | Reaction: species_30 + species_3 => species_31 + species_4; species_6, species_3, species_30, species_4, species_6, species_31, Rate Law: compartment_1*parameter_44*(parameter_17*species_3/Kadp/(1+parameter_17*species_3/Kadp)*parameter_17*species_30/Kpep*(1+parameter_17*species_30/Kpep)^3/(L*(1+parameter_17*species_4/Kiatp)^4/(1+parameter_17*species_6/Kfbp)^4+(1+parameter_17*species_30/Kpep)^4)-parameter_17*species_4*parameter_17*species_31/(Katp*Kpyr*Kapp)/(parameter_17*species_4/Katp+parameter_17*species_31/Kpyr+parameter_17*species_4*parameter_17*species_31/(Katp*Kpyr)+1)) |
Kq=0.0035; parameter_31 = 86.85; Kapp=651.0; Ka=1.0E-4; Kb=0.0011; Kp=2.0E-5 | Reaction: species_1 + species_4 => species_2 + species_3; species_1, species_4, species_2, species_3, Rate Law: compartment_1*parameter_31/(Ka*Kb)*(species_1*species_4-species_2*species_3/Kapp)/(1+species_1/Ka+species_4/Kb+species_1*species_4/(Ka*Kb)+species_2/Kp+species_3/Kq+species_2*species_3/(Kp*Kq)+species_1*species_3/(Ka*Kq)+species_2*species_4/(Kp*Kb)) |
alfa=1.0; beta=1.0; parameter_45 = 340.3; parameter_83 = 0.0047; parameter_85 = 2.0E-6; parameter_86 = 3.0E-4; parameter_84 = 7.0E-5; parameter_26 = 54.0471638909003 | Reaction: species_31 + species_18 => species_32 + species_19; species_18, species_31, species_32, species_19, Rate Law: compartment_1*(parameter_45*species_18*species_31/(alfa*parameter_85*parameter_86)-parameter_26*species_32*species_19/(beta*parameter_83*parameter_84))/(1+species_18/parameter_85+species_31/parameter_86+species_18*species_31/(alfa*parameter_85*parameter_86)+species_32*species_19/(beta*parameter_83*parameter_84)+species_32/parameter_83+species_19/parameter_84) |
Kery4p=1.0E-6; parameter_81 = 5.0E-5; Kfbp=6.0E-5; parameter_32 = 7778.0; parameter_82 = 4.0E-4; Kpg=1.5E-5; parameter_13 = 17486.5107913669 | Reaction: species_2 => species_5; species_7, species_6, species_8, species_2, species_5, species_7, species_6, species_8, Rate Law: compartment_1*(parameter_32*species_2/parameter_82-parameter_13*species_5/parameter_81)/(1+species_2/parameter_82+species_5/parameter_81+species_7/Kery4p+species_6/Kfbp+species_8/Kpg) |
Keq=2.26; Vf=141.2 | Reaction: species_3 => species_4 + species_20; species_3, species_4, species_20, Rate Law: compartment_1*Vf*species_3^2*(1-species_4*species_20/Keq)/(((1+species_3)^2+(1+species_4)*(1+species_20))-1) |
K3=1.733E-7; K6=0.4653; K2=4.765E-8; K7=2.524; Vmax=58.27; K5=0.8683; K1=8.23E-9; Keq_TAL=2.703; K4=6.095E-9 | Reaction: species_17 + species_16 => species_5 + species_7; species_17, species_16, species_7, species_5, Rate Law: compartment_1*Vmax*(species_17*species_16-species_7*species_5/Keq_TAL)/((K1+species_16)*species_17+(K2+K6*species_5)*species_16+(K3+K5*species_5)*species_7+K4*species_5+K7*species_17*species_7) |
parameter_30 = 23.03; keq=1.0; KGlc=0.0093; KGlc_e=0.01 | Reaction: species_9 => species_1; species_9, species_1, Rate Law: compartment_1*parameter_30*(species_9-species_1/keq)/(KGlc_e*(1+species_1/KGlc)+species_9) |
KNADP=3.67E-9; KG6P=6.67E-8; KATP=7.49E-7; Kapp=2000.0; KNADPH=3.12E-9; KPGA23=2.289E-6; parameter_33 = 1.008 | Reaction: species_2 + species_10 => species_8 + species_11; species_4, species_12, species_2, species_10, species_8, species_11, species_4, species_12, Rate Law: compartment_1*parameter_33/KG6P/KNADP*(species_2*species_10-species_8*species_11/Kapp)/(1+species_10*(1+species_2/KG6P)/KNADP+species_4/KATP+species_11/KNADPH+species_12/KPGA23) |
KRu5P=1.9E-7; KX5P=5.0E-7; Keq_RUPE=2.7; Vmax=1.471 | Reaction: species_13 => species_14; species_13, species_14, Rate Law: compartment_1*Vmax*(species_13-species_14/Keq_RUPE)/(species_13+KRu5P*(1+species_14/KX5P)) |
parameter_57 = 6.3E-5; parameter_15 = 0.203875968992248; parameter_56 = 3.0E-5; parameter_55 = 7.364 | Reaction: species_22 => species_2; species_22, species_2, Rate Law: compartment_1*(parameter_55*species_22/parameter_57-parameter_15*species_2/parameter_56)/(1+species_22/parameter_57+species_2/parameter_56) |
Vmax=0.7646; Keq_R5PI=3.0; KRu5P=7.8E-7; KR5P=2.2E-6 | Reaction: species_13 => species_15; species_13, species_15, Rate Law: compartment_1*Vmax*(species_13-species_15/Keq_R5PI)/(species_13+KRu5P*(1+species_15/KR5P)) |
KiPi=0.0047; KGLYb=1.5E-4; parameter_4 = 0.0177545693277311; parameter_60 = 0.0101; parameter_61 = 0.0017; parameter_58 = 0.03347; parameter_59 = 1.5E-4; parameter_62 = 0.004 | Reaction: species_24 + species_23 => species_24 + species_22; species_24, species_23, species_22, Rate Law: compartment_1*(parameter_58*species_24*species_23/(parameter_61*parameter_62)-parameter_4*species_24*species_22/(KGLYb*parameter_60))/(1+species_24/parameter_61+species_23/KiPi+species_24/parameter_59+species_22/parameter_60+species_24*species_23/(parameter_61*KiPi)+species_24*species_22/(parameter_59*parameter_60)) |
Kapp=100000.0; KR5P=5.7E-7; Vmax=0.5104; KATP=3.0E-8 | Reaction: species_15 + species_4 => species_20 + species_21; species_15, species_4, species_21, species_20, Rate Law: compartment_1*Vmax*(species_15*species_4-species_21*species_20/Kapp)/((KATP+species_4)*(KR5P+species_15)) |
Kf=17400.0; Kr=158.0; Keq=267100.0; parameter_41 = 32040.0; parameter_17 = 1000.0 | Reaction: species_22 + species_4 => species_24 + species_3 + species_23; species_22, species_4, species_24, species_23, species_3, Rate Law: compartment_1*parameter_41/Kf*parameter_17*species_22*parameter_17*species_4*parameter_17*species_24*(1-(parameter_17*species_23)^2*parameter_17*species_3/(parameter_17*species_22*parameter_17*species_4*Keq))/(1+parameter_17*species_22*parameter_17*species_4*parameter_17*species_24/Kf+parameter_17*species_24*(parameter_17*species_23)^2*parameter_17*species_3/Kr) |
Keq_TKL2=29.7; K3=5.48E-8; parameter_36 = 0.1761; K1=1.84E-9; K7=0.215; K6=0.122; K4=3.0E-10; K5=0.0287; K2=3.055E-7 | Reaction: species_14 + species_7 => species_16 + species_5; species_7, species_14, species_16, species_5, Rate Law: compartment_1*parameter_36*(species_7*species_14-species_16*species_5/Keq_TKL2)/((K1+species_7)*species_14+(K2+K6*species_5)*species_7+(K3+K5*species_5)*species_16+K4*species_5+K7*species_14*species_16) |
Katp=2.1E-5; parameter_17 = 1000.0; alfa=0.32; Kfbp=5.0; Kf26bp=8.4E-7; parameter_42 = 107.6; Kf6p=1.0; Kcit=6.8; L=4.1; Kapp=247.0; Kiatp=20.0; Kadp=5.0; beta=0.98 | Reaction: species_5 + species_4 => species_6 + species_3; species_26, species_25, species_4, species_26, species_5, species_25, species_3, species_6, Rate Law: compartment_1*parameter_42*parameter_17*species_4/Katp/(1+parameter_17*species_4/Katp)*(1+beta*parameter_17*species_26/(alfa*Kf26bp))/(1+parameter_17*species_26/(alfa*Kf26bp))*(parameter_17*species_5*(1+parameter_17*species_26/(alfa*Kf26bp))/(Kf6p*(1+parameter_17*species_26/Kf26bp))*(1+parameter_17*species_5*(1+parameter_17*species_26/(alfa*Kf26bp))/(Kf6p*(1+parameter_17*species_26/Kf26bp)))^3/(L*(1+parameter_17*species_25/Kcit)^4*(1+parameter_17*species_4/Kiatp)^4/(1+parameter_17*species_26/Kf26bp)^4+(1+parameter_17*species_5*(1+parameter_17*species_26/(alfa*Kf26bp))/(Kf6p*(1+parameter_17*species_26/Kf26bp)))^4)-parameter_17*species_3*parameter_17*species_6/(Kadp*Kfbp*Kapp)/(parameter_17*species_3/Kadp+parameter_17*species_6/Kfbp+parameter_17*species_3*parameter_17*species_6/(Kadp*Kfbp)+1)) |
k1=6210.0 | Reaction: species_4 => species_3 + species_23; species_4, Rate Law: compartment_1*k1*species_4 |
parameter_2 = 1.932E-5 | Reaction: species_10 = parameter_2-species_11, Rate Law: missing |
K6=0.00774; K4=4.96E-9; K1=4.177E-7; Keq_TKL=2.08; parameter_35 = 1056.0; K5=0.41139; K2=3.055E-7; K3=1.2432E-5; K7=48.8 | Reaction: species_15 + species_14 => species_16 + species_17; species_15, species_14, species_16, species_17, Rate Law: compartment_1*parameter_35*(species_15*species_14-species_16*species_17/Keq_TKL)/((K1+species_15)*species_14+(K2+K6*species_17)*species_15+(K3+K5*species_17)*species_16+K4*species_17+K7*species_14*species_16) |
parameter_8 = 11.5595061728395; parameter_68 = 8.0E-5; parameter_69 = 1.6E-4; parameter_70 = 9.0E-6; parameter_37 = 14.63 | Reaction: species_6 => species_16 + species_27; species_6, species_27, species_16, Rate Law: compartment_1*(parameter_37*species_6/parameter_70-parameter_8*species_27*species_16/(parameter_68*parameter_69))/(1+species_6/parameter_70+species_27/parameter_68+species_16/parameter_69+species_27*species_16/(parameter_68*parameter_69)) |
parameter_9 = 49.2079666512274; parameter_38 = 5.976; parameter_72 = 5.1E-4; parameter_71 = 0.0016 | Reaction: species_16 => species_27; species_16, species_27, Rate Law: compartment_1*(parameter_38*species_16/parameter_72-parameter_9*species_27/parameter_71)/(1+species_16/parameter_72+species_27/parameter_71) |
parameter_46 = 4982000.0; Keq=300.0 | Reaction: species_18 => species_19; species_18, species_19, Rate Law: compartment_1*parameter_46*species_18*(1-species_19/(species_18*Keq))/((1+species_18+1+species_19)-1) |
parameter_49 = 1.3E-4; parameter_51 = 7.9E-5; parameter_50 = 2.7E-4; alfa=1.0; beta=1.0; parameter_11 = 71.7220990679741; parameter_52 = 4.0E-5; parameter_40 = 73.41 | Reaction: species_12 + species_3 => species_28 + species_4; species_12, species_3, species_28, species_4, Rate Law: compartment_1*(parameter_40*species_12*species_3/(alfa*parameter_51*parameter_52)-parameter_11*species_28*species_4/(beta*parameter_49*parameter_50))/(1+species_12/parameter_51+species_3/parameter_52+species_12*species_3/(alfa*parameter_51*parameter_52)+species_28*species_4/(beta*parameter_49*parameter_50)+species_28/parameter_49+species_4/parameter_50) |
parameter_1 = 0.0114 | Reaction: species_3 = parameter_1-species_4, Rate Law: missing |
K6PG1=1.0E-8; Kapp=141.7; KNADPH=4.5E-9; parameter_34 = 31.02; KPGA23=1.2E-7; KATP=1.54E-7; KNADP=1.8E-8; K6PG2=5.8E-8 | Reaction: species_8 + species_10 => species_13 + species_11; species_12, species_4, species_8, species_10, species_13, species_11, species_12, species_4, Rate Law: compartment_1*parameter_34/K6PG1/KNADP*(species_8*species_10-species_13*species_11/Kapp)/((1+species_10/KNADP)*(1+species_8/K6PG1+species_12/KPGA23)+species_4/KATP+species_11*(1+species_8/K6PG2)/KNADPH) |
parameter_78 = 154.0; parameter_80 = 1.9E-4; parameter_79 = 1.2E-4; parameter_22 = 58.9795390787319 | Reaction: species_28 => species_29; species_28, species_29, Rate Law: compartment_1*(parameter_78*species_28/parameter_80-parameter_22*species_29/parameter_79)/(1+species_28/parameter_80+species_29/parameter_79) |
parameter_47 = 127800.0; Keq=0.2 | Reaction: species_11 => species_10; species_11, species_10, Rate Law: compartment_1*parameter_47*species_11*(1-species_10/(species_11*Keq))/((1+species_11+1+species_10)-1) |
y=12.5; Keq=1000000.0; parameter_48 = 9801000.0 | Reaction: species_31 + species_34 + species_23 + species_3 => species_33 + species_4; species_31, species_23, species_3, species_34, species_4, species_33, Rate Law: compartment_1*parameter_48*species_31^(1/y)*species_23*species_3*species_34^(5/(2*y))*(1-species_4*species_33^(3/y)/(species_31^(1/y)*species_34^(5/(2*y))*species_23*species_3*Keq))/(((1+species_31)^(1/y)*(1+species_34)^(5/(2*y))*(1+species_23)*(1+species_3)+(1+species_4)*(1+species_33)^(3/y))-1) |
parameter_73 = 2.2E-5; parameter_10 = 135.42497838741; parameter_75 = 1.9E-4; parameter_77 = 0.029; parameter_76 = 9.0E-5; parameter_74 = 1.0E-5; parameter_39 = 109.1 | Reaction: species_16 + species_19 + species_23 => species_12 + species_18; species_19, species_16, species_23, species_12, species_18, Rate Law: compartment_1*(parameter_39*species_19*species_16*species_23/(parameter_76*parameter_75*parameter_77)-parameter_10*species_12*species_18/(parameter_73*parameter_74))/(1+species_19/parameter_76+species_19*species_16/(parameter_76*parameter_75)+species_19*species_16*species_23/(parameter_76*parameter_75*parameter_77)+species_12*species_18/(parameter_73*parameter_74)+species_18/parameter_74) |
parameter_3 = 0.001345 | Reaction: species_18 = parameter_3-species_19, Rate Law: missing |
parameter_24 = 179.83480680891; parameter_43 = 160.9; parameter_53 = 6.0E-5; parameter_54 = 3.8E-5 | Reaction: species_29 => species_30; species_29, species_30, Rate Law: compartment_1*(parameter_43*species_29/parameter_54-parameter_24*species_30/parameter_53)/(1+species_29/parameter_54+species_30/parameter_53) |
parameter_7 = 6.03725213205671E-5; KiG1P=0.0074; nH=1.75; Kamp=1.9E-12; parameter_66 = 0.015; parameter_27 = 0.00311; parameter_64 = 0.0044; parameter_63 = 0.01049; parameter_67 = 0.0046; parameter_65 = 0.0015; KPi=2.0E-4 | Reaction: species_24 + species_23 => species_24 + species_22; species_24, species_23, species_22, Rate Law: compartment_1*(parameter_63*species_24*species_23/(parameter_66*KPi)-parameter_7*species_24*species_22/(parameter_64*parameter_65))/(1+species_24/parameter_66+species_23/parameter_67+species_24/parameter_64+species_22/KiG1P+species_24*species_23/(parameter_66*KPi)+species_24*species_22/(parameter_64*parameter_65))*parameter_27^nH/Kamp/(1+parameter_27^nH/Kamp) |
States:
Name | Description |
---|---|
species 9 | [endoplasmic reticulum; glucose] |
species 27 | [glycerone phosphate(2-)] |
species 31 | [pyruvate] |
species 1 | [glucose] |
species 18 | [NADH] |
species 4 | [ATP] |
species 16 | [glyceraldehyde 3-phosphate] |
species 20 | [AMP] |
species 28 | [3-phosphoglyceric acid] |
species 34 | [singlet dioxygen] |
species 32 | [lactate] |
species 8 | [6-O-phosphono-D-glucono-1,5-lactone] |
species 30 | [phosphoenolpyruvate] |
species 12 | [683] |
species 17 | [sedoheptulose 7-phosphate] |
species 5 | [keto-D-fructose 6-phosphate] |
species 15 | [aldehydo-D-ribose 5-phosphate(2-)] |
species 21 | [7339] |
species 2 | [alpha-D-glucose 6-phosphate] |
species 29 | [3-ADP-2-phosphoglyceric acid] |
species 6 | [alpha-D-fructofuranose 1,6-bisphosphate] |
species 19 | [NAD] |
species 10 | [NADP] |
species 33 | [carbon dioxide] |
species 11 | [salicyl alcohol] |
species 24 | [glycogen] |
species 14 | [D-xylulose 5-phosphate(2-)] |
species 22 | [D-glucopyranose 1-phosphate] |
species 3 | [ADP] |
species 23 | [phosphate(3-)] |
species 7 | [D-erythrose 4-phosphate] |
species 13 | [D-ribulose 5-phosphate(2-)] |
BIOMD0000000736
— v0.0.1# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes only normal iron with…
Details
Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769
Parameters:
Name | Description |
---|---|
kInBM = 15.7690636138556 | Reaction: Fe2Tf => FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma |
kInLiver = 2.97790545667672 | Reaction: Fe1Tf => FeLiver + Tf, Rate Law: kInLiver*Fe1Tf*Plasma |
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeLiver => NTBI; Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver)*(1+Hepcidin/Ki)) |
kDuoLoss = 0.0270113302698216 | Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum |
kFe1Tf_Fe2Tf = 1.084322005E9 | Reaction: Fe1Tf + NTBI => Fe2Tf, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI |
kNTBI_Fe1Tf = 1.084322005E9 | Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf |
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeRest => NTBI; Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest)*(1+Hepcidin/Ki)) |
kBMSpleen = 0.061902954378781 | Reaction: FeBM => FeSpleen, Rate Law: kBMSpleen*FeBM*BoneMarrow |
vRBCSpleen = 0.0235286 | Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC |
vDiet = 0.00377422331938439 | Reaction: => FeDuo, Rate Law: Duodenum*vDiet |
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 | Reaction: FeDuo => NTBI; Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo)*(1+Hepcidin/Ki)) |
v=1.7393E-8 | Reaction: => Hepcidin, Rate Law: Plasma*v |
kInRest = 6.16356235352873 | Reaction: Fe1Tf => FeRest + Tf, Rate Law: kInRest*Fe1Tf*Plasma |
k1=0.75616 | Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin |
kInDuo = 0.0689984226081531 | Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma |
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeSpleen => NTBI; Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen)*(1+Hepcidin/Ki)) |
kInRBC = 1.08447580176706 | Reaction: FeBM => FeRBC, Rate Law: kInRBC*FeBM*BoneMarrow |
kRestLoss = 0.023533240736163 | Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest |
States:
Name | Description |
---|---|
FeRest | [iron cation] |
Fe2Tf | [iron(3+); Serotransferrin] |
NTBI | [iron cation] |
FeSpleen | [iron cation] |
FeBM | [iron cation] |
FeRBC | [iron cation] |
Fe1Tf | [Serotransferrin; iron(3+)] |
FeLiver | [iron cation] |
FeDuo | [iron cation] |
Tf | [Serotransferrin] |
Hepcidin | [Hepcidin] |
FeOutside | [iron cation] |
BIOMD0000000735
— v0.0.1# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes normal iron and radio…
Details
Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769
Parameters:
Name | Description |
---|---|
kInBM = 15.7690636138556 | Reaction: Fe2Tf_0 => FeBM + Tf, Rate Law: kInBM*Fe2Tf_0*Plasma |
kInLiver = 2.97790545667672 | Reaction: Fe1Tf => FeLiver + Tf, Rate Law: kInLiver*Fe1Tf*Plasma |
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeLiver_0 => NTBI_0; FeLiver, Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver_0/((Km+FeLiver_0+FeLiver)*(1+Hepcidin/Ki)) |
kDuoLoss = 0.0270113302698216 | Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum |
kFe1Tf_Fe2Tf = 1.084322005E9 | Reaction: Fe1Tf + NTBI => Fe2Tf_, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI |
kNTBI_Fe1Tf = 1.084322005E9 | Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf |
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeRest => NTBI; FeRest_0, Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest+FeRest_0)*(1+Hepcidin/Ki)) |
kBMSpleen = 0.061902954378781 | Reaction: FeBM_0 => FeSpleen, Rate Law: kBMSpleen*FeBM_0*BoneMarrow |
vRBCSpleen = 0.0235286 | Reaction: FeRBC_0 => FeSpleen_0, Rate Law: vRBCSpleen*FeRBC_0*RBC |
vDiet = 0.00377422331938439 | Reaction: => FeDuo_0, Rate Law: Duodenum*vDiet |
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 | Reaction: FeDuo => NTBI; FeDuo_0, Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo+FeDuo_0)*(1+Hepcidin/Ki)) |
v=1.7393E-8 | Reaction: => Hepcidin, Rate Law: Plasma*v |
kInRest = 6.16356235352873 | Reaction: Fe2Tf => FeRest + FeRest_0 + Tf, Rate Law: kInRest*Fe2Tf*Plasma |
k1=0.75616 | Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin |
kInDuo = 0.0689984226081531 | Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma |
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeSpleen => NTBI; FeSpleen_0, Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen+FeSpleen_0)*(1+Hepcidin/Ki)) |
kInRBC = 1.08447580176706 | Reaction: FeBM_0 => FeRBC, Rate Law: kInRBC*FeBM_0*BoneMarrow |
kRestLoss = 0.023533240736163 | Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest |
States:
Name | Description |
---|---|
FeRest | [iron cation] |
FeOutside 0 | [iron cation] |
NTBI 0 | [iron cation] |
Fe2Tf | [iron(3+); Serotransferrin] |
NTBI | [iron cation] |
Fe1Tf 0 | [iron(3+); Serotransferrin] |
Fe2Tf 0 | [Serotransferrin; iron(3+)] |
FeSpleen | [iron cation] |
FeRBC 0 | [iron cation] |
FeLiver 0 | [iron cation] |
FeBM | [iron cation] |
FeRBC | [iron cation] |
FeSpleen 0 | [iron cation] |
Fe1Tf | [Serotransferrin; iron(3+)] |
FeLiver | [iron cation] |
FeDuo | [iron cation] |
Tf | [Serotransferrin] |
Hepcidin | [Hepcidin] |
FeBM 0 | [iron cation] |
FeDuo 0 | [iron cation] |
BIOMD0000000737
— v0.0.1# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes only normal iron with…
Details
Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769
Parameters:
Name | Description |
---|---|
kInBM = 15.7690636138556 | Reaction: Fe2Tf => FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma |
kInLiver = 2.97790545667672 | Reaction: Fe2Tf => FeLiver + Tf, Rate Law: kInLiver*Fe2Tf*Plasma |
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeLiver => NTBI; Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver)*(1+Hepcidin/Ki)) |
kDuoLoss = 0.0270113302698216 | Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum |
kFe1Tf_Fe2Tf = 1.084322005E9 | Reaction: Fe1Tf + NTBI => Fe2Tf, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI |
kNTBI_Fe1Tf = 1.084322005E9 | Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf |
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeRest => NTBI; Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest)*(1+Hepcidin/Ki)) |
kBMSpleen = 0.061902954378781 | Reaction: FeBM => FeSpleen, Rate Law: kBMSpleen*FeBM*BoneMarrow |
vRBCSpleen = 0.0235286 | Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC |
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 | Reaction: FeDuo => NTBI; Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo)*(1+Hepcidin/Ki)) |
v=8.54927E-9 | Reaction: => Hepcidin, Rate Law: Plasma*v |
kInRest = 6.16356235352873 | Reaction: Fe2Tf => FeRest + Tf, Rate Law: kInRest*Fe2Tf*Plasma |
k1=0.75616 | Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin |
kInDuo = 0.0689984226081531 | Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma |
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeSpleen => NTBI; Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen)*(1+Hepcidin/Ki)) |
vDiet = 0.0 | Reaction: => FeDuo, Rate Law: Duodenum*vDiet |
kInRBC = 1.08447580176706 | Reaction: FeBM => FeRBC, Rate Law: kInRBC*FeBM*BoneMarrow |
kRestLoss = 0.023533240736163 | Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest |
States:
Name | Description |
---|---|
FeRest | [iron cation] |
Fe2Tf | [Serotransferrin; iron(3+)] |
NTBI | [iron cation] |
FeSpleen | [iron cation] |
FeBM | [iron cation] |
FeRBC | [iron cation] |
Fe1Tf | [iron(3+); Serotransferrin] |
FeLiver | [iron cation] |
FeDuo | [iron cation] |
Tf | [Serotransferrin] |
Hepcidin | [Hepcidin] |
FeOutside | [iron cation] |
BIOMD0000000734
— v0.0.1# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model attempts to fit the radioiron…
Details
Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769
Parameters:
Name | Description |
---|---|
kInBM = 15.7690636138556 | Reaction: Fe2Tf => FeBM_0 + FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma |
kInLiver = 2.97790545667672 | Reaction: Fe2Tf_ => FeLiver + Tf, Rate Law: kInLiver*Fe2Tf_*Plasma |
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeLiver => NTBI; FeLiver_0, Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver+FeLiver_0)*(1+Hepcidin/Ki)) |
kDuoLoss = 0.0270113302698216 | Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum |
kFe1Tf_Fe2Tf = 1.084322005E9 | Reaction: Fe1Tf + NTBI => Fe2Tf_, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI |
kNTBI_Fe1Tf = 1.084322005E9 | Reaction: NTBI_0 + Tf => Fe1Tf_0, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI_0*Tf |
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeRest => NTBI; FeRest_0, Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest+FeRest_0)*(1+Hepcidin/Ki)) |
kBMSpleen = 0.061902954378781 | Reaction: FeBM_0 => FeSpleen, Rate Law: kBMSpleen*FeBM_0*BoneMarrow |
vRBCSpleen = 0.0235286 | Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC |
vDiet = 0.00377422331938439 | Reaction: => FeDuo_0, Rate Law: Duodenum*vDiet |
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 | Reaction: FeDuo => NTBI; FeDuo_0, Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo+FeDuo_0)*(1+Hepcidin/Ki)) |
v=1.7393E-8 | Reaction: => Hepcidin, Rate Law: Plasma*v |
kInRest = 6.16356235352873 | Reaction: Fe2Tf => FeRest + FeRest_0 + Tf, Rate Law: kInRest*Fe2Tf*Plasma |
k1=0.75616 | Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin |
kInDuo = 0.0689984226081531 | Reaction: Fe2Tf_0 => FeDuo_0 + Tf, Rate Law: kInDuo*Fe2Tf_0*Plasma |
kInRBC = 1.08447580176706 | Reaction: FeBM => FeRBC_0, Rate Law: kInRBC*FeBM*BoneMarrow |
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeSpleen => NTBI; FeSpleen_0, Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen+FeSpleen_0)*(1+Hepcidin/Ki)) |
kRestLoss = 0.023533240736163 | Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest |
States:
Name | Description |
---|---|
FeRest | [iron cation] |
FeRBC 0 | [iron cation] |
FeRBC | [iron cation] |
FeSpleen 0 | [iron cation] |
Fe1Tf | [iron(3+); Serotransferrin] |
FeLiver | [iron cation] |
FeBM 0 | [iron cation] |
FeOutside 0 | [iron cation] |
NTBI 0 | [iron cation] |
Fe2Tf | [Serotransferrin; iron(3+)] |
NTBI | [iron cation] |
Fe1Tf 0 | [Serotransferrin; iron(3+)] |
Fe2Tf 0 | [iron(3+); Serotransferrin] |
FeSpleen | [iron cation] |
FeRest 0 | [iron cation] |
FeLiver 0 | [iron cation] |
FeBM | [iron cation] |
FeDuo | [iron cation] |
Tf | [Serotransferrin] |
Hepcidin | [Hepcidin] |
FeOutside | [iron cation] |
FeDuo 0 | [iron cation] |
BIOMD0000000738
— v0.0.1# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes only normal iron with…
Details
Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769
Parameters:
Name | Description |
---|---|
kInBM = 15.7690636138556 | Reaction: Fe2Tf => FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma |
kInLiver = 2.97790545667672 | Reaction: Fe2Tf => FeLiver + Tf, Rate Law: kInLiver*Fe2Tf*Plasma |
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeLiver => NTBI; Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver)*(1+Hepcidin/Ki)) |
kDuoLoss = 0.0270113302698216 | Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum |
kFe1Tf_Fe2Tf = 1.084322005E9 | Reaction: Fe1Tf + NTBI => Fe2Tf, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI |
kNTBI_Fe1Tf = 1.084322005E9 | Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf |
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeRest => NTBI; Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest)*(1+Hepcidin/Ki)) |
kBMSpleen = 0.061902954378781 | Reaction: FeBM => FeSpleen, Rate Law: kBMSpleen*FeBM*BoneMarrow |
vRBCSpleen = 0.0235286 | Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC |
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 | Reaction: FeDuo => NTBI; Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo)*(1+Hepcidin/Ki)) |
kInRest = 6.16356235352873 | Reaction: Fe1Tf => FeRest + Tf, Rate Law: kInRest*Fe1Tf*Plasma |
k1=0.75616 | Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin |
kInDuo = 0.0689984226081531 | Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma |
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 | Reaction: FeSpleen => NTBI; Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen)*(1+Hepcidin/Ki)) |
kInRBC = 1.08447580176706 | Reaction: FeBM => FeRBC, Rate Law: kInRBC*FeBM*BoneMarrow |
vDiet = 0.00415624 | Reaction: => FeDuo, Rate Law: Duodenum*vDiet |
v=2.30942E-8 | Reaction: => Hepcidin, Rate Law: Plasma*v |
kRestLoss = 0.023533240736163 | Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest |
States:
Name | Description |
---|---|
FeRest | [iron cation] |
Fe2Tf | [Serotransferrin; iron(3+)] |
NTBI | [iron cation] |
FeSpleen | [iron cation] |
FeBM | [iron cation] |
FeRBC | [iron cation] |
Fe1Tf | [Serotransferrin; iron(3+)] |
FeLiver | [iron cation] |
FeDuo | [iron cation] |
Tf | [Serotransferrin] |
Hepcidin | [Hepcidin] |
FeOutside | [iron cation] |
MODEL2001200002
— v0.0.1Role of vascular normalization in benefit from metronomic chemotherapy. Mpekris F1, Baish JW2, Stylianopoulos T3, Jain R…
Details
Metronomic dosing of chemotherapy-defined as frequent administration at lower doses-has been shown to be more efficacious than maximum tolerated dose treatment in preclinical studies, and is currently being tested in the clinic. Although multiple mechanisms of benefit from metronomic chemotherapy have been proposed, how these mechanisms are related to one another and which one is dominant for a given tumor-drug combination is not known. To this end, we have developed a mathematical model that incorporates various proposed mechanisms, and report here that improved function of tumor vessels is a key determinant of benefit from metronomic chemotherapy. In our analysis, we used multiple dosage schedules and incorporated interactions among cancer cells, stem-like cancer cells, immune cells, and the tumor vasculature. We found that metronomic chemotherapy induces functional normalization of tumor blood vessels, resulting in improved tumor perfusion. Improved perfusion alleviates hypoxia, which reprograms the immunosuppressive tumor microenvironment toward immunostimulation and improves drug delivery and therapeutic outcomes. Indeed, in our model, improved vessel function enhanced the delivery of oxygen and drugs, increased the number of effector immune cells, and decreased the number of regulatory T cells, which in turn killed a larger number of cancer cells, including cancer stem-like cells. Vessel function was further improved owing to decompression of intratumoral vessels as a result of increased killing of cancer cells, setting up a positive feedback loop. Our model enables evaluation of the relative importance of these mechanisms, and suggests guidelines for the optimal use of metronomic therapy. link: http://identifiers.org/pubmed/28174262
BIOMD0000000367
— v0.0.1This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…
Details
This paper focus on the quest for mechanisms that are able to create tolerance and an activation threshold in the extrinsic coagulation cascade. We propose that the interplay of coagulation inhibitor and blood flow creates threshold behavior. First we test this hypothesis in a minimal, four dimensional model. This model can be analysed by means of time scale analysis. We find indeed that only the interplay of blood flow and inhibition together are able to produce threshold behavior. The mechanism relays on a combination of raw substance supply and wash-out effect by the blood flow and a stabilization of the resting state by the inhibition. We use the insight into this minimal model to interpret the simulation results of a large model. Here, we find that the initiating steps (TF that produces together with fVII(a) factor Xa) does not exhibit threshold behavior, but the overall system does. Hence, the threshold behavior appears via the feedback loop (in that fIIa produces indirectly fXa that in turn produces fIIa again) inhibited by ATIII and blood flow. link: http://identifiers.org/pubmed/17936855
Parameters:
Name | Description |
---|---|
zeta = 0.5; b = 1.5; mu_z = 0.4 | Reaction: z = ((-b)*y*z+zeta*mu_z)-zeta*z, Rate Law: ((-b)*y*z+zeta*mu_z)-zeta*z |
zeta = 0.5; mu_x = 4.0; r = 0.2 | Reaction: x = ((-r)*x*y+zeta*mu_x)-zeta*x, Rate Law: ((-r)*x*y+zeta*mu_x)-zeta*x |
zeta = 0.5; r = 0.2; b = 1.5 | Reaction: y = (r*x*y-b*y*z)-zeta*y, Rate Law: (r*x*y-b*y*z)-zeta*y |
States:
Name | Description |
---|---|
x | x |
z | z |
y | y |
BIOMD0000000568
— v0.0.1Mueller2015 - Hepatocyte proliferation, T160 phosphorylation of CDK2This model is described in the article: [T160-phosp…
Details
Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF. link: http://identifiers.org/pubmed/25771250
Parameters:
Name | Description |
---|---|
kp_c2cak = 101.599112819407 | Reaction: S13 => S18; S13, Rate Law: Nucleus*kp_c2cak*S13/cell |
ks_e2fe2f = 0.459601740303536; ks_e2fmyc = 2.49174531457788E-6; tf = 0.635098964160441 | Reaction: => S14; S14, Rate Law: Nucleus*(ks_e2fe2f*S14+ks_e2fmyc)*tf/cell |
kd_p21c4 = 1430.78413614709 | Reaction: S19 => S10 + S12; S19, Rate Law: cell*kd_p21c4*S19/cell |
kb_p21c2 = 997.938141166465 | Reaction: S4 + S12 => S20; S4, S12, Rate Law: cell*kb_p21c2*S4*S12/cell |
ks_c2myc = 0.157511710670132; ks_c2e2f = 2.19944932286058; tf = 0.635098964160441 | Reaction: => S4; S14, S16, Rate Law: cell*(ks_c2myc*tf+ks_c2e2f*(S14+S16))/cell |
kdeg_rbbound = 0.0889964132806627 | Reaction: S16 => S14; S16, Rate Law: Nucleus*kdeg_rbbound*S16/cell |
kb_p21c4 = 14.3083360067931 | Reaction: S10 + S12 => S19; S10, S12, Rate Law: cell*kb_p21c4*S10*S12/cell |
kdeg_p21erkskp2 = 2.82976267377082E-4; erk = 0.16; kdeg_p21skp2 = 0.750574831653576; kdeg_p21c2skp2 = 0.040108041739907 | Reaction: S23 => S18; S18, S14, S23, Rate Law: Nucleus*(kdeg_p21erkskp2*erk+kdeg_p21c2skp2*S18+kdeg_p21skp2)*S14*S23/cell |
kcatdp_rbc4 = 2892.0219338341; nrb = 3.0; Km_dprb = 0.118988383643671; kinh_pp1 = 16634.9400020267 | Reaction: S15 => S1; S18, S15, Rate Law: Nucleus*kcatdp_rbc4*S15^nrb/(Km_dprb^nrb+S15^nrb)*1/(1+kinh_pp1*S18)/cell |
kdp_c2cak = 101.282119534273 | Reaction: S18 => S13; S18, Rate Law: Nucleus*kdp_c2cak*S18/cell |
kdeg_e2ffree = 0.100037217670528 | Reaction: S14 => ; S14, Rate Law: Nucleus*kdeg_e2ffree*S14/cell |
kb_rbe2f = 229.976400323907 | Reaction: S1 + S14 => S2; S1, S14, Rate Law: Nucleus*kb_rbe2f*S1*S14/cell |
kd_rbpe2f = 87735.365961809 | Reaction: S16 => S14 + S15; S16, Rate Law: Nucleus*kd_rbpe2f*S16/cell |
nrb = 3.0; Km_prb = 2.03458881189349; kcatp_rbc2 = 7142308.07232621 | Reaction: S16 => S14 + S21; S18, S16, Rate Law: Nucleus*kcatp_rbc2*S18*S16^nrb/(Km_prb^nrb+S16^nrb)/cell |
kdeg_rbfree = 0.346759895758394 | Reaction: S1 => ; S1, Rate Law: Nucleus*kdeg_rbfree*S1/cell |
gsk3b = 0.47; kdeg_c4 = 1.01433121526038; kdeg_c4gsk3b = 0.107637073030656 | Reaction: S19 => S12; S19, Rate Law: cell*(kdeg_c4+kdeg_c4gsk3b*gsk3b)*S19/cell |
kd_rbe2f = 11499.4014796088 | Reaction: S2 => S1 + S14; S2, Rate Law: Nucleus*kd_rbe2f*S2/cell |
kdeg_e2fbound = 0.0999954023364359 | Reaction: S2 => S1; S2, Rate Law: Nucleus*kdeg_e2fbound*S2/cell |
ks_rb = 72.5245257602228; ks_rbe2f = 20.0129834334888 | Reaction: => S1; S14, Rate Law: Nucleus*(ks_rb+ks_rbe2f*S14)/cell |
scale_TotCDK2T160 = 2.728395741944; Vnuc = 0.25; Vcyto = 12.67 | Reaction: ObsTotCDK2T160_obs = scale_TotCDK2T160*Vnuc*(S18+S23)/(Vnuc+Vcyto), Rate Law: missing |
erk = 0.16; gsk3b = 0.47; kdeg_p21erk = 0.736488746268804; kdeg_p21gsk3b = 0.00464010657330714 | Reaction: S12 => ; S12, Rate Law: cell*(kdeg_p21gsk3b*gsk3b+kdeg_p21erk*erk)*S12/cell |
scale_PhosRbS800 = 0.82377467648995; Vnuc = 0.25; Vcyto = 12.67 | Reaction: ObsPhosRbS800_obs = scale_PhosRbS800*Vnuc*S21/(Vnuc+Vcyto), Rate Law: missing |
ks_c4 = 14298.6715905912; tf = 0.635098964160441 | Reaction: => S10, Rate Law: cell*ks_c4*tf/cell |
scale_TotE2F = 28.7418; Vnuc = 0.25; Vcyto = 12.67; scale_TotRb = 0.2605 | Reaction: ObsTotE2F_obs = (scale_TotE2F+scale_TotRb)*Vnuc*(S2+S14+S16)/(Vnuc+Vcyto), Rate Law: missing |
kd_p21c2 = 9.98179979713068 | Reaction: S20 => S4 + S12; S20, Rate Law: cell*kd_p21c2*S20/cell |
Vratio = 0.0197316495659037; kimport = 0.0744777523096695 | Reaction: S12 => S11; S12, Rate Law: kimport/Vratio*S12/cell |
Vnuc = 0.25; Vcyto = 12.67; scale_TotRb = 0.2605 | Reaction: ObsTotRb_obs = scale_TotRb*Vnuc*(S1+S2+S15+S16+S21)/(Vnuc+Vcyto), Rate Law: missing |
Vnuc = 0.25; scale_Totp21CDK2 = 0.339790715037712; Vcyto = 12.67 | Reaction: ObsCDK2P21_obs = scale_Totp21CDK2*(Vnuc*(S3+S23)+Vcyto*S20)/(Vnuc+Vcyto), Rate Law: missing |
nrb = 3.0; kcatdp_rbc2 = 0.00313841707547858; Km_dprb = 0.118988383643671; kinh_pp1 = 16634.9400020267 | Reaction: S21 => S15; S18, S21, Rate Law: Nucleus*kcatdp_rbc2*S21^nrb/(Km_dprb^nrb+S21^nrb)*1/(1+kinh_pp1*S18)/cell |
ks_p21p53 = 3.84136205729286E-6; tfp21 = 0.635098964160441; ks_p21e2f = 0.811617200647839 | Reaction: => S12; S14, Rate Law: cell*(ks_p21p53+ks_p21e2f*S14)*tfp21/cell |
kcatp_rbc4 = 2797.82326282727; nrb = 3.0; Km_prb = 2.03458881189349 | Reaction: S1 => S15; S24, S1, Rate Law: Nucleus*kcatp_rbc4*S24*S1^nrb/(Km_prb^nrb+S1^nrb)/cell |
kb_rbpe2f = 182.218452288549 | Reaction: S14 + S15 => S16; S14, S15, Rate Law: Nucleus*kb_rbpe2f*S14*S15/cell |
k_dna = 0.00949790539669408 | Reaction: S5 => S17; S18, S14, S5, Rate Law: Nucleus*k_dna*S18*S14*S5/cell |
gsk3b = 0.47; kdeg_c2 = 0.225746618767114; kdeg_c2gsk3b = 1.55090179808215E-5 | Reaction: S3 => S11; S3, Rate Law: Nucleus*(kdeg_c2+kdeg_c2gsk3b*gsk3b)*S3/cell |
k_delay = 23.6658781343201 | Reaction: S27 => S28; S27, Rate Law: Nucleus*k_delay*S27/cell |
Vnuc = 0.25; scale_Totp21 = 0.1728; Vcyto = 12.67 | Reaction: ObsTotP21_obs = scale_Totp21*(Vnuc*(S3+S11+S23+S24)+Vcyto*(S12+S19+S20))/(Vnuc+Vcyto), Rate Law: missing |
kdeg_rbp21 = 0.863570809432207 | Reaction: S16 => S14; S11, S16, Rate Law: Nucleus*kdeg_rbp21*S11*S16/cell |
kdeg_c4 = 1.01433121526038 | Reaction: S24 => ; S24, Rate Law: Nucleus*kdeg_c4*S24/cell |
States:
Name | Description |
---|---|
S11 | [Cyclin-dependent kinase inhibitor 1B] |
S14 | [Transcription factor E2F1] |
S16 | [Transcription factor E2F1; Retinoblastoma-associated protein; phosphorylated] |
ObsDNAContent obs | [deoxyribonucleic acid] |
inhp53 | [Cellular tumor antigen p53] |
S13 | [phosphorylated; cyclin E1-CDK2 complex] |
S23 | [cyclin E1-CDK2 complex; Cyclin-dependent kinase inhibitor 1B; phosphorylated] |
inhakt | [RAC-alpha serine/threonine-protein kinase] |
inhc4d1 | [cyclin D1-CDK4 complex] |
S17 | [pre-replicative complex] |
S28 | [pre-replicative complex] |
S4 | [cyclin E1-CDK2 complex; phosphorylated] |
S1 | [Retinoblastoma-associated protein; phosphorylated] |
S25 | [pre-replicative complex] |
ObsTotE2F obs | [Transcription factor E2F1] |
inherk | [Mitogen-activated protein kinase 3] |
S19 | [cyclin D1-CDK4 complex; Cyclin-dependent kinase inhibitor 1B] |
ObsPhosRbS800 obs | [Retinoblastoma-associated protein; phosphorylated] |
S5 | [pre-replicative complex] |
S10 | [cyclin D1-CDK4 complex] |
S3 | [cyclin E1-CDK2 complex; Cyclin-dependent kinase inhibitor 1B; phosphorylated] |
S27 | [pre-replicative complex] |
ObsTotCDK2T160 obs | [Cyclin-dependent kinase 2; phosphorylated] |
S2 | [Transcription factor E2F1; Retinoblastoma-associated protein; phosphorylated] |
S26 | [cyclin D1-CDK4 complex] |
S21 | [Retinoblastoma-associated protein; phosphorylated] |
ObsTotP21 obs | [Cyclin-dependent kinase inhibitor 1B] |
ObsCDK2P21 obs | [Cyclin-dependent kinase inhibitor 1B; Cyclin-dependent kinase 2] |
S20 | [cyclin E1-CDK2 complex; Cyclin-dependent kinase inhibitor 1B; phosphorylated] |
hgf | [Hepatocyte growth factor] |
ObsTotRb obs | [Retinoblastoma-associated protein] |
S15 | [Retinoblastoma-associated protein; phosphorylated] |
S12 | [Cyclin-dependent kinase inhibitor 1B] |
S24 | [cyclin D1-CDK4 complex; Cyclin-dependent kinase inhibitor 1B] |
S22 | [pre-replicative complex] |
MODEL2007020001
— v0.0.1A mechanistically detailed model of the cell cycle control network of Saccharomyces cerevisiae.
Details
Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models—and eventually whole-cell models—of human cells. link: https://doi.org/10.1038/s41467-019-08903-w
BIOMD0000000642
— v0.0.1Mufudza2012 - Estrogen effect on the dynamics of breast cancerThis deterministic model shows the dynamics of breast canc…
Details
Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer. link: http://identifiers.org/pubmed/23365616
Parameters:
Name | Description |
---|---|
sigma3 = 0.3; s = 0.4; mu = 0.29; rho = 0.2; gamma3 = 0.085; omega = 0.3; v = 0.4 | Reaction: I = (((s+rho*I*T/(omega+T))-gamma3*I*T)-mu*I)-sigma3*I*E/(v+E), Rate Law: (((s+rho*I*T/(omega+T))-gamma3*I*T)-mu*I)-sigma3*I*E/(v+E) |
beta1 = 0.3; alpha1 = 0.7; sigma1 = 1.2; delta1 = 1.0 | Reaction: H = H*((alpha1-beta1*H)-delta1*T)-sigma1*H*E, Rate Law: H*((alpha1-beta1*H)-delta1*T)-sigma1*H*E |
beta2 = 0.4; alpha3 = 1.0; gamma2 = 0.9; sigma2 = 0.94 | Reaction: T = (T*(alpha3-beta2*T)-gamma2*I*T)+sigma2*H*E, Rate Law: (T*(alpha3-beta2*T)-gamma2*I*T)+sigma2*H*E |
States:
Name | Description |
---|---|
I | [immune response; cell] |
T | [neoplastic cell] |
H | [cell] |
MODEL1805230001
— v0.0.1Mathematical model for HIV, malaria and HIV-malaria co-infection.
Details
A deterministic model for the co-interaction of HIV and malaria in a community is presented and rigorously analyzed. Two sub-models, namely the HIV-only and malaria-only sub-models, are considered first of all. Unlike the HIV-only sub-model, which has a globally-asymptotically stable disease-free equilibrium whenever the associated reproduction number is less than unity, the malaria-only sub-model undergoes the phenomenon of backward bifurcation, where a stable disease-free equilibrium co-exists with a stable endemic equilibrium, for a certain range of the associated reproduction number less than unity. Thus, for malaria, the classical requirement of having the associated reproduction number to be less than unity, although necessary, is not sufficient for its elimination. It is also shown, using centre manifold theory, that the full HIV-malaria co-infection model undergoes backward bifurcation. Simulations of the full HIV-malaria model show that the two diseases co-exist whenever their reproduction numbers exceed unity (with no competitive exclusion occurring). Further, the reduction in sexual activity of individuals with malaria symptoms decreases the number of new cases of HIV and the mixed HIV-malaria infection while increasing the number of malaria cases. Finally, these simulations show that the HIV-induced increase in susceptibility to malaria infection has marginal effect on the new cases of HIV and malaria but increases the number of new cases of the dual HIV-malaria infection. link: http://identifiers.org/pubmed/19364156
BIOMD0000000978
— v0.0.1The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern ti…
Details
The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. Africa is still early in this epidemic, therefore the extent of disease severity is not yet clear. We used a mathematical model to fit to the observed cases of COVID-19 in South Africa to estimate the basic reproductive number and critical vaccination coverage to control the disease for different hypothetical vaccine efficacy scenarios. We also estimated the percentage reduction in effective contacts due to the social distancing measures implemented. Early model estimates show that COVID-19 outbreak in South Africa had a basic reproductive number of 2.95 (95% credible interval [CrI] 2.83-3.33). A vaccine with 70% efficacy had the capacity to contain COVID-19 outbreak but at very higher vaccination coverage 94.44% (95% Crl 92.44-99.92%) with a vaccine of 100% efficacy requiring 66.10% (95% Crl 64.72-69.95%) coverage. Social distancing measures put in place have so far reduced the number of social contacts by 80.31% (95% Crl 79.76-80.85%). These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. Therefore, the current social distancing measures to reduce contacts will remain key in controlling the infection in the absence of vaccines and other therapeutics. link: http://identifiers.org/pubmed/32706790
MODEL1604100000
— v0.0.1Mukhopadhyay2013 - T cell receptor proximal signaling reveals emergent ultrasensitivityThis model is described in the ar…
Details
Receptor phosphorylation is thought to be tightly regulated because phosphorylated receptors initiate signaling cascades leading to cellular activation. The T cell antigen receptor (TCR) on the surface of T cells is phosphorylated by the kinase Lck and dephosphorylated by the phosphatase CD45 on multiple immunoreceptor tyrosine-based activation motifs (ITAMs). Intriguingly, Lck sequentially phosphorylates ITAMs and ZAP-70, a cytosolic kinase, binds to phosphorylated ITAMs with differential affinities. The purpose of multiple ITAMs, their sequential phosphorylation, and the differential ZAP-70 affinities are unknown. Here, we use a systems model to show that this signaling architecture produces emergent ultrasensitivity resulting in switch-like responses at the scale of individual TCRs. Importantly, this switch-like response is an emergent property, so that removal of multiple ITAMs, sequential phosphorylation, or differential affinities abolishes the switch. We propose that highly regulated TCR phosphorylation is achieved by an emergent switch-like response and use the systems model to design novel chimeric antigen receptors for therapy. link: http://identifiers.org/pubmed/23555234
MODEL2003160003
— v0.0.1A campaign for malaria control, using Long Lasting Insecticide Nets (LLINs) was launched in South Sudan in 2009. The suc…
Details
A campaign for malaria control, using Long Lasting Insecticide Nets (LLINs) was launched in South Sudan in 2009. The success of such a campaign often depends upon adequate available resources and reliable surveillance data which help officials understand existing infections. An optimal allocation of resources for malaria control at a sub-national scale is therefore paramount to the success of efforts to reduce malaria prevalence. In this paper, we extend an existing SIR mathematical model to capture the effect of LLINs on malaria transmission. Available data on malaria is utilized to determine realistic parameter values of this model using a Bayesian approach via Markov Chain Monte Carlo (MCMC) methods. Then, we explore the parasite prevalence on a continued rollout of LLINs in three different settings in order to create a sub-national projection of malaria. Further, we calculate the model's basic reproductive number and study its sensitivity to LLINs' coverage and its efficacy. From the numerical simulation results, we notice a basic reproduction number, [Formula: see text], confirming a substantial increase of incidence cases if no form of intervention takes place in the community. This work indicates that an effective use of LLINs may reduce [Formula: see text] and hence malaria transmission. We hope that this study will provide a basis for recommending a scaling-up of the entry point of LLINs' distribution that targets households in areas at risk of malaria. link: http://identifiers.org/pubmed/29879166
BIOMD0000000664
— v0.0.1Muller2008 - Simplified MAPK activation Dynamics (Model B)Simplified mathematical model (model B) for predicting MAPK si…
Details
Activation of the fibroblast growth factor (FGFR) and melanocyte stimulating hormone (MC1R) receptors stimulates B-Raf and C-Raf isoforms that regulate the dynamics of MAPK1,2 signaling. Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. Further reducing a 106-node ordinary differential equations network encompassing the complete network to a 6-node network encompassing rate-limiting processes sustains the feedback loops and the bifan, providing sufficient information to predict biological responses. link: http://identifiers.org/pubmed/18171696
Parameters:
Name | Description |
---|---|
E = 10.0; f13 = 0.6 0.06*ml/(mol*s) | Reaction: => C_Raf; C_Raf_inactive, FGFR, Rate Law: Compartment*f13*((E-C_Raf)-C_Raf_inactive)*FGFR |
E = 10.0; f53 = 1.5 0.06*ml/(mol*s) | Reaction: => C_Raf; C_Raf_inactive, MAPK, Rate Law: Compartment*f53*((E-C_Raf)-C_Raf_inactive)*MAPK |
f14 = 0.1 1/(59.9999*s) | Reaction: => B_Raf; FGFR, Rate Law: Compartment*f14*FGFR |
g1 = 0.0 | Reaction: g1_0 = g1, Rate Law: missing |
f24 = 0.8 1/(59.9999*s) | Reaction: => B_Raf; MSH, Rate Law: Compartment*f24*MSH |
b2 = 10.0; a2 = 10.0 0.06*mmol/(l*s); g2 = 1.0 | Reaction: => MSH; g2_0, Rate Law: Compartment*a2*g2/(b2+g2) |
f45 = 0.1 1/(59.9999*s) | Reaction: => MAPK; B_Raf, Rate Law: Compartment*f45*B_Raf |
d3 = 1.0 1/(59.9999*s) | Reaction: C_Raf =>, Rate Law: Compartment*d3*C_Raf |
d6 = 0.001 1/(59.9999*s) | Reaction: C_Raf_inactive =>, Rate Law: Compartment*d6*C_Raf_inactive |
f35 = 0.3 1/(59.9999*s) | Reaction: => MAPK; C_Raf, Rate Law: Compartment*f35*C_Raf |
d5 = 1.0 1/(59.9999*s) | Reaction: MAPK =>, Rate Law: Compartment*d5*MAPK |
h36_y3 = 0.1 0.06*ml/(mol*s) | Reaction: C_Raf => C_Raf_inactive; MSH, Rate Law: Compartment*h36_y3*MSH*C_Raf |
d1 = 0.2 1/(59.9999*s) | Reaction: FGFR =>, Rate Law: Compartment*d1*FGFR |
b1 = 10.0; a1 = 10.0 0.06*mmol/(l*s); g1 = 0.0 | Reaction: => FGFR; g1_0, Rate Law: Compartment*a1*g1/(b1+g1) |
d2 = 0.1 1/(59.9999*s) | Reaction: MSH =>, Rate Law: Compartment*d2*MSH |
g2 = 1.0 | Reaction: g2_0 = g2, Rate Law: missing |
d4 = 1.1 1/(59.9999*s) | Reaction: B_Raf =>, Rate Law: Compartment*d4*B_Raf |
States:
Name | Description |
---|---|
FGFR | [Fibroblast growth factor receptor 1] |
C Raf | [RAF proto-oncogene serine/threonine-protein kinase] |
C Raf inactive | [RAF proto-oncogene serine/threonine-protein kinase] |
B Raf | [Serine/threonine-protein kinase B-raf] |
MAPK | [Mitogen-activated protein kinase 1] |
g2 0 | [Melanocyte-stimulating hormone; Stimulus] |
MSH | [melanocyte-stimulating hormone receptor] |
g1 0 | [Fibroblast growth factor 1; Stimulus] |
MODEL5950552398
— v0.0.1This model is described and analysed in a series of three articles: **Model of 2,3-bisphosphoglycerate metabolism in t…
Details
This is the third of three papers [see also Mulquiney, Bubb and Kuchel (1999) Biochem. J. 342, 565-578; Mulquiney and Kuchel (1999) Biochem. J. 342, 579-594] for which the general goal was to explain the regulation and control of 2,3-bisphosphoglycerate (2,3-BPG) metabolism in human erythrocytes. 2,3-BPG is a major modulator of haemoglobin oxygen affinity and hence is vital in blood oxygen transport. A detailed mathematical model of erythrocyte metabolism was presented in the first two papers. The model was refined through an iterative loop of experiment and simulation and it was used to predict outcomes that are consistent with the metabolic behaviour of the erythrocyte under a wide variety of experimental and physiological conditions. For the present paper, the model was examined using computer simulation and Metabolic Control Analysis. The analysis yielded several new insights into the regulation and control of 2,3-BPG metabolism. Specifically it was found that: (1) the feedback inhibition of hexokinase and phosphofructokinase by 2, 3-BPG are equally as important as the product inhibition of 2,3-BPG synthase in controlling the normal in vivo steady-state concentration of 2,3-BPG; (2) H(+) and oxygen are effective regulators of 2,3-BPG concentration and that increases in 2,3-BPG concentrations are achieved with only small changes in glycolytic rate; (3) these two effectors exert most of their influence through hexokinase and phosphofructokinase; (4) flux through the 2,3-BPG shunt changes in absolute terms in response to different energy demands placed on the cell. This response of the 2,3-BPG shunt contributes an [ATP]-stabilizing effect. A 'cost' of this is that 2, 3-BPG concentrations are very sensitive to the energy demand of the cell and; (5) the flux through the 2,3-BPG shunt does not change in response to different non-glycolytic demands for NADH. link: http://identifiers.org/pubmed/10477270
MODEL1008060000
— v0.0.1Munz2009 - Zombie Impulsive KillingThis is the basic SZR model with impulsive killing described in the article. This mo…
Details
Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection
MODEL1009230000
— v0.0.1Munz2009 - Zombie basic SZRThis is the basic SZR model for zombie infection. It is based on a classic mathematical mode…
Details
Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection
BIOMD0000000882
— v0.0.1Munz2009 - Zombie SIZRC This is the model with an latent infection and cure for zombies described in the article. This…
Details
Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection
Parameters:
Name | Description |
---|---|
delta = 1.0E-4; alpha = 0.005 | Reaction: => Removal; Susceptible, Zombie, Rate Law: compartment*(alpha*Susceptible*Zombie+delta*Susceptible) |
zeta = 1.0E-4; beta = 0.0095 | Reaction: => Zombie; Susceptible, Removal, Rate Law: compartment*(beta*Susceptible*Zombie+zeta*Removal) |
p = 0.05 | Reaction: => Susceptible, Rate Law: compartment*p |
delta = 1.0E-4; beta = 0.0095 | Reaction: Susceptible => ; Zombie, Rate Law: compartment*(beta*Susceptible*Zombie+delta*Susceptible) |
alpha = 0.005 | Reaction: Zombie => ; Susceptible, Rate Law: compartment*alpha*Susceptible*Zombie |
zeta = 1.0E-4 | Reaction: Removal => ; Susceptible, Zombie, Rate Law: compartment*zeta*Removal |
States:
Name | Description |
---|---|
Removal | [C64914] |
Zombie | Zombie |
Susceptible | [Susceptibility] |
MODEL1008060002
— v0.0.1Munz2009 - Zombie SIZRQThis is the model with latent infection and quarantine described in the article. This model was…
Details
Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection
BIOMD0000000416
— v0.0.1This model is from the article: The influence of cytokinin-auxin cross-regulation on cell-fate determination in Arab…
Details
Root growth and development in Arabidopsis thaliana are sustained by a specialised zone termed the meristem, which contains a population of dividing and differentiating cells that are functionally analogous to a stem cell niche in animals. The hormones auxin and cytokinin control meristem size antagonistically. Local accumulation of auxin promotes cell division and the initiation of a lateral root primordium. By contrast, high cytokinin concentrations disrupt the regular pattern of divisions that characterises lateral root development, and promote differentiation. The way in which the hormones interact is controlled by a genetic regulatory network. In this paper, we propose a deterministic mathematical model to describe this network and present model simulations that reproduce the experimentally observed effects of cytokinin on the expression of auxin regulated genes. We show how auxin response genes and auxin efflux transporters may be affected by the presence of cytokinin. We also analyse and compare the responses of the hormones auxin and cytokinin to changes in their supply with the responses obtained by genetic mutations of SHY2, which encodes a protein that plays a key role in balancing cytokinin and auxin regulation of meristem size. We show that although shy2 mutations can qualitatively reproduce the effect of varying auxin and cytokinin supply on their response genes, some elements of the network respond differently to changes in hormonal supply and to genetic mutations, implying a different, general response of the network. We conclude that an analysis based on the ratio between these two hormones may be misleading and that a mathematical model can serve as a useful tool for stimulate further experimental work by predicting the response of the network to changes in hormone levels and to other genetic mutations. link: http://identifiers.org/pubmed/21640126
Parameters:
Name | Description |
---|---|
psiARF = 0.1; psiARFIAA = 0.1; thetaARF = 0.1; thetaARF2 = 0.01; thARFIAA = 0.1; thARRBph = 0.1 | Reaction: F1 = ARF/thetaARF/(1+ARF/thetaARF+ARF2/thetaARF2+ARFIAA/thARFIAA+ARF*IAAp/psiARFIAA+ARF^2/psiARF+ARRBph/thARRBph), Rate Law: missing |
muAux = 0.1; ka = 100.0; eps = 0.01; alphaAux = 1.0; kd = 1.0; etaAuxTIR1 = 10.0 | Reaction: => Aux; TIR1, AuxTIR1, Rate Law: muAux*(alphaAux-Aux)-1/eps*etaAuxTIR1*(ka*Aux*TIR1-kd*AuxTIR1) |
thARRAph = 0.1; thARRBph = 0.1 | Reaction: F4 = ARRBph/thARRBph/(1+ARRAph/thARRAph+ARRBph/thARRBph), Rate Law: missing |
qa = 1.0; qd = 1.0 | Reaction: => ARF2; ARF, Rate Law: qa*ARF^2-qd*ARF2 |
eps = 0.01; deltaPINp = 1.0 | Reaction: => PINp; PINm, Rate Law: 1/eps*(deltaPINp*PINm-PINp) |
alphaAHK = 1.0; etaAHKph = 1.0 | Reaction: CkAHK = alphaAHK-etaAHKph*(AHKph+CkAHKph), Rate Law: missing |
lambda1 = 0.1; phiARp = 2.0 | Reaction: => ARm; F5a, F5b, Rate Law: phiARp*(lambda1*F5a+F5b)-ARm |
ud = 1.0; eps = 0.01; ua = 1.0 | Reaction: => ARRBph; CkAHKph, CkAHK, ARRBp, Rate Law: 1/eps*(ua*CkAHKph*ARRBp-ud*CkAHK*ARRBph) |
phiCRp = 2.0 | Reaction: => CRm; F4, Rate Law: phiCRp*F4-CRm |
alphaPH = 1.0 | Reaction: CkAHKph = ((alphaPH-AHKph)-ARRAph)-ARRBph, Rate Law: missing |
deltaARRAp = 1.0; eps = 0.01; sa = 1.0; etaAHKph = 1.0; sd = 1.0 | Reaction: => ARRAp; ARRAm, CkAHK, ARRAph, CkAHKph, Rate Law: 1/eps*((deltaARRAp*ARRAm-ARRAp)+etaAHKph*(sd*CkAHK*ARRAph-sa*CkAHKph*ARRAp)) |
alphaTIR1 = 1.0 | Reaction: TIR1 = (alphaTIR1-AuxTIR1)-AuxTIAA, Rate Law: missing |
ka = 100.0; eps = 0.01; la = 0.5; kd = 1.0; ld = 0.1 | Reaction: => AuxTIR1; Aux, TIR1, AuxTIAA, IAAp, Rate Law: 1/eps*(((ka*Aux*TIR1-kd*AuxTIR1)+(ld+1)*AuxTIAA)-la*AuxTIR1*IAAp) |
lambda1 = 0.1; phiIAAp = 100.0; lambda3 = 0.02 | Reaction: => IAAm; F1, F2, F3, Rate Law: phiIAAp*(lambda1*F1+F2+lambda3*F3)-IAAm |
eps = 0.01; sa = 1.0; sd = 1.0 | Reaction: => ARRAph; CkAHKph, ARRAp, CkAHK, ARRAph, Rate Law: 1/eps*(sa*CkAHKph*ARRAp-sd*CkAHK*ARRAph) |
eps = 0.01; la = 0.5; ld = 0.1 | Reaction: => AuxTIAA; AuxTIAA, IAAp, AuxTIR1, Rate Law: 1/eps*(la*IAAp*AuxTIR1-(ld+1)*AuxTIAA) |
phiARRAp = 100.0 | Reaction: => ARRAm; F6, Rate Law: phiARRAp*F6-ARRAm |
eps = 0.01; etaCkPh = 1.0; ra = 1.0; rd = 1.0; muCk = 0.1; alphaCk = 1.0 | Reaction: => Ck; AHKph, CkAHKph, Rate Law: muCk*(alphaCk-Ck)-etaCkPh/eps*(ra*AHKph*Ck-rd*CkAHKph) |
eps = 0.01; ra = 1.0; rd = 1.0 | Reaction: => AHKph; CkAHKph, Ck, Rate Law: 1/eps*(rd*CkAHKph-ra*AHKph*Ck) |
alphaARF = 1.0 | Reaction: ARF = (alphaARF-2*ARF2)-ARFIAA, Rate Law: missing |
psiARF = 0.1; psiARFIAA = 0.1; thetaARF = 0.1; thetaARF2 = 0.01; thARFIAA = 0.1 | Reaction: F5a = ARF/thetaARF/(1+ARF/thetaARF+ARF2/thetaARF2+ARFIAA/thARFIAA+ARF*IAAp/psiARFIAA+ARF^2/psiARF), Rate Law: missing |
thetaARp = 0.1 | Reaction: F6 = ARp/thetaARp/(1+ARp/thetaARp), Rate Law: missing |
eps = 0.01; etaARFIAA = 1.0; la = 0.5; pa = 10.0; ld = 0.1; deltaIAAp = 1.0; pd = 10.0 | Reaction: => IAAp; IAAm, AuxTIR1, AuxTIAA, ARFIAA, ARF, Rate Law: 1/eps*((deltaIAAp*IAAm-la*IAAp*AuxTIR1)+ld*AuxTIAA)+etaARFIAA*(pd*ARFIAA-pa*IAAp*ARF) |
eps = 0.01; deltaCRp = 1.0 | Reaction: => CRp; CRm, Rate Law: 1/eps*(deltaCRp*CRm-CRp) |
eps = 0.01; muIAAs = 1.0 | Reaction: => IAAs; AuxTIAA, Rate Law: 1/eps*(AuxTIAA-muIAAs*IAAs) |
pa = 10.0; pd = 10.0 | Reaction: => ARFIAA; ARF, IAAp, Rate Law: pa*ARF*IAAp-pd*ARFIAA |
eps = 0.01; deltaARp = 1.0 | Reaction: => ARp; ARm, Rate Law: 1/eps*(deltaARp*ARm-ARp) |
lambda1 = 0.1; phiPINp = 100.0 | Reaction: => PINm; F5a, F5b, Rate Law: phiPINp*(lambda1*F5a+F5b)-PINm |
alphaARRB = 2.0; etaAHKph = 1.0 | Reaction: ARRBp = alphaARRB-etaAHKph*ARRBph, Rate Law: missing |
States:
Name | Description |
---|---|
AHKph | [Histidine kinase 4; Phosphoprotein] |
F3 | F3 |
F5b | F5b |
CkAHK | [cytokinin; Histidine kinase 4] |
IAAs | [Auxin-responsive protein IAA1] |
ARF2 | [Auxin response factor 2] |
AuxTIAA | [auxin; Protein TRANSPORT INHIBITOR RESPONSE 1; Auxin-responsive protein IAA1] |
ARRBph | [Two-component response regulator ARR1; Phosphoprotein] |
ARFIAA | [Auxin response factor 2; Auxin-responsive protein IAA1] |
F6 | F6 |
AuxTIR1 | [auxin; Protein TRANSPORT INHIBITOR RESPONSE 1] |
F1 | F1 |
ARRAph | [Two-component response regulator ARR2; Phosphoprotein] |
ARRAp | [Two-component response regulator ARR1] |
ARm | [Protein AUXIN RESPONSE 4] |
PINm | [Peptidyl-prolyl cis-trans isomerase Pin1] |
ARRAm | [Two-component response regulator ARR1] |
Aux | [auxin] |
CRm | [Ethylene-responsive transcription factor CRF1] |
CkAHKph | [cytokinin; Histidine kinase 4] |
PINp | [Peptidyl-prolyl cis-trans isomerase Pin1] |
F4 | F4 |
ARF | [Auxin response factor 2] |
TIR1 | [Protein TRANSPORT INHIBITOR RESPONSE 1] |
ARp | [Protein AUXIN RESPONSE 4] |
CRp | [Ethylene-responsive transcription factor CRF1] |
IAAm | [Auxin-responsive protein IAA1; messenger RNA] |
ARRBp | [Two-component response regulator ARR1] |
F2 | F2 |
F5a | F5a |
IAAp | [Auxin-responsive protein IAA1] |
Ck | [cytokinin] |
BIOMD0000000522
— v0.0.1Muraro2014 - Vascular patterning in Arabidopsis rootsUsing a multicellular model, maintanence of vascular patterning in…
Details
As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot. link: http://identifiers.org/pubmed/24381155
Parameters:
Name | Description |
---|---|
lambda_IAA2 = 10.0; mu_m_IAA2 = 10.0; F_IAA2 = 0.0 | Reaction: IAA2m = lambda_IAA2*F_IAA2-mu_m_IAA2*IAA2m, Rate Law: lambda_IAA2*F_IAA2-mu_m_IAA2*IAA2m |
delta_AHP6 = 1.0; mu_p_AHP6 = 1.0 | Reaction: AHP6p = delta_AHP6*AHP6m-mu_p_AHP6*AHP6p, Rate Law: delta_AHP6*AHP6m-mu_p_AHP6*AHP6p |
mu_p_PHB = 1.0; delta_PHB = 1.0 | Reaction: PHBp = delta_PHB*PHBm-mu_p_PHB*PHBp, Rate Law: delta_PHB*PHBm-mu_p_PHB*PHBp |
F_CK = 0.0; p_ck = 2.0; d_ck = 10.0; phloem_rate_ck = 1.0 | Reaction: Cytokinin = phloem_rate_ck*p_ck*F_CK-d_ck*Cytokinin, Rate Law: phloem_rate_ck*p_ck*F_CK-d_ck*Cytokinin |
lambda_ARR5 = 20.0; mu_m_ARR5 = 10.0; F_ARR5 = 0.0 | Reaction: ARR5m = lambda_ARR5*F_ARR5-mu_m_ARR5*ARR5m, Rate Law: lambda_ARR5*F_ARR5-mu_m_ARR5*ARR5m |
mu_m_PIN1 = 0.0; lambda_PIN1 = 0.0; F_PIN1 = 0.0 | Reaction: PIN1m = lambda_PIN1*F_PIN1-mu_m_PIN1*PIN1m, Rate Law: lambda_PIN1*F_PIN1-mu_m_PIN1*PIN1m |
mu_m_AHP6 = 1.0; lambda_AHP6 = 2.0; F_AHP6 = 0.0 | Reaction: AHP6m = lambda_AHP6*F_AHP6-mu_m_AHP6*AHP6m, Rate Law: lambda_AHP6*F_AHP6-mu_m_AHP6*AHP6m |
phloem_rate_ax = 1.0; p_ax = 0.06; d_ax = 1.0 | Reaction: Auxin = phloem_rate_ax*p_ax-d_ax*Auxin, Rate Law: phloem_rate_ax*p_ax-d_ax*Auxin |
lambda_PIN3 = 0.0; F_PIN3 = 0.0; mu_m_PIN3 = 0.0 | Reaction: PIN3m = lambda_PIN3*F_PIN3-mu_m_PIN3*PIN3m, Rate Law: lambda_PIN3*F_PIN3-mu_m_PIN3*PIN3m |
delta_CKX3 = 1.0; mu_p_CKX3 = 1.0 | Reaction: CKX3p = delta_CKX3*CKX3m-mu_p_CKX3*CKX3p, Rate Law: delta_CKX3*CKX3m-mu_p_CKX3*CKX3p |
mu_p_ARR5 = 10.0; delta_ARR5 = 10.0 | Reaction: ARR5p = delta_ARR5*ARR5m-mu_p_ARR5*ARR5p, Rate Law: delta_ARR5*ARR5m-mu_p_ARR5*ARR5p |
d_phb = 1.0; d_mirna_mrna = 10.0; p_phb = 2.0 | Reaction: PHBm = (p_phb-d_phb*PHBm)-d_mirna_mrna*PHBm*miRNA, Rate Law: (p_phb-d_phb*PHBm)-d_mirna_mrna*PHBm*miRNA |
mu_p_IAA2 = 10.0; delta_IAA2 = 10.0 | Reaction: IAA2p = delta_IAA2*IAA2m-mu_p_IAA2*IAA2p, Rate Law: delta_IAA2*IAA2m-mu_p_IAA2*IAA2p |
mu_m_PIN7 = 1.0; lambda_PIN7 = 1.0; F_PIN7 = 0.0 | Reaction: PIN7m = lambda_PIN7*F_PIN7-mu_m_PIN7*PIN7m, Rate Law: lambda_PIN7*F_PIN7-mu_m_PIN7*PIN7m |
States:
Name | Description |
---|---|
AHP6m | [Pseudo histidine-containing phosphotransfer protein 6] |
Cytokinin | [cytokinin] |
IAA2p | [Auxin-responsive protein IAA2] |
PIN3m | [Auxin efflux carrier component 3] |
ARR5p | [Two-component response regulator ARR5] |
PHBp | [Homeobox-leucine zipper protein ATHB-14] |
ARR5m | [Two-component response regulator ARR5] |
Auxin | [Auxin transporter protein 1] |
miRNA | [SBO:0000316] |
PHBm | [Homeobox-leucine zipper protein ATHB-14] |
IAA2m | [Auxin-responsive protein IAA2] |
PIN1m | [Peptidyl-prolyl cis-trans isomerase Pin1] |
CKX3p | [Cytokinin dehydrogenase 3] |
AHP6p | [Pseudo histidine-containing phosphotransfer protein 6] |
PIN7m | [Auxin efflux carrier component 7] |
BIOMD0000000671
— v0.0.1Murphy2016 - Differences in predictions of ODE models of tumor growthComparison of 7 ODE models for tumour size. This mo…
Details
While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning. link: http://identifiers.org/pubmed/26921070
Parameters:
Name | Description |
---|---|
a_exp = 0.0246 | Reaction: V_exp = a_exp*V_exp, Rate Law: a_exp*V_exp |
a_surf = 0.291; b_surf = 708.0 | Reaction: V_surf = a_surf*V_surf/(V_surf+b_surf)^(1/3), Rate Law: a_surf*V_surf/(V_surf+b_surf)^(1/3) |
a_mend = 0.105; b_mend = 0.785 | Reaction: V_mend = a_mend*V_mend^b_mend, Rate Law: a_mend*V_mend^b_mend |
a_bert = 0.2344; b_bert = 3.46E-19 | Reaction: V_bert = a_bert*V_bert^(2/3)-b_bert*V_bert, Rate Law: a_bert*V_bert^(2/3)-b_bert*V_bert |
c_gomp = 10700.0; a_gomp = 0.0919; b_gomp = 15500.0 | Reaction: V_gomp = a_gomp*V_gomp*ln(b_gomp/(V_gomp+c_gomp)), Rate Law: a_gomp*V_gomp*ln(b_gomp/(V_gomp+c_gomp)) |
b_lin = 4300.0; a_lin = 132.0 | Reaction: V_lin = a_lin*V_lin/(V_lin+b_lin), Rate Law: a_lin*V_lin/(V_lin+b_lin) |
b_log = 6920.0; a_log = 0.0295 | Reaction: V_log = a_log*V_log*(1-V_log/b_log), Rate Law: a_log*V_log*(1-V_log/b_log) |
States:
Name | Description |
---|---|
V gomp | V_gomp |
V surf | V_surf |
V lin | V_lin |
V mend | V_mend |
V log | V_log |
V bert | V_bert |
V exp | [Exponential Function] |
BIOMD0000000643
— v0.0.1Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitionsThis model is described in the artic…
Details
ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition. link: http://identifiers.org/doi/10.7554/eLife.24998
Parameters:
Name | Description |
---|---|
ModelValue_1 = 2.0; kmpp2a = 0.0161515151515152 | Reaction: BB = A46+ModelValue_1+kmpp2a, Rate Law: missing |
ModelValue_12 = 0.5; ModelValue_14 = 1.0; ModelValue_23 = 2.36; ModelValue_10 = 0.935; ModelValue_11 = 1.6; ModelValue_0 = 10.0; ModelValue_13 = 5.0 | Reaction: A88 = ModelValue_10*PKA*(ModelValue_0-A88)/((ModelValue_11+ModelValue_23*A46/ModelValue_0+ModelValue_0)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88), Rate Law: ModelValue_10*PKA*(ModelValue_0-A88)/((ModelValue_11+ModelValue_23*A46/ModelValue_0+ModelValue_0)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88) |
ModelValue_1 = 2.0 | Reaction: Complex = (BB-(BB^2-4*A46*ModelValue_1)^(0.5))/2, Rate Law: missing |
ModelValue_2 = 2.7; ModelValue_15 = 0.01865; kppx = 0.05 | Reaction: M = kppx*(ModelValue_2-M)-ModelValue_15*A88*M, Rate Law: kppx*(ModelValue_2-M)-ModelValue_15*A88*M |
ModelValue_22 = 0.37526; ModelValue_9 = 0.09; ModelValue_6 = 0.05; ModelValue_0 = 10.0; ModelValue_8 = 0.0988 | Reaction: A46 = ModelValue_8*M*(ModelValue_0-A46)/(ModelValue_9+ModelValue_22*A88/ModelValue_0+(ModelValue_0-A46))-ModelValue_6*Complex, Rate Law: ModelValue_8*M*(ModelValue_0-A46)/(ModelValue_9+ModelValue_22*A88/ModelValue_0+(ModelValue_0-A46))-ModelValue_6*Complex |
ModelValue_3 = 12.0; ModelValue_17 = 2.0; ModelValue_16 = 0.7; ModelValue_20 = 0.0; ModelValue_19 = 0.02335; ModelValue_18 = 10.0 | Reaction: PKA = ModelValue_16*(ModelValue_3-PKA)*ModelValue_20^ModelValue_17/(ModelValue_18^ModelValue_17+ModelValue_20^ModelValue_17)-ModelValue_19*A46*PKA, Rate Law: ModelValue_16*(ModelValue_3-PKA)*ModelValue_20^ModelValue_17/(ModelValue_18^ModelValue_17+ModelValue_20^ModelValue_17)-ModelValue_19*A46*PKA |
States:
Name | Description |
---|---|
BB | [urn:miriam:pr:PR%3AP56212-2] |
A88 | [urn:miriam:pr:PR_P56212-2] |
M | [Microtubule-associated serine/threonine-protein kinase 3] |
Complex | [protein phosphatase type 2A complex] |
PKA | [cAMP-dependent protein kinase catalytic subunit alpha] |
A46 | [urn:miriam:pr:PR_P56212-2] |
BIOMD0000000644
— v0.0.1Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3This model is…
Details
ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition. link: http://identifiers.org/doi/10.7554/eLife.24998
Parameters:
Name | Description |
---|---|
ModelValue_3 = 12.0; ModelValue_18 = 0.7; ModelValue_20 = 10.0; ModelValue_22 = 0.0; ModelValue_19 = 2.0; ModelValue_21 = 0.02335 | Reaction: PKA = ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA, Rate Law: ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA |
ModelValue_1 = 2.0; kmpp2a = 0.0161515151515152 | Reaction: BB = A46+ModelValue_1+kmpp2a, Rate Law: missing |
ModelValue_1 = 2.0 | Reaction: Complex = (BB-(BB^2-4*A46*ModelValue_1)^(0.5))/2, Rate Law: missing |
ModelValue_12 = 0.5; ModelValue_14 = 1.0; ARPPtot = 10.0; ModelValue_28 = 2.36; ModelValue_10 = 0.935; ModelValue_11 = 1.6; ModelValue_0 = 10.0; ModelValue_13 = 5.0 | Reaction: A88 = ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88), Rate Law: ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88) |
ModelValue_2 = 2.7; kpka = 0.097; ModelValue_17 = 0.01865; ModelValue_30 = 0.05 | Reaction: M = (ModelValue_30*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M, Rate Law: (ModelValue_30*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M |
ModelValue_2 = 2.7; ARPPtot = 10.0; ModelValue_9 = 0.09; ModelValue_29 = 1.2; ModelValue_6 = 0.05; ModelValue_0 = 10.0; ModelValue_8 = 0.0988; ModelValue_25 = 0.37526 | Reaction: A46 = ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex, Rate Law: ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex |
States:
Name | Description |
---|---|
BB | [urn:miriam:pr:PR%3AP56212-2] |
A88 | [urn:miriam:pr:PR_P56212-2] |
M | [Microtubule-associated serine/threonine-protein kinase 3] |
Complex | [protein phosphatase type 2A complex] |
PKA | [cAMP-dependent protein kinase catalytic subunit alpha] |
A46 | [urn:miriam:pr:PR_P56212-2] |
BIOMD0000000645
— v0.0.1Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3 and dominant…
Details
ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition. link: http://identifiers.org/doi/10.7554/eLife.24998
Parameters:
Name | Description |
---|---|
ModelValue_3 = 12.0; ModelValue_18 = 0.7; ModelValue_20 = 10.0; ModelValue_22 = 0.0; ModelValue_19 = 2.0; ModelValue_21 = 0.02335 | Reaction: PKA = ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA, Rate Law: ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA |
ModelValue_1 = 2.0 | Reaction: Complex = (BB-(BB^2-4*A46*ModelValue_1)^(0.5))/2, Rate Law: missing |
ModelValue_12 = 0.5; ModelValue_14 = 1.0; ARPPtot = 10.0; ModelValue_28 = 2.36; ModelValue_10 = 0.935; ModelValue_11 = 1.6; ModelValue_0 = 10.0; ModelValue_13 = 5.0 | Reaction: A88 = ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88), Rate Law: ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88) |
ModelValue_2 = 2.7; kpka = 0.097; ModelValue_17 = 0.01865; ModelValue_33 = 0.05 | Reaction: M = (ModelValue_33*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M, Rate Law: (ModelValue_33*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M |
ModelValue_1 = 2.0; kmpp2a = 0.0484545454545436 | Reaction: BB = A46+ModelValue_1+kmpp2a, Rate Law: missing |
ModelValue_2 = 2.7; ARPPtot = 10.0; ModelValue_9 = 0.09; ModelValue_29 = 1.2; ModelValue_6 = 0.05; ModelValue_0 = 10.0; ModelValue_8 = 0.0988; ModelValue_25 = 0.37526 | Reaction: A46 = ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex, Rate Law: ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex |
States:
Name | Description |
---|---|
BB | [urn:miriam:pr:PR_P56212-2] |
A88 | [urn:miriam:pr:PR_P56212-2] |
M | [Microtubule-associated serine/threonine-protein kinase 3] |
Complex | [protein phosphatase type 2A complex] |
PKA | [cAMP-dependent protein kinase catalytic subunit alpha] |
A46 | [urn:miriam:pr:PR_P56212-2] |
MODEL1812040004
— v0.0.1A mathematical model was designed to explore the co-interaction of gonorrhea and HIV in the presence of antiretroviral t…
Details
A mathematical model was designed to explore the co-interaction of gonorrhea and HIV in the presence of antiretroviral therapy and gonorrhea treatment. Qualitative and comprehensive mathematical techniques have been used to analyse the model. The gonorrhea-only and HIV-only sub-models are first considered. Analytic expressions for the threshold parameter in each sub-model and the co-interaction model are derived. Global dynamics of this co-interaction shows that whenever the threshold parameter for the respective sub-models and co-interaction model is less than unity, the epidemics dies out, while the reverse results in persistence of the epidemics in the community. The impact of gonorrhea and its treatment on HIV dynamics is also investigated. Numerical simulations using a set of reasonable parameter values show that the two epidemics co-exists whenever their reproduction numbers exceed unity (with no competitive exclusion). Further, simulations of the full HIV-gonorrhea model also suggests that an increase in the number of individuals infected with gonorrhea (either singly or dually with HIV) in the presence of treatment results in a decrease in gonorrhea-only cases, dual-infection cases but increases the number of HIV-only cases. link: http://identifiers.org/pubmed/20869424
BIOMD0000000964
— v0.0.1Objective: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused…
Details
OBJECTIVE:Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number [Formula: see text]. The model equations are solved numerically using fourth and fifth order Runge-Kutta methods. RESULTS:We found an [Formula: see text] of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus. link: http://identifiers.org/pubmed/32703315
MODEL1812040007
— v0.0.1Mathematical analysis of a cholera model with public health interventions
Details
Cholera, an acute gastro-intestinal infection and a waterborne disease continues to emerge in developing countries and remains an important global health challenge. We formulate a mathematical model that captures some essential dynamics of cholera transmission to study the impact of public health educational campaigns, vaccination and treatment as control strategies in curtailing the disease. The education-induced, vaccination-induced and treatment-induced reproductive numbers R(E), R(V), R(T) respectively and the combined reproductive number R(C) are compared with the basic reproduction number R(0) to assess the possible community benefits of these control measures. A Lyapunov functional approach is also used to analyse the stability of the equilibrium points. We perform sensitivity analysis on the key parameters that drive the disease dynamics in order to determine their relative importance to disease transmission and prevalence. Graphical representations are provided to qualitatively support the analytical results. link: http://identifiers.org/pubmed/null
N
BIOMD0000000353
— v0.0.1This model is from the article: Kinetic modeling and exploratory numerical simulation of chloroplastic starch degrad…
Details
BACKGROUND: Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date. RESULTS: Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing β-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter k(cat) and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase k(cat) and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments. CONCLUSIONS: We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions. link: http://identifiers.org/pubmed/21682905
Parameters:
Name | Description |
---|---|
R06050CY_GlcAG_KM = 2100.0 µmol/l; R06050CY_G1P_KM = 2000.0 µmol/l; R06050CY_GlcAG_Ki = 3800.0 µmol/l; R06050CY_AG_KM = 3800.0 µmol/l; R06050CY_Pi_KM = 5900.0 µmol/l; R06050CY_kcat = 50.0 1/s; R06050CY_Keq = 6.15E-4 1; R06050CY_G1P_Ki = 3100.0 µmol/l | Reaction: cpd_C00569Glc_CY + cpd_C00009tot_CY => cpd_C00103tot_CY + cpd_C00569_CY; ec_2_4_1_1_CY, Rate Law: Cytosol*R06050CY_kcat*ec_2_4_1_1_CY/Cytosol*(cpd_C00569Glc_CY/Cytosol*cpd_C00009tot_CY/Cytosol-cpd_C00103tot_CY/Cytosol*cpd_C00569_CY/Cytosol/R06050CY_Keq)/(R06050CY_GlcAG_Ki*R06050CY_Pi_KM+R06050CY_Pi_KM*cpd_C00569Glc_CY/Cytosol+R06050CY_GlcAG_KM*cpd_C00009tot_CY/Cytosol+cpd_C00569Glc_CY/Cytosol*cpd_C00009tot_CY/Cytosol+R06050CY_GlcAG_Ki*R06050CY_Pi_KM/(R06050CY_G1P_Ki*R06050CY_AG_KM)*(R06050CY_AG_KM*cpd_C00103tot_CY/Cytosol+R06050CY_G1P_KM*cpd_C00569_CY/Cytosol+cpd_C00103tot_CY/Cytosol*cpd_C00569_CY/Cytosol)) |
f_bamylase = 0.582 1; conv_gm_umole = 1.0 µg/mol; R02112CS_Gn_KM = 0.5 g/l; f_G3 = 0.13 1; R02112CS_Gn_kcat = 0.073 1/s | Reaction: cpd_C00369Glc_CS => cpd_C01835_CS; ec_3_2_1_2_CS, cpd_C00369_CS, cpd_C00369db_CS, Rate Law: ChloroplastStroma*R02112CS_Gn_kcat*ec_3_2_1_2_CS/ChloroplastStroma*f_G3*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)/(conv_gm_umole*(f_G3*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)+R02112CS_Gn_KM)) |
R02112CS_G2C_KM = 4.19 g²/l²; f_bamylase = 0.582 1; f_G2 = 0.87 1; conv_gm_umole = 1.0 µg/mol; R02112CS_Keq = 18800.0 g/l; R02112CS_Gn_KM = 0.5 g/l; C00208_MW = 3.42E-4 µg/mol; R02112CS_Gn_kcat = 0.073 1/s | Reaction: cpd_C00369Glc_CS => cpd_C00208_CS; ec_3_2_1_2_CS, cpd_C00369_CS, cpd_C00369db_CS, Rate Law: ChloroplastStroma*R02112CS_Gn_kcat*ec_3_2_1_2_CS/ChloroplastStroma*(f_G2*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)-(cpd_C00208_CS/ChloroplastStroma*C00208_MW)^2/R02112CS_Keq)/(conv_gm_umole*(f_G2*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)+R02112CS_Gn_KM*(1+(cpd_C00208_CS/ChloroplastStroma*C00208_MW)^2/R02112CS_G2C_KM))) |
f_bamylase = 0.582 1; ec_3_2_1_68_CS_kcat = 0.0198 1/s | Reaction: cpd_C00369db_CS = ec_3_2_1_68_CS/ChloroplastStroma*ec_3_2_1_68_CS_kcat*((1-1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3))))+1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*(1-1.429*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*ChloroplastStroma, Rate Law: ec_3_2_1_68_CS/ChloroplastStroma*ec_3_2_1_68_CS_kcat*((1-1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3))))+1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*(1-1.429*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*ChloroplastStroma |
TC_2_A_1_1_17_KM = 19300.0 µmol/l; TC_2_A_1_1_17_kcat = 240.278 1/s | Reaction: cpd_C00031_CS => cpd_C00031_CY; tc_2_A_1_1_17_CIMS, Rate Law: ChloroplastStroma*TC_2_A_1_1_17_kcat*tc_2_A_1_1_17_CIMS/ChloroplastIntermembraneSpace*cpd_C00031_CS/ChloroplastStroma/(TC_2_A_1_1_17_KM+cpd_C00031_CS/ChloroplastStroma) |
R05196CS_G3_Ki = 746.42 µmol/l; R05196CS_G5_Ki = 100.0 µmol/l; R05196CS_Glc_KM = 11700.0 µmol/l; R05196CS_G3_KM = 3300.0 µmol/l; R05196CS_Keq = 1.0 1; R05196CS_G5_KM = 210.0 µmol/l; R05196CS_kcat = 50.0 1/s | Reaction: cpd_C01835_CS => cpd_C00031_CS + cpd_G00343_CS; ec_2_4_1_25_CS, Rate Law: ChloroplastStroma*R05196CS_kcat*ec_2_4_1_25_CS/ChloroplastStroma*((cpd_C01835_CS/ChloroplastStroma)^2-cpd_C00031_CS/ChloroplastStroma*cpd_G00343_CS/ChloroplastStroma/R05196CS_Keq)/(R05196CS_G3_KM*cpd_C01835_CS/ChloroplastStroma+(cpd_C01835_CS/ChloroplastStroma)^2+R05196CS_G3_KM*R05196CS_G3_Ki/(R05196CS_Glc_KM*R05196CS_G5_Ki)*(R05196CS_G5_KM*cpd_C00031_CS/ChloroplastStroma*(1+cpd_C01835_CS/ChloroplastStroma/R05196CS_G3_Ki)+R05196CS_Glc_KM*cpd_G00343_CS/ChloroplastStroma*(1+cpd_C01835_CS/ChloroplastStroma/R05196CS_G3_Ki)+cpd_C00031_CS/ChloroplastStroma*cpd_G00343_CS/ChloroplastStroma)) |
TC_2_A_84_1_2_kcat = 5.963 1/s; TC_2_A_84_1_2_KM = 4000.0 µmol/l | Reaction: cpd_C00208_CS => cpd_C00208_CY; tc_2_A_84_1_2_CIMS, Rate Law: ChloroplastStroma*TC_2_A_84_1_2_kcat*tc_2_A_84_1_2_CIMS/ChloroplastIntermembraneSpace*cpd_C00208_CS/ChloroplastStroma/(TC_2_A_84_1_2_KM+cpd_C00208_CS/ChloroplastStroma) |
AT2G40840CY_Keq = 1.0 1; AT2G40840CY_G2_KM = 4600.0 µmol/l; AT2G40840CY_Glc_KM = 11700.0 µmol/l; AT2G40840CY_G2_Ki = 2190.476 µmol/l; AT2G40840CY_AG_Ki = 1000.0 µmol/l; AT2G40840CY_GlcAG_KM = 1100.0 µmol/l; AT2G40840CY_AG_KM = 1100.0 µmol/l; AT2G40840CY_kcat = 50.0 1/s; AT2G40840CY_GlcAG_Ki = 1000.0 µmol/l | Reaction: cpd_C00208_CY + cpd_C00569_CY => cpd_C00031_CY + cpd_C00569Glc_CY; ec_2_4_1_25_CY, Rate Law: Cytosol*AT2G40840CY_kcat*ec_2_4_1_25_CY/Cytosol*(cpd_C00208_CY/Cytosol*cpd_C00569_CY/Cytosol-cpd_C00031_CY/Cytosol*cpd_C00569Glc_CY/Cytosol/AT2G40840CY_Keq)/(AT2G40840CY_AG_KM*cpd_C00208_CY/Cytosol+AT2G40840CY_G2_KM*cpd_C00569_CY/Cytosol+cpd_C00208_CY/Cytosol*cpd_C00569_CY/Cytosol+AT2G40840CY_G2_KM*AT2G40840CY_AG_Ki/(AT2G40840CY_Glc_KM*AT2G40840CY_GlcAG_Ki)*(AT2G40840CY_GlcAG_KM*cpd_C00031_CY/Cytosol*(1+cpd_C00208_CY/Cytosol/AT2G40840CY_G2_Ki)+AT2G40840CY_Glc_KM*cpd_C00569Glc_CY/Cytosol*(1+cpd_C00569_CY/Cytosol/AT2G40840CY_AG_Ki)+cpd_C00031_CY/Cytosol*cpd_C00569Glc_CY/Cytosol)) |
R02112CS_G5_kcat = 0.0913 1/s; G00343_MW = 8.28E-4 µg/mol; conv_gm_umole = 1.0 µg/mol; R02112CS_G5_KM = 1.46 g/l | Reaction: cpd_G00343_CS => cpd_C00208_CS + cpd_C01835_CS; ec_3_2_1_2_CS, Rate Law: ChloroplastStroma*R02112CS_G5_kcat*ec_3_2_1_2_CS/ChloroplastStroma*cpd_G00343_CS/ChloroplastStroma*G00343_MW/(conv_gm_umole*(cpd_G00343_CS/ChloroplastStroma*G00343_MW+R02112CS_G5_KM)) |
C00369_MW = 0.27 µg/mol; N_Glc_Starch = 1667.0 1 | Reaction: cpd_C00369_CS = cpd_C00369Glc_CS/ChloroplastStroma*C00369_MW/N_Glc_Starch*ChloroplastStroma, Rate Law: missing |
R00299CY_G6P_KM = 47.0 µmol/l; R00299CY_kfor = 180.0 1/s; R00299CY_G16P_Kip = 30.0 µmol/l; R00299CY_Glc_Ki = 47.0 µmol/l; R00299CY_MgADP_Ki = 1000.0 µmol/l; R00299CY_MgATP_Ki = 1000.0 µmol/l; R00299CY_G6P_Kip = 10.0 µmol/l; R00299CY_krev = 1.16129032258065 1/s; R00299CY_BPG_Kip = 4000.0 µmol/l; R00299CY_GSH_Kip = 3000.0 µmol/l; R00299CY_MgATP_KM = 1000.0 µmol/l; R00299CY_G6P_Ki = 47.0 µmol/l | Reaction: cpd_C00002tot_CY + cpd_C00031_CY => cpd_C00092tot_CY + cpd_C00008tot_CY + cpd_C00080_CY; ec_2_7_1_1_CY, cpd_C00051_CY, cpd_C00660tot_CY, cpd_C03339tot_CY, Rate Law: Cytosol*ec_2_7_1_1_CY/Cytosol*(R00299CY_kfor*cpd_C00002tot_CY/Cytosol*cpd_C00031_CY/Cytosol/(R00299CY_Glc_Ki*R00299CY_MgATP_KM)-R00299CY_krev*cpd_C00092tot_CY/Cytosol*cpd_C00008tot_CY/Cytosol/(R00299CY_MgADP_Ki*R00299CY_G6P_KM))/(1+cpd_C00002tot_CY/Cytosol/R00299CY_MgATP_Ki+cpd_C00031_CY/Cytosol/R00299CY_Glc_Ki*(1+cpd_C00092tot_CY/Cytosol/R00299CY_G6P_Kip+cpd_C00660tot_CY/Cytosol/R00299CY_G16P_Kip+cpd_C03339tot_CY/Cytosol/R00299CY_BPG_Kip+cpd_C00051_CY/Cytosol/R00299CY_GSH_Kip)+cpd_C00002tot_CY/Cytosol*cpd_C00031_CY/Cytosol/(R00299CY_Glc_Ki*R00299CY_MgATP_KM)+cpd_C00092tot_CY/Cytosol/R00299CY_G6P_Ki+cpd_C00008tot_CY/Cytosol/R00299CY_MgADP_Ki+cpd_C00092tot_CY/Cytosol*cpd_C00008tot_CY/Cytosol/(R00299CY_MgADP_Ki*R00299CY_G6P_KM)) |
States:
Name | Description |
---|---|
cpd C00569 CY | [simple chemical; arabinogalactan; Arabinogalactan] |
cpd C00002tot CY | [simple chemical; ATP; ATP] |
cpd C00080 CY | [non-macromolecular ion; H+; proton] |
cpd C00103tot CY | [simple chemical; C11450; alpha-D-glucose 1-phosphate] |
cpd C00208 CY | [simple chemical; maltose; Maltose] |
cpd C00369Glc CS | [simple chemical; MOD:00726; Starch; starch] |
cpd C00031 CS | [simple chemical; D-glucose] |
cpd C00031 CY | [simple chemical; D-Glucose; D-glucose] |
cpd C00569Glc CY | [simple chemical; arabinogalactan; Arabinogalactan; MOD:00726] |
cpd C00369db CS | [simple chemical; Starch; starch; MOD:00726] |
cpd C00009tot CY | [simple chemical; Orthophosphate; phosphate(3-)] |
cpd C00208 CS | [simple chemical; maltose; Maltose] |
cpd C00369 CS | [simple chemical; Starch; starch] |
cpd G00343 CS | [simple chemical; maltopentaose] |
cpd C00092tot CY | [simple chemical; alpha-D-Glucose 6-phosphate; alpha-D-glucose 6-phosphate] |
cpd C01835 CS | [simple chemical; maltotriose; Maltotriose] |
cpd C00008tot CY | [simple chemical; ADP; ADP] |
BIOMD0000000747
— v0.0.1Mathematical model of blood coagulation and the effects of inhibitors of Xa, Va:Xa and IIa.
Details
The present study began with mathematical modeling of how inhibitors of both factor Xa (fXa) and thrombin affect extrinsic pathway-triggered blood coagulation. Numerical simulation demonstrated a stronger inhibition of thrombin generation by a thrombin inhibitor than a fXa inhibitor, but both prolonged clot time to a similar extent when they were given an equal dissociation constant (30 nm) for interaction with their respective target enzymes. These differences were then tested by comparison with the real inhibitors DX-9065a and argatroban, specific competitive inhibitors of fXa and thrombin, respectively, with similar K(i) values. Comparisons were made in extrinsically triggered human citrated plasma, for which endogenous thrombin potential and clot formation were simultaneously measured with a Wallac multilabel counter equipped with both fluorometric and photometric detectors and a fluorogenic reporter substrate. The results demonstrated stronger inhibition of endogenous thrombin potential by argatroban than by DX-9065a, especially when coagulation was initiated at higher tissue factor concentrations, while argatroban appeared to be slightly less potent in its ability to prolong clot time. This study demonstrates differential inhibition of thrombin generation by fXa and thrombin inhibitors and has implications for the pharmacological regulation of blood coagulation by the anticoagulant protease inhibitors. link: http://identifiers.org/pubmed/12496240
Parameters:
Name | Description |
---|---|
k02 = 2.2; k01 = 0.1 | Reaction: TF_VIIa + IX => TF_VIIa_IX, Rate Law: compartment*(k01*TF_VIIa*IX-k02*TF_VIIa_IX) |
k17 = 29.0 | Reaction: VIIIa_IXa_X => VIIIa_IXa + Xa, Rate Law: compartment*k17*VIIIa_IXa_X |
k24 = 0.1; k25 = 0.1 | Reaction: Xa + Va => Va_Xa, Rate Law: compartment*(k24*Xa*Va-k25*Va_Xa) |
k31 = 84.0 | Reaction: Fibrinogen_IIa => Fibrin + IIa, Rate Law: compartment*k31*Fibrinogen_IIa |
k41 = 3.0; k40 = 0.1 | Reaction: Va_Xa + Xa_Inhibitor => Va_Xa_Xa_Inhibitor, Rate Law: compartment*(k40*Va_Xa*Xa_Inhibitor-k41*Va_Xa_Xa_Inhibitor) |
k06 = 1.4 | Reaction: TF_VIIa_X => TF_VIIa + Xa, Rate Law: compartment*k06*TF_VIIa_X |
k36 = 0.1; k37 = 3.0 | Reaction: Xa + Xa_Inhibitor => Xa_Xa_Inhibitor, Rate Law: compartment*(k36*Xa*Xa_Inhibitor-k37*Xa_Xa_Inhibitor) |
k08 = 2.1; k07 = 0.1 | Reaction: Xa + VIII => Xa_VIII, Rate Law: compartment*(k07*Xa*VIII-k08*Xa_VIII) |
k43 = 3.0; k42 = 0.1 | Reaction: IIa + IIa_Inhibitor => IIa_IIa_Inhibitor, Rate Law: compartment*(k42*IIa*IIa_Inhibitor-k43*IIa_IIa_Inhibitor) |
k16 = 19.0; k15 = 0.1 | Reaction: VIIIa_IXa + X => VIIIa_IXa_X, Rate Law: compartment*(k15*VIIIa_IXa*X-k16*VIIIa_IXa_X) |
k30 = 720.0; k29 = 0.1 | Reaction: Fibrinogen + IIa => Fibrinogen_IIa, Rate Law: compartment*(k29*Fibrinogen*IIa-k30*Fibrinogen_IIa) |
k28 = 35.0 | Reaction: Va_Xa_II => Va_Xa + IIa, Rate Law: compartment*k28*Va_Xa_II |
k14 = 0.17; k13 = 0.1 | Reaction: VIIIa + IXa => VIIIa_IXa, Rate Law: compartment*(k13*VIIIa*IXa-k14*VIIIa_IXa) |
k04 = 0.1; k05 = 5.5 | Reaction: TF_VIIa + X => TF_VIIa_X, Rate Law: compartment*(k04*TF_VIIa*X-k05*TF_VIIa_X) |
k12 = 0.9 | Reaction: IIa_VIII => IIa + VIIIa, Rate Law: compartment*k12*IIa_VIII |
k34 = 0.011 | Reaction: Xa => Xa_inact, Rate Law: compartment*k34*Xa |
k20 = 0.043 | Reaction: Xa_V => Xa + Va, Rate Law: compartment*k20*Xa_V |
k18 = 0.1; k19 = 1.0 | Reaction: Xa + V => Xa_V, Rate Law: compartment*(k18*Xa*V-k19*Xa_V) |
k32 = 0.0011 | Reaction: VIIIa => VIIIa_inact, Rate Law: compartment*k32*VIIIa |
k35 = 0.024 | Reaction: IIa => IIa_inact, Rate Law: compartment*k35*IIa |
k11 = 15.0; k10 = 0.1 | Reaction: IIa + VIII => IIa_VIII, Rate Law: compartment*(k10*IIa*VIII-k11*IIa_VIII) |
k33 = 0.0017 | Reaction: IXa => IXa_inact, Rate Law: compartment*k33*IXa |
k23 = 0.26 | Reaction: IIa_V => IIa + Va, Rate Law: compartment*k23*IIa_V |
k03 = 0.47 | Reaction: TF_VIIa_IX => TF_VIIa + IXa, Rate Law: compartment*k03*TF_VIIa_IX |
k38 = 0.1; k39 = 0.1 | Reaction: Va + Xa_Xa_Inhibitor => Va_Xa_Xa_Inhibitor, Rate Law: compartment*(k38*Va*Xa_Xa_Inhibitor-k39*Va_Xa_Xa_Inhibitor) |
k22 = 7.2; k21 = 0.1 | Reaction: IIa + V => IIa_V, Rate Law: compartment*(k21*IIa*V-k22*IIa_V) |
k26 = 0.1; k27 = 100.0 | Reaction: Va_Xa + II => Va_Xa_II, Rate Law: compartment*(k26*Va_Xa*II-k27*Va_Xa_II) |
k09 = 0.023 | Reaction: Xa_VIII => Xa + VIIIa, Rate Law: compartment*k09*Xa_VIII |
States:
Name | Description |
---|---|
IIa inact | [Thrombin] |
VIIIa IXa X | [Coagulation Factor X Human; Coagulation Factor IX Human; Coagulation Factor VIII] |
VIII | [Coagulation Factor VIII] |
Fibrin | [Fibrin] |
IIa IIa Inhibitor | [EC 3.4.21.5 (thrombin) inhibitor; Thrombin] |
V | [Coagulation Factor V] |
Xa VIII | [Coagulation Factor VIII; Coagulation Factor X Human] |
Xa | [Coagulation Factor X Human] |
IIa Inhibitor | [EC 3.4.21.5 (thrombin) inhibitor] |
VIIIa inact | [Coagulation Factor VIII] |
Va Xa | [Coagulation Factor X Human; Coagulation Factor V] |
IIa VIII | [Coagulation Factor VIII; Thrombin] |
TF VIIa X | [Coagulation Factor X Human; Coagulation Factor VII Human; Tissue Factor] |
Fibrinogen IIa | [Thrombin; Fibrinogen] |
Xa V | [Coagulation Factor V; Coagulation Factor X Human] |
Xa Xa Inhibitor | [EC 3.4.21.5 (thrombin) inhibitor; Coagulation Factor X Human] |
Fibrinogen | [Fibrinogen] |
X | [Coagulation Factor X Human] |
Va Xa II | [Coagulation Factor X Human; Prothrombin; Coagulation Factor V] |
Xa inact | [Coagulation Factor X Human] |
TF VIIa | [Coagulation Factor VII Human; Tissue Factor] |
VIIIa | [Coagulation Factor VIII] |
Xa Inhibitor | [EC 3.4.21.6 (coagulation factor Xa) inhibitor] |
IIa V | [Coagulation Factor V; Thrombin] |
Va | [Coagulation Factor V] |
IIa | [Thrombin] |
TF VIIa IX | [Coagulation Factor IX Human; Tissue Factor; Coagulation Factor VII Human] |
Va Xa Xa Inhibitor | [Coagulation Factor X Human; Coagulation Factor V; EC 3.4.21.6 (coagulation factor Xa) inhibitor] |
IXa | [Coagulation Factor IX Human] |
VIIIa IXa | [Coagulation Factor IX Human; Coagulation Factor VIII] |
II | [Prothrombin] |
IX | [Coagulation Factor IX Human] |
IXa inact | [Coagulation Factor IX Human] |
BIOMD0000000635
— v0.0.1Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1 Neuron)This model is describ…
Details
Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation. link: http://identifiers.org/pubmed/26468202
Parameters:
Name | Description |
---|---|
mw05f4bef4_5e8d_4a92_bb74_cc0bb4c0260e = 1.0; mw77fab49b_2ba6_4efe_9342_285f4fd3b7fa = 0.01 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw219e8fae_a38b_4620_8726_e6bd1829a351 => mwf46d3666_f0f3_4f05_9603_d7e6bb69005e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw77fab49b_2ba6_4efe_9342_285f4fd3b7fa*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw219e8fae_a38b_4620_8726_e6bd1829a351-mw05f4bef4_5e8d_4a92_bb74_cc0bb4c0260e*mwf46d3666_f0f3_4f05_9603_d7e6bb69005e) |
ModelValue_145 = 100.0; AChdip = 1.0; ModelValue_143 = 100.0; ModelValue_138 = 0.0; ModelValue_144 = 0.001 | Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 = (1-ModelValue_138)*ModelValue_143+ModelValue_138*(ModelValue_144+(ModelValue_145-ModelValue_144)*AChdip), Rate Law: missing |
mw7419e1e3_b601_44a8_93ff_e5b31995791e = 0.08; mw4a930624_fcc1_4d08_8e24_9a0082418629 = 0.04 | Reaction: mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw06380287_79c9_4f85_aed6_fa34e7bcdff1 => mwc57c3c2e_69d5_4336_aff5_d1f429420df2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw4a930624_fcc1_4d08_8e24_9a0082418629*mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0*mw06380287_79c9_4f85_aed6_fa34e7bcdff1-mw7419e1e3_b601_44a8_93ff_e5b31995791e*mwc57c3c2e_69d5_4336_aff5_d1f429420df2) |
mwf633f298_303f_46d1_b644_ae07ae366f45 = 3.0 | Reaction: mw6e845d87_603e_4463_874d_866f554303df => mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwf633f298_303f_46d1_b644_ae07ae366f45*mw6e845d87_603e_4463_874d_866f554303df |
mw515fcf69_b724_40d9_84ba_5f92d75ae5a7 = 1.5E-4 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw515fcf69_b724_40d9_84ba_5f92d75ae5a7*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw7df45520_98cc_4c0b_91a7_c6e7297de98a |
mw269c014a_6379_44c3_813b_52d8145506e7 = 1.0E-4; mwc4c3d33d_b2b7_4ab2_a171_1864ea638ec0 = 0.1 | Reaction: mw522cacf1_5e61_4b95_8742_cf61cb824893 + mwccd3a17c_e207_4663_9b16_327b78882497 => mw3fcd1ec2_a459_49d4_89f7_361e276096d6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw269c014a_6379_44c3_813b_52d8145506e7*mw522cacf1_5e61_4b95_8742_cf61cb824893*mwccd3a17c_e207_4663_9b16_327b78882497-mwc4c3d33d_b2b7_4ab2_a171_1864ea638ec0*mw3fcd1ec2_a459_49d4_89f7_361e276096d6) |
mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9 = 2.8125E-5 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc |
mw448bd49f_40ad_46c9_81f6_3494057dc37d = 0.003; mwa466eec8_9bc0_44d5_8027_d5925b378429 = 5.0 | Reaction: mwe2fc02e6_2684_4071_932a_f7a8bd13b2fe + mw351f6cee_3e64_4b8e_8e60_24b1aca99a92 => mw0b46978f_b522_4cde_97f0_574cd7dbbae7, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw448bd49f_40ad_46c9_81f6_3494057dc37d*mwe2fc02e6_2684_4071_932a_f7a8bd13b2fe*mw351f6cee_3e64_4b8e_8e60_24b1aca99a92-mwa466eec8_9bc0_44d5_8027_d5925b378429*mw0b46978f_b522_4cde_97f0_574cd7dbbae7) |
mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79 = 0.1; mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296 = 2.0 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw3d9e6efb_8e12_49c9_a87f_e067914b951d => mw6e845d87_603e_4463_874d_866f554303df, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw3d9e6efb_8e12_49c9_a87f_e067914b951d-mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296*mw6e845d87_603e_4463_874d_866f554303df) |
mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d = 7.5E-5; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 | Reaction: mw7df45520_98cc_4c0b_91a7_c6e7297de98a + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d*mw7df45520_98cc_4c0b_91a7_c6e7297de98a*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw619502c3_e319_4e29_a677_b2b5f74fc2cf) |
mw009f9583_4e96_4672_ab71_0ef4b697aa6f = 6.4; mw2226fa14_2b95_45a6_8705_4b38073fc5f7 = 8.0E-4 | Reaction: mw522cacf1_5e61_4b95_8742_cf61cb824893 + mw1184c368_03fc_435a_9086_dc6ed3067935 => mw0459271f_3b39_40a4_948f_aed773482cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw2226fa14_2b95_45a6_8705_4b38073fc5f7*mw522cacf1_5e61_4b95_8742_cf61cb824893*mw1184c368_03fc_435a_9086_dc6ed3067935-mw009f9583_4e96_4672_ab71_0ef4b697aa6f*mw0459271f_3b39_40a4_948f_aed773482cfc) |
mw1db20a7e_3972_4c3a_83c0_c6fcd7c9cb45 = 3.0E-4; mw4e2575eb_3641_422c_b836_d854958d4d1e = 8.0 | Reaction: mw68d3f409_9462_4515_8c07_bc105fa0eaf1 + mw24435476_9c30_4878_b26f_4b3c5a0685c6 => mw4179e1ff_9035_4c67_a67c_099e25beb9b0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1db20a7e_3972_4c3a_83c0_c6fcd7c9cb45*mw68d3f409_9462_4515_8c07_bc105fa0eaf1*mw24435476_9c30_4878_b26f_4b3c5a0685c6-mw4e2575eb_3641_422c_b836_d854958d4d1e*mw4179e1ff_9035_4c67_a67c_099e25beb9b0) |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a = 1.75E-4 | Reaction: mw2badefa3_32e8_4b66_9e69_245d9ec74e33 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw07c7392b_8d89_4b94_97c5_59f7e256b6f2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a*mw2badefa3_32e8_4b66_9e69_245d9ec74e33*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2) |
mwfcfb91ff_a495_41f9_bdff_fcef779112fd = 30.0 | Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwfcfb91ff_a495_41f9_bdff_fcef779112fd*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 |
mwa390f769_ebf1_4023_8af0_1c00e2a9bf82 = 0.002; mw0beb6cc4_36bd_4022_8993_29f981652ebe = 1.0 | Reaction: mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw4855b1cd_d7bc_4072_9736_dca30bbe448d => mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwa390f769_ebf1_4023_8af0_1c00e2a9bf82*mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0*mw4855b1cd_d7bc_4072_9736_dca30bbe448d-mw0beb6cc4_36bd_4022_8993_29f981652ebe*mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9) |
mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb = 5.0 | Reaction: mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e |
mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b = 90.0; mw066c69e2_66da_4621_9180_bce71b7077c3 = 1.0 | Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwd86ce0dc_7329_4b27_9de0_ee6bffee3083 => mwe4e36b8e_18b8_4c76_bd46_13614b71da5c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw066c69e2_66da_4621_9180_bce71b7077c3*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwd86ce0dc_7329_4b27_9de0_ee6bffee3083-mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c) |
mwdcc4ce84_732d_4f5b_84e2_e5b93617200b = 0.3 | Reaction: mw8825a609_0983_4fb4_a264_e2f7e43d17b3 => mw3fcd1ec2_a459_49d4_89f7_361e276096d6 + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwdcc4ce84_732d_4f5b_84e2_e5b93617200b*mw8825a609_0983_4fb4_a264_e2f7e43d17b3 |
mw2561b5ab_39c9_4453_99d8_f0f37779b63a = 10.0 | Reaction: mw4179e1ff_9035_4c67_a67c_099e25beb9b0 => mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw68d3f409_9462_4515_8c07_bc105fa0eaf1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw2561b5ab_39c9_4453_99d8_f0f37779b63a*mw4179e1ff_9035_4c67_a67c_099e25beb9b0 |
mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9 = 2.0; mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2 = 0.13 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw1041345b_f015_436c_9eff_98211008aa1c => mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw1041345b_f015_436c_9eff_98211008aa1c-mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43) |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw72ceb3da_d538_4f25_8e69_f322eb0b5e57 = 0.00105 | Reaction: mwfe9ed415_d5af_469c_a549_d8981f1eb01f + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw166e3335_56c3_41ef_af0f_b583860991c1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw72ceb3da_d538_4f25_8e69_f322eb0b5e57*mwfe9ed415_d5af_469c_a549_d8981f1eb01f*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw166e3335_56c3_41ef_af0f_b583860991c1) |
mw88c9326a_fbe9_4dd8_aded_b5be3f012691 = 2.3 | Reaction: mwde741b91_d5bf_44a9_ad45_404d7259d051 => mw081c9f7b_011e_440f_971d_d0316d2a1e6c + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw88c9326a_fbe9_4dd8_aded_b5be3f012691*mwde741b91_d5bf_44a9_ad45_404d7259d051 |
mw5175a06e_3927_4993_9242_8f76b0aaf42f = 100.0 | Reaction: mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8 + mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1 => mwd8ea533a_c66e_4de4_8c5c_0d4201d8c8a2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5175a06e_3927_4993_9242_8f76b0aaf42f*mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8*mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1 |
mw65cae8fe_0eac_4792_88bf_2dfb441030e5 = 0.5 | Reaction: mw619502c3_e319_4e29_a677_b2b5f74fc2cf => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw65cae8fe_0eac_4792_88bf_2dfb441030e5*mw619502c3_e319_4e29_a677_b2b5f74fc2cf |
mwac1bc66c_2623_47e6_a76d_c1629d962be5 = 10.0 | Reaction: mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43 => mw1041345b_f015_436c_9eff_98211008aa1c + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwac1bc66c_2623_47e6_a76d_c1629d962be5*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43 |
mw6af7af00_75ac_4f58_8383_7047a5fb5181 = 1.0 | Reaction: mw0459271f_3b39_40a4_948f_aed773482cfc => mw522cacf1_5e61_4b95_8742_cf61cb824893 + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw6af7af00_75ac_4f58_8383_7047a5fb5181*mw0459271f_3b39_40a4_948f_aed773482cfc |
mwe737a297_e5be_46ed_af75_ccc7428c3977 = 0.001; mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b = 0.9 | Reaction: mwccd3a17c_e207_4663_9b16_327b78882497 + mw7086a13a_619e_4069_b163_d8a05fc55f42 => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwe737a297_e5be_46ed_af75_ccc7428c3977*mwccd3a17c_e207_4663_9b16_327b78882497*mw7086a13a_619e_4069_b163_d8a05fc55f42-mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b*mw619502c3_e319_4e29_a677_b2b5f74fc2cf) |
mw8e4e88b6_60b3_43bd_8f5c_923712ee64ea = 5.0; mwb17941e5_1ad5_42b9_98c6_e62b1a697dbb = 0.003 | Reaction: mwdb9dc389_2bf0_4039_9f09_282f5511958b + mw351f6cee_3e64_4b8e_8e60_24b1aca99a92 => mw6b2f1c44_e0be_4406_bcef_ad5061d519e4, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb17941e5_1ad5_42b9_98c6_e62b1a697dbb*mwdb9dc389_2bf0_4039_9f09_282f5511958b*mw351f6cee_3e64_4b8e_8e60_24b1aca99a92-mw8e4e88b6_60b3_43bd_8f5c_923712ee64ea*mw6b2f1c44_e0be_4406_bcef_ad5061d519e4) |
mwcd307ee9_33da_4303_9c28_644ad2d1630c = 0.1; mw0a255671_d9ca_4384_a153_ce17e1111453 = 0.2 | Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfe9ed415_d5af_469c_a549_d8981f1eb01f, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0a255671_d9ca_4384_a153_ce17e1111453*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c-mwcd307ee9_33da_4303_9c28_644ad2d1630c*mwfe9ed415_d5af_469c_a549_d8981f1eb01f) |
mw0dd72d64_80e1_4f76_a444_fd175dbfab0c = 15.0 | Reaction: mw6b2f1c44_e0be_4406_bcef_ad5061d519e4 => mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 + mwdb9dc389_2bf0_4039_9f09_282f5511958b + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw0dd72d64_80e1_4f76_a444_fd175dbfab0c*mw6b2f1c44_e0be_4406_bcef_ad5061d519e4 |
mwaa3af366_350e_4f18_936b_6372dc484f82 = 4.0E-4 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw724f1afe_8032_40ae_96ca_808ab7b8b943 => mw7086a13a_619e_4069_b163_d8a05fc55f42, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwaa3af366_350e_4f18_936b_6372dc484f82*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw724f1afe_8032_40ae_96ca_808ab7b8b943 |
mwd1b16e73_4fcb_4e4c_9804_3137259ba749 = 1.0E-6; mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2 = 9.0 | Reaction: mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw1c97b02d_169a_4eb8_bc84_1be57c51a255 => mw1041345b_f015_436c_9eff_98211008aa1c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwd1b16e73_4fcb_4e4c_9804_3137259ba749*mw3d9e6efb_8e12_49c9_a87f_e067914b951d*mw1c97b02d_169a_4eb8_bc84_1be57c51a255^2-mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2*mw1041345b_f015_436c_9eff_98211008aa1c) |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4 = 5.5E-4 | Reaction: mw42919ead_5972_4151_85ac_fcc88ca105a6 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4*mw42919ead_5972_4151_85ac_fcc88ca105a6*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba) |
mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3 = 10.0 | Reaction: mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw42919ead_5972_4151_85ac_fcc88ca105a6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba |
mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39 = 0.002; mwc911f28c_b62f_4269_84ed_d852f6da24f9 = 0.01 | Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c + mw56dff932_134c_4d88_a611_daad00623fd0 => mw07c7392b_8d89_4b94_97c5_59f7e256b6f2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c*mw56dff932_134c_4d88_a611_daad00623fd0-mwc911f28c_b62f_4269_84ed_d852f6da24f9*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2) |
mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5 = 30.0 | Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c |
mwe79f507b_73c9_4056_ae91_6244dcbc49bb = 0.5; mw26206710_ba98_4010_9e5b_c3aae2ce29ec = 1.0 | Reaction: mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw3fcd1ec2_a459_49d4_89f7_361e276096d6 => mw8825a609_0983_4fb4_a264_e2f7e43d17b3, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwe79f507b_73c9_4056_ae91_6244dcbc49bb*mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0*mw3fcd1ec2_a459_49d4_89f7_361e276096d6-mw26206710_ba98_4010_9e5b_c3aae2ce29ec*mw8825a609_0983_4fb4_a264_e2f7e43d17b3) |
mw62c51fcf_c107_4d3c_849e_9b168df54490 = 10.0 | Reaction: mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9 => mw24435476_9c30_4878_b26f_4b3c5a0685c6 + mw4855b1cd_d7bc_4072_9736_dca30bbe448d, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw62c51fcf_c107_4d3c_849e_9b168df54490*mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9 |
mwce0df80f_1563_453d_b33d_a88f6b2c93b7 = 90.0; mw80292f32_fd53_4b5d_872a_e21c2d90c52a = 0.01 | Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwf82770b9_766a_4c4e_851a_d76da19e8517 => mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw80292f32_fd53_4b5d_872a_e21c2d90c52a*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwf82770b9_766a_4c4e_851a_d76da19e8517-mwce0df80f_1563_453d_b33d_a88f6b2c93b7*mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2) |
mwa4148cd1_a298_447c_aea8_226688c3f526 = 2.0 | Reaction: mwf46d3666_f0f3_4f05_9603_d7e6bb69005e => mw219e8fae_a38b_4620_8726_e6bd1829a351 + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwa4148cd1_a298_447c_aea8_226688c3f526*mwf46d3666_f0f3_4f05_9603_d7e6bb69005e |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb93138ce_a80b_4b26_b927_6b4a00651b64 = 3.0E-4 | Reaction: mwd794c746_c826_4ba1_9e09_a9d1e122d925 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb93138ce_a80b_4b26_b927_6b4a00651b64*mwd794c746_c826_4ba1_9e09_a9d1e122d925*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e) |
mw858f28f3_086a_436b_ba23_4fc7372c8884 = 5.0; mw3fc2c1ed_0097_4f7f_bcd5_904dc6ad5a56 = 0.005 | Reaction: mw0b46978f_b522_4cde_97f0_574cd7dbbae7 + mwbe974953_e869_4622_b4a8_745555c8d7fd => mw6b2f1c44_e0be_4406_bcef_ad5061d519e4, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw3fc2c1ed_0097_4f7f_bcd5_904dc6ad5a56*mw0b46978f_b522_4cde_97f0_574cd7dbbae7*mwbe974953_e869_4622_b4a8_745555c8d7fd-mw858f28f3_086a_436b_ba23_4fc7372c8884*mw6b2f1c44_e0be_4406_bcef_ad5061d519e4) |
mwcabc0868_2435_4850_964b_e3ddee39f5ad = 30.0 | Reaction: mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba + mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwcabc0868_2435_4850_964b_e3ddee39f5ad*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 |
mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761 = 5.0; mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6 = 50.0 | Reaction: mw7086a13a_619e_4069_b163_d8a05fc55f42 + mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6*mw7086a13a_619e_4069_b163_d8a05fc55f42*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3-mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761*mw2075d2cf_955e_4150_98b8_847103c53845) |
mwd05b4199_53ad_4807_9a8c_d93ce35be857 = 60.0 | Reaction: mwe4e36b8e_18b8_4c76_bd46_13614b71da5c => mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2 + mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwd05b4199_53ad_4807_9a8c_d93ce35be857*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c |
mw9c2302f8_3d47_4247_a338_a02c53fc5331 = 1.0E-4; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 | Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw7086a13a_619e_4069_b163_d8a05fc55f42, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9c2302f8_3d47_4247_a338_a02c53fc5331*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw7086a13a_619e_4069_b163_d8a05fc55f42) |
mwc728d91d_7616_43db_bd1d_55e49e9c026a = 0.125 | Reaction: mw56dff932_134c_4d88_a611_daad00623fd0 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwc728d91d_7616_43db_bd1d_55e49e9c026a*mw56dff932_134c_4d88_a611_daad00623fd0 |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwa1bc2233_5bb9_4135_88ed_bb51640faec8 = 5.625E-5 | Reaction: mwed1b3928_8d78_44d1_aee7_9d11d6437cfc + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwa1bc2233_5bb9_4135_88ed_bb51640faec8*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw56dff932_134c_4d88_a611_daad00623fd0) |
mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba = 0.2 | Reaction: mw166e3335_56c3_41ef_af0f_b583860991c1 => mw7086a13a_619e_4069_b163_d8a05fc55f42 + mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba*mw166e3335_56c3_41ef_af0f_b583860991c1 |
mwc52aebc2_571c_4f96_84ee_0613ae73db89 = 0.01; mw9330e49a_b214_4807_b614_4241a4a12c43 = 0.01 | Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mwfe9ed415_d5af_469c_a549_d8981f1eb01f => mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwc52aebc2_571c_4f96_84ee_0613ae73db89*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3*mwfe9ed415_d5af_469c_a549_d8981f1eb01f-mw9330e49a_b214_4807_b614_4241a4a12c43*mwd794c746_c826_4ba1_9e09_a9d1e122d925) |
mw1a6a8649_d7cb_4379_983a_cca2acac3112 = 2.5 | Reaction: mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw2badefa3_32e8_4b66_9e69_245d9ec74e33, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw1a6a8649_d7cb_4379_983a_cca2acac3112*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw541807fb_7d9f_4788_9f21_cc62846b5826 = 6.25E-5 | Reaction: mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a + mw29ba9e7c_6865_4817_8775_be2dbc29651e => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw541807fb_7d9f_4788_9f21_cc62846b5826*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a*mw29ba9e7c_6865_4817_8775_be2dbc29651e-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw2075d2cf_955e_4150_98b8_847103c53845) |
States:
Name | Description |
---|---|
mw8825a609 0983 4fb4 a264 e2f7e43d17b3 | [calcium(2+); Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Protein phosphatase 1 regulatory subunit 1B] |
mwf46d3666 f0f3 4f05 9603 d7e6bb69005e | [3',5'-cyclic AMP; cAMP-specific 3',5'-cyclic phosphodiesterase 4D] |
mw8e34c23f 1891 4dc9 8f97 dc2f12a1706c | [GTP; Guanine nucleotide-binding protein G(olf) subunit alpha] |
mw1041345b f015 436c 9eff 98211008aa1c | [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mw2f3e9c55 e57f 416e b4b1 cc49a26192c0 | [Protein phosphatase 1 regulatory subunit 1B] |
mw46dccec6 6f0f 40f6 a10c 2f34ae7a005a | [ATP] |
mw0b46978f b522 4cde 97f0 574cd7dbbae7 | [D(1A) dopamine receptor; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
mwb80e4fa1 4849 4ed5 b3b0 3e3025c61ad8 | [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
mw3e1a2fbf 37b1 490c 9528 6cb6bbf11b21 | [acetylcholine] |
mw24435476 9c30 4878 b26f 4b3c5a0685c6 | [Protein phosphatase 1 regulatory subunit 1B] |
mwfed0682b 39f1 4b09 94e8 c45a51744092 | [GDP; Guanine nucleotide-binding protein G(olf) subunit alpha] |
mw3d9e6efb 8e12 49c9 a87f e067914b951d | [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mw07c7392b 8d89 4b94 97c5 59f7e256b6f2 | [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw7086a13a 619e 4069 b163 d8a05fc55f42 | [ATP; Adenylate cyclase type 5] |
mwaf471bc1 f98a 4115 b0ee 45c189ea20b5 | [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1] |
mw619502c3 e319 4e29 a677 b2b5f74fc2cf | [calcium(2+); ATP; Adenylate cyclase type 5] |
mw56dff932 134c 4d88 a611 daad00623fd0 | [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw351f6cee 3e64 4b8e 8e60 24b1aca99a92 | [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
mwd794c746 c826 4ba1 9e09 a9d1e122d925 | [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw0a10f9cb 3f4b 4bfa ace9 0ecd2bd74b5e | [GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mwa2c44a01 28c9 4dbd b034 364f9b5b6cc3 | [GTP; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw9bcba6bc 9788 4f7f afb5 1c8f3b33c3d1 | [GDP; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw6e845d87 603e 4463 874d 866f554303df | [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mw081c9f7b 011e 440f 971d d0316d2a1e6c | [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
mwbae3bd11 0ab4 4587 a931 9c5dc5e777ba | [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha] |
mwde741b91 d5bf 44a9 ad45 404d7259d051 | [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Protein phosphatase 1 regulatory subunit 1B] |
mwe4e36b8e 18b8 4c76 bd46 13614b71da5c | [acetylcholine; Muscarinic acetylcholine receptor M4; Guanine nucleotide-binding protein G(i) subunit alpha-1; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
mw6b2f1c44 e0be 4406 bcef ad5061d519e4 | [dopamine; D(1A) dopamine receptor; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
totalActivePKA | [3',5'-cyclic AMP; cAMP-dependent protein kinase catalytic subunit alpha; cAMP-dependent protein kinase catalytic subunit beta; cAMP-dependent protein kinase type I-alpha regulatory subunit; cAMP-dependent protein kinase type I-beta regulatory subunit] |
mw1f3b8982 3b8c 42b6 8b0f 49b037cbda43 | [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mw522cacf1 5e61 4b95 8742 cf61cb824893 | [IPR006186; Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
mw4179e1ff 9035 4c67 a67c 099e25beb9b0 | [cAMP-dependent protein kinase catalytic subunit alpha; cAMP-dependent protein kinase catalytic subunit beta; Protein phosphatase 1 regulatory subunit 1B] |
BIOMD0000000636
— v0.0.1Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 Neuron)This model is describ…
Details
Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation. link: http://identifiers.org/pubmed/26468202
Parameters:
Name | Description |
---|---|
mwf633f298_303f_46d1_b644_ae07ae366f45 = 3.0 | Reaction: mw6e845d87_603e_4463_874d_866f554303df => mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwf633f298_303f_46d1_b644_ae07ae366f45*mw6e845d87_603e_4463_874d_866f554303df |
mwdad9965c_2334_481f_8544_f1a81385a28e = 0.005; mwc23d8bf6_2a60_4760_8bf5_c1bab432ab52 = 1.0 | Reaction: A2AR + mwbe974953_e869_4622_b4a8_745555c8d7fd => A2ARAdn, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwdad9965c_2334_481f_8544_f1a81385a28e*A2AR*mwbe974953_e869_4622_b4a8_745555c8d7fd-mwc23d8bf6_2a60_4760_8bf5_c1bab432ab52*A2ARAdn) |
mw515fcf69_b724_40d9_84ba_5f92d75ae5a7 = 1.5E-4 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw515fcf69_b724_40d9_84ba_5f92d75ae5a7*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw7df45520_98cc_4c0b_91a7_c6e7297de98a |
mw80292f32_fd53_4b5d_872a_e21c2d90c52a = 0.1; mwce0df80f_1563_453d_b33d_a88f6b2c93b7 = 200.0 | Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwf82770b9_766a_4c4e_851a_d76da19e8517 => mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw80292f32_fd53_4b5d_872a_e21c2d90c52a*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwf82770b9_766a_4c4e_851a_d76da19e8517-mwce0df80f_1563_453d_b33d_a88f6b2c93b7*mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2) |
mwfe873584_629a_46c8_aae9_fdacdb9ad266 = 0.1; mwf3c85708_890c_45d1_bcbc_fe90e9ca792f = 10.0 | Reaction: mwd1171b65_ed6c_4413_bf47_5ed80038a7bd + mwccd3a17c_e207_4663_9b16_327b78882497 => mw4855b1cd_d7bc_4072_9736_dca30bbe448d, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwfe873584_629a_46c8_aae9_fdacdb9ad266*mwd1171b65_ed6c_4413_bf47_5ed80038a7bd*mwccd3a17c_e207_4663_9b16_327b78882497-mwf3c85708_890c_45d1_bcbc_fe90e9ca792f*mw4855b1cd_d7bc_4072_9736_dca30bbe448d) |
mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9 = 2.8125E-5 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc |
mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79 = 0.1; mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296 = 2.0 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw3d9e6efb_8e12_49c9_a87f_e067914b951d => mw6e845d87_603e_4463_874d_866f554303df, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw3d9e6efb_8e12_49c9_a87f_e067914b951d-mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296*mw6e845d87_603e_4463_874d_866f554303df) |
mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d = 7.5E-5; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 | Reaction: mw7df45520_98cc_4c0b_91a7_c6e7297de98a + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d*mw7df45520_98cc_4c0b_91a7_c6e7297de98a*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw619502c3_e319_4e29_a677_b2b5f74fc2cf) |
mw009f9583_4e96_4672_ab71_0ef4b697aa6f = 6.4; mw2226fa14_2b95_45a6_8705_4b38073fc5f7 = 8.0E-4 | Reaction: mw522cacf1_5e61_4b95_8742_cf61cb824893 + mw1184c368_03fc_435a_9086_dc6ed3067935 => mw0459271f_3b39_40a4_948f_aed773482cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw2226fa14_2b95_45a6_8705_4b38073fc5f7*mw522cacf1_5e61_4b95_8742_cf61cb824893*mw1184c368_03fc_435a_9086_dc6ed3067935-mw009f9583_4e96_4672_ab71_0ef4b697aa6f*mw0459271f_3b39_40a4_948f_aed773482cfc) |
mw066c69e2_66da_4621_9180_bce71b7077c3 = 12.0; mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b = 200.0 | Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwd86ce0dc_7329_4b27_9de0_ee6bffee3083 => mwe4e36b8e_18b8_4c76_bd46_13614b71da5c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw066c69e2_66da_4621_9180_bce71b7077c3*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwd86ce0dc_7329_4b27_9de0_ee6bffee3083-mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c) |
mw1b9e5266_efac_4696_a213_80f9f83d948a = 9.0E-4; mwa27c20d8_b6ed_4617_a6f6_9af2752d3a33 = 2.0 | Reaction: mw32351ce4_eaaf_4827_8efa_342224548d8a + mw24435476_9c30_4878_b26f_4b3c5a0685c6 => mw0130a500_18e9_470f_9fac_70af44dc4a9e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1b9e5266_efac_4696_a213_80f9f83d948a*mw32351ce4_eaaf_4827_8efa_342224548d8a*mw24435476_9c30_4878_b26f_4b3c5a0685c6-mwa27c20d8_b6ed_4617_a6f6_9af2752d3a33*mw0130a500_18e9_470f_9fac_70af44dc4a9e) |
ModelValue_131 = 0.0; ModelValue_124 = 0.0; DAdip = 10.0; DApeak = 10.0; ModelValue_138 = 10.0 | Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 = ((1-ModelValue_124)-ModelValue_131)*ModelValue_138+ModelValue_124*DAdip+ModelValue_131*DApeak, Rate Law: missing |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a = 1.75E-4 | Reaction: mw2badefa3_32e8_4b66_9e69_245d9ec74e33 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw07c7392b_8d89_4b94_97c5_59f7e256b6f2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a*mw2badefa3_32e8_4b66_9e69_245d9ec74e33*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2) |
mw0dd72d64_80e1_4f76_a444_fd175dbfab0c = 30.0 | Reaction: A2ARAdnGolf => mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 + A2ARAdn + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw0dd72d64_80e1_4f76_a444_fd175dbfab0c*A2ARAdnGolf |
mwfcfb91ff_a495_41f9_bdff_fcef779112fd = 30.0 | Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwfcfb91ff_a495_41f9_bdff_fcef779112fd*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 |
mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb = 5.0 | Reaction: mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e |
mw034d8151_fae1_4738_b675_39c38a58118d = 0.022 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw42919ead_5972_4151_85ac_fcc88ca105a6 => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw034d8151_fae1_4738_b675_39c38a58118d*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw42919ead_5972_4151_85ac_fcc88ca105a6 |
mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9 = 2.0; mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2 = 0.13 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw1041345b_f015_436c_9eff_98211008aa1c => mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw1041345b_f015_436c_9eff_98211008aa1c-mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43) |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw72ceb3da_d538_4f25_8e69_f322eb0b5e57 = 0.00105 | Reaction: mwfe9ed415_d5af_469c_a549_d8981f1eb01f + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw166e3335_56c3_41ef_af0f_b583860991c1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw72ceb3da_d538_4f25_8e69_f322eb0b5e57*mwfe9ed415_d5af_469c_a549_d8981f1eb01f*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw166e3335_56c3_41ef_af0f_b583860991c1) |
mw88c9326a_fbe9_4dd8_aded_b5be3f012691 = 2.3 | Reaction: mwde741b91_d5bf_44a9_ad45_404d7259d051 => mw081c9f7b_011e_440f_971d_d0316d2a1e6c + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw88c9326a_fbe9_4dd8_aded_b5be3f012691*mwde741b91_d5bf_44a9_ad45_404d7259d051 |
mw65cae8fe_0eac_4792_88bf_2dfb441030e5 = 0.5 | Reaction: mw619502c3_e319_4e29_a677_b2b5f74fc2cf => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw65cae8fe_0eac_4792_88bf_2dfb441030e5*mw619502c3_e319_4e29_a677_b2b5f74fc2cf |
mwac1bc66c_2623_47e6_a76d_c1629d962be5 = 10.0 | Reaction: mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43 => mw1041345b_f015_436c_9eff_98211008aa1c + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwac1bc66c_2623_47e6_a76d_c1629d962be5*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43 |
mwe737a297_e5be_46ed_af75_ccc7428c3977 = 0.001; mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b = 0.9 | Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mwccd3a17c_e207_4663_9b16_327b78882497 => mw7df45520_98cc_4c0b_91a7_c6e7297de98a, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwe737a297_e5be_46ed_af75_ccc7428c3977*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mwccd3a17c_e207_4663_9b16_327b78882497-mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b*mw7df45520_98cc_4c0b_91a7_c6e7297de98a) |
mw2f090a45_946b_4587_a3e3_b29f3bb5c6ae = 100.0 | Reaction: mwfed0682b_39f1_4b09_94e8_c45a51744092 + mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 => mw351f6cee_3e64_4b8e_8e60_24b1aca99a92, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw2f090a45_946b_4587_a3e3_b29f3bb5c6ae*mwfed0682b_39f1_4b09_94e8_c45a51744092*mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 |
mwcd307ee9_33da_4303_9c28_644ad2d1630c = 0.1; mw0a255671_d9ca_4384_a153_ce17e1111453 = 0.2 | Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfe9ed415_d5af_469c_a549_d8981f1eb01f, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0a255671_d9ca_4384_a153_ce17e1111453*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c-mwcd307ee9_33da_4303_9c28_644ad2d1630c*mwfe9ed415_d5af_469c_a549_d8981f1eb01f) |
mw68039b16_b516_4fba_bedd_d4bbc1a23a02 = 9.1; mw894b221b_266d_4277_ac01_83579ed103e6 = 0.006 | Reaction: mw4b358131_010c_4545_ac4a_13a6c8bc34c4 + mwccd3a17c_e207_4663_9b16_327b78882497 => mw65a14789_ffcf_4bfd_9d53_d2eb2f4d0896, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw894b221b_266d_4277_ac01_83579ed103e6*mw4b358131_010c_4545_ac4a_13a6c8bc34c4*mwccd3a17c_e207_4663_9b16_327b78882497-mw68039b16_b516_4fba_bedd_d4bbc1a23a02*mw65a14789_ffcf_4bfd_9d53_d2eb2f4d0896) |
mwd1b16e73_4fcb_4e4c_9804_3137259ba749 = 1.0E-6; mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2 = 9.0 | Reaction: mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw1c97b02d_169a_4eb8_bc84_1be57c51a255 => mw1041345b_f015_436c_9eff_98211008aa1c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwd1b16e73_4fcb_4e4c_9804_3137259ba749*mw3d9e6efb_8e12_49c9_a87f_e067914b951d*mw1c97b02d_169a_4eb8_bc84_1be57c51a255^2-mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2*mw1041345b_f015_436c_9eff_98211008aa1c) |
mw8186cb1d_66c4_4855_bcbb_82d75173ae8a = 20.0 | Reaction: mw166e3335_56c3_41ef_af0f_b583860991c1 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwfe9ed415_d5af_469c_a549_d8981f1eb01f, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw8186cb1d_66c4_4855_bcbb_82d75173ae8a*mw166e3335_56c3_41ef_af0f_b583860991c1 |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4 = 5.5E-4 | Reaction: mw42919ead_5972_4151_85ac_fcc88ca105a6 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4*mw42919ead_5972_4151_85ac_fcc88ca105a6*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba) |
mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3 = 10.0 | Reaction: mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw42919ead_5972_4151_85ac_fcc88ca105a6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba |
mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39 = 0.002; mwc911f28c_b62f_4269_84ed_d852f6da24f9 = 0.01 | Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c + mw2075d2cf_955e_4150_98b8_847103c53845 => mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c*mw2075d2cf_955e_4150_98b8_847103c53845-mwc911f28c_b62f_4269_84ed_d852f6da24f9*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e) |
mw6bf18344_b899_4a62_ac8d_5f8bdd4bbe2f = 8.0 | Reaction: mw3a3e53fb_bbbf_4433_9f75_a12610dbc312 => mw9417144e_14b1_40d9_bd4b_ccd9f4714305 + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw6bf18344_b899_4a62_ac8d_5f8bdd4bbe2f*mw3a3e53fb_bbbf_4433_9f75_a12610dbc312 |
mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5 = 30.0 | Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c |
mwa466eec8_9bc0_44d5_8027_d5925b378429 = 1.0; mw448bd49f_40ad_46c9_81f6_3494057dc37d = 0.005 | Reaction: A2AR + mw351f6cee_3e64_4b8e_8e60_24b1aca99a92 => mw0b46978f_b522_4cde_97f0_574cd7dbbae7, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw448bd49f_40ad_46c9_81f6_3494057dc37d*A2AR*mw351f6cee_3e64_4b8e_8e60_24b1aca99a92-mwa466eec8_9bc0_44d5_8027_d5925b378429*mw0b46978f_b522_4cde_97f0_574cd7dbbae7) |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb93138ce_a80b_4b26_b927_6b4a00651b64 = 3.0E-4 | Reaction: mwd794c746_c826_4ba1_9e09_a9d1e122d925 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb93138ce_a80b_4b26_b927_6b4a00651b64*mwd794c746_c826_4ba1_9e09_a9d1e122d925*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e) |
mwed967767_31e4_4e9e_8117_5372f9f4f79a = 3.0 | Reaction: mw0130a500_18e9_470f_9fac_70af44dc4a9e => mw32351ce4_eaaf_4827_8efa_342224548d8a + mw1184c368_03fc_435a_9086_dc6ed3067935, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwed967767_31e4_4e9e_8117_5372f9f4f79a*mw0130a500_18e9_470f_9fac_70af44dc4a9e |
mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba = 0.25 | Reaction: mwfe9ed415_d5af_469c_a549_d8981f1eb01f => mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba*mwfe9ed415_d5af_469c_a549_d8981f1eb01f |
mwcabc0868_2435_4850_964b_e3ddee39f5ad = 30.0 | Reaction: mw56dff932_134c_4d88_a611_daad00623fd0 => mw619502c3_e319_4e29_a677_b2b5f74fc2cf + mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwcabc0868_2435_4850_964b_e3ddee39f5ad*mw56dff932_134c_4d88_a611_daad00623fd0 |
mwe4474191_0c92_406c_a6f5_4a167f541d36 = 0.25 | Reaction: mw2075d2cf_955e_4150_98b8_847103c53845 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw29ba9e7c_6865_4817_8775_be2dbc29651e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwe4474191_0c92_406c_a6f5_4a167f541d36*mw2075d2cf_955e_4150_98b8_847103c53845 |
mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761 = 5.0; mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6 = 50.0 | Reaction: mw619502c3_e319_4e29_a677_b2b5f74fc2cf + mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6*mw619502c3_e319_4e29_a677_b2b5f74fc2cf*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3-mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761*mw56dff932_134c_4d88_a611_daad00623fd0) |
mwd05b4199_53ad_4807_9a8c_d93ce35be857 = 60.0 | Reaction: mwe4e36b8e_18b8_4c76_bd46_13614b71da5c => mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2 + mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwd05b4199_53ad_4807_9a8c_d93ce35be857*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c |
mw17d612a2_c9d5_4251_8122_5f037fc630e3 = 0.00105 | Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw29ba9e7c_6865_4817_8775_be2dbc29651e => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw17d612a2_c9d5_4251_8122_5f037fc630e3*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw29ba9e7c_6865_4817_8775_be2dbc29651e |
mw9c2302f8_3d47_4247_a338_a02c53fc5331 = 1.0E-4; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 | Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw7086a13a_619e_4069_b163_d8a05fc55f42, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9c2302f8_3d47_4247_a338_a02c53fc5331*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw7086a13a_619e_4069_b163_d8a05fc55f42) |
mwffa5af7e_9155_4942_9424_cd94ac5018ed = 0.055; mw0060906c_a035_468c_aa1c_130959bcf15a = 200.0 | Reaction: mwf82770b9_766a_4c4e_851a_d76da19e8517 + mwd8ea533a_c66e_4de4_8c5c_0d4201d8c8a2 => mwd86ce0dc_7329_4b27_9de0_ee6bffee3083, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwffa5af7e_9155_4942_9424_cd94ac5018ed*mwf82770b9_766a_4c4e_851a_d76da19e8517*mwd8ea533a_c66e_4de4_8c5c_0d4201d8c8a2-mw0060906c_a035_468c_aa1c_130959bcf15a*mwd86ce0dc_7329_4b27_9de0_ee6bffee3083) |
mw2037ae7c_b1dc_4517_a61c_88a1f2bdcd12 = 1.0; mw49e66b9c_64bd_428a_9090_15e4132e9781 = 3.7E-4 | Reaction: mw1184c368_03fc_435a_9086_dc6ed3067935 + mw68d3f409_9462_4515_8c07_bc105fa0eaf1 => mwb320746f_6a8c_4c8b_ae55_23db454339d8, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw49e66b9c_64bd_428a_9090_15e4132e9781*mw1184c368_03fc_435a_9086_dc6ed3067935*mw68d3f409_9462_4515_8c07_bc105fa0eaf1-mw2037ae7c_b1dc_4517_a61c_88a1f2bdcd12*mwb320746f_6a8c_4c8b_ae55_23db454339d8) |
mwc728d91d_7616_43db_bd1d_55e49e9c026a = 0.125 | Reaction: mw56dff932_134c_4d88_a611_daad00623fd0 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwc728d91d_7616_43db_bd1d_55e49e9c026a*mw56dff932_134c_4d88_a611_daad00623fd0 |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwa1bc2233_5bb9_4135_88ed_bb51640faec8 = 5.625E-5 | Reaction: mwed1b3928_8d78_44d1_aee7_9d11d6437cfc + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwa1bc2233_5bb9_4135_88ed_bb51640faec8*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw56dff932_134c_4d88_a611_daad00623fd0) |
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw541807fb_7d9f_4788_9f21_cc62846b5826 = 6.25E-5 | Reaction: mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a + mw29ba9e7c_6865_4817_8775_be2dbc29651e => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw541807fb_7d9f_4788_9f21_cc62846b5826*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a*mw29ba9e7c_6865_4817_8775_be2dbc29651e-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw2075d2cf_955e_4150_98b8_847103c53845) |
mwc52aebc2_571c_4f96_84ee_0613ae73db89 = 0.01; mw9330e49a_b214_4807_b614_4241a4a12c43 = 0.01 | Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mwfe9ed415_d5af_469c_a549_d8981f1eb01f => mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwc52aebc2_571c_4f96_84ee_0613ae73db89*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3*mwfe9ed415_d5af_469c_a549_d8981f1eb01f-mw9330e49a_b214_4807_b614_4241a4a12c43*mwd794c746_c826_4ba1_9e09_a9d1e122d925) |
mw1a6a8649_d7cb_4379_983a_cca2acac3112 = 2.5 | Reaction: mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw2badefa3_32e8_4b66_9e69_245d9ec74e33, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw1a6a8649_d7cb_4379_983a_cca2acac3112*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 |
mw3a56b314_299f_48d2_a179_97bf6a30f38f = 0.006 | Reaction: mw9417144e_14b1_40d9_bd4b_ccd9f4714305 => mw081c9f7b_011e_440f_971d_d0316d2a1e6c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw3a56b314_299f_48d2_a179_97bf6a30f38f*mw9417144e_14b1_40d9_bd4b_ccd9f4714305 |
States:
Name | Description |
---|---|
mw1041345b f015 436c 9eff 98211008aa1c | [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mw1c97b02d 169a 4eb8 bc84 1be57c51a255 | [3',5'-cyclic AMP] |
mwed1b3928 8d78 44d1 aee7 9d11d6437cfc | [calcium(2+); GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw46dccec6 6f0f 40f6 a10c 2f34ae7a005a | [ATP] |
mw3e1a2fbf 37b1 490c 9528 6cb6bbf11b21 | [dopamine] |
mw2badefa3 32e8 4b66 9e69 245d9ec74e33 | [calcium(2+); GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mwd86ce0dc 7329 4b27 9de0 ee6bffee3083 | [D(2) dopamine receptor; Guanine nucleotide-binding protein G(i) subunit alpha-1; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
A2AR | [Adenosine receptor A2a] |
mw3d9e6efb 8e12 49c9 a87f e067914b951d | [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mwfed0682b 39f1 4b09 94e8 c45a51744092 | [GDP; Guanine nucleotide-binding protein G(olf) subunit alpha] |
mw07c7392b 8d89 4b94 97c5 59f7e256b6f2 | [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mwccd3a17c e207 4663 9b16 327b78882497 | [calcium(2+)] |
mw32351ce4 eaaf 4827 8efa 342224548d8a | [Cyclin-dependent-like kinase 5] |
mw619502c3 e319 4e29 a677 b2b5f74fc2cf | [calcium(2+); ATP; Adenylate cyclase type 5] |
mw56dff932 134c 4d88 a611 daad00623fd0 | [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw9417144e 14b1 40d9 bd4b ccd9f4714305 | [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
mw0a10f9cb 3f4b 4bfa ace9 0ecd2bd74b5e | [GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw724f1afe 8032 40ae 96ca 808ab7b8b943 | [Adenylate cyclase type 5] |
A2ARAdnGolf | [adenosine; Adenosine receptor A2a; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2] |
mwa2c44a01 28c9 4dbd b034 364f9b5b6cc3 | [GTP; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw6e845d87 603e 4463 874d 866f554303df | [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
mw9bcba6bc 9788 4f7f afb5 1c8f3b33c3d1 | [GDP; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
A2ARAdn | [adenosine; Adenosine receptor A2a] |
mw29ba9e7c 6865 4817 8775 be2dbc29651e | [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw081c9f7b 011e 440f 971d d0316d2a1e6c | [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform] |
mwd794c746 c826 4ba1 9e09 a9d1e122d925 | [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1] |
mw7df45520 98cc 4c0b 91a7 c6e7297de98a | [calcium(2+); Adenylate cyclase type 5] |
mw0130a500 18e9 470f 9fac 70af44dc4a9e | [Cyclin-dependent-like kinase 5; Protein phosphatase 1 regulatory subunit 1B] |
mwfe9ed415 d5af 469c a549 d8981f1eb01f | [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha] |
mwf82770b9 766a 4c4e 851a d76da19e8517 | [D(2) dopamine receptor] |
mw42919ead 5972 4151 85ac fcc88ca105a6 | [calcium(2+); GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha] |
mw1184c368 03fc 435a 9086 dc6ed3067935 | [Protein phosphatase 1 regulatory subunit 1B] |
mw1f3b8982 3b8c 42b6 8b0f 49b037cbda43 | [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A] |
BIOMD0000000251
— v0.0.1This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of th…
Details
Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands. link: http://identifiers.org/pubmed/20493519
Parameters:
Name | Description |
---|---|
tau1 = 3.07; L = 1.0; K1 = 1.09 | Reaction: => x1, Rate Law: compartment*((-x1)/tau1+K1*L/tau1) |
k6=0.13; k7 = 0.5; n=1.1 | Reaction: => cFOSp; ppERKn, pRSKn, Rate Law: compartment*((ppERKn*pRSKn)^n/(k6^n+(ppERKn*pRSKn)^n)-k7*cFOSp) |
k8=0.08; k7 = 0.5 | Reaction: => cFOSm; cFOSp, Rate Law: compartment*(k7*cFOSp-k8*cFOSm) |
k2=50.0; k1=15.0; k3=14.0 | Reaction: => ppERKn; ppERKc, DUSP, Rate Law: compartment*((k1*ppERKc-k2*ppERKn)-k3*DUSP*ppERKn) |
tau2 = 472.0; L = 1.0; K2 = 2.89 | Reaction: => x2, Rate Law: compartment*((-x2)/tau2+K2*L/tau2) |
k=1.0 | Reaction: => DUSP; ppERKn, Rate Law: compartment*k*ppERKn |
k4=0.1; k5=0.15 | Reaction: => pRSKn; ppERKn, Rate Law: compartment*(k4*ppERKn-k5*pRSKn) |
k13 = 0.06; k11 = 0.11; k12=0.001 | Reaction: => pcFOS; cFOS, ppERKc, Rate Law: compartment*((k11*cFOS*ppERKc-k12*pcFOS)-k13*pcFOS) |
k9=0.3; k13 = 0.06; k10=0.3; k11 = 0.11 | Reaction: => cFOS; cFOSm, ppERKc, pcFOS, Rate Law: compartment*(((k9*cFOSm-k10*cFOS)-k11*cFOS*ppERKc)+k13*pcFOS) |
States:
Name | Description |
---|---|
ppERKn | [Phosphoprotein; Mitogen-activated protein kinase 1; Mitogen-activated protein kinase 3; p-ERK1/2/5 [nucleoplasm]] |
cFOSp | [FOS [nucleoplasm]; FOS, AP-1, C-FOS, p55] |
x1 | x1 |
cFOS | [Proto-oncogene c-Fos; FOS [nucleoplasm]] |
pcFOS | [Proto-oncogene c-Fos; p-T325,T331,S362,S374-FOS [nucleoplasm]] |
cFOSm | [FOS [nucleoplasm]; Proto-oncogene c-Fos] |
x2 | x2 |
DUSP | [DUSP, MKP; IPR014393; ERK-specific DUSP [nucleoplasm]; Dual specificity protein phosphatase 8; Dual specificity protein phosphatase 10; Dual specificity protein phosphatase 5; Dual specificity protein phosphatase 4; Dual specificity protein phosphatase 2; Dual specificity protein phosphatase 1] |
pRSKn | [Ribosomal protein S6 kinase [nucleoplasm]] |
ppERKc | [Phosphoprotein; p-T185,Y187-MAPK1 [cytosol]; p-T202,Y204-MAPK3 [cytosol]] |
BIOMD0000000250
— v0.0.1This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimu…
Details
Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands. link: http://identifiers.org/pubmed/20493519
Parameters:
Name | Description |
---|---|
V14 = 5.636949216; K14 = 34180.48 | Reaction: DUSP_c => pDUSP_c; ppERK_c, Rate Law: cytoplasm*V14*ppERK_c*DUSP_c/(K14+DUSP_c) |
K4 = 60.0; K3 = 160.0; V4 = 0.648 | Reaction: ppERK_c => pERK_c; pERK_c, Rate Law: cytoplasm*V4*ppERK_c/(K4*(1+pERK_c/K3)+ppERK_c) |
p50 = 26.59483436 | Reaction: DUSP_n_pERK_n => DUSP_n + ERK_n, Rate Law: nucleus*p50*DUSP_n_pERK_n |
p38 = 2.57E-4 | Reaction: c_FOS_c =>, Rate Law: cytoplasm*p38*c_FOS_c |
m51 = 9.544308421; p51 = 0.01646825 | Reaction: DUSP_n + ERK_n => DUSP_n_ERK_n, Rate Law: nucleus*(p51*DUSP_n*ERK_n-m51*DUSP_n_ERK_n) |
p49 = 0.314470502; m49 = 2.335459127 | Reaction: DUSP_n + pERK_n => DUSP_n_pERK_n, Rate Law: nucleus*(p49*DUSP_n*pERK_n-m49*DUSP_n_pERK_n) |
KexERKP = 0.018; Vc = 940.0; KimERKP = 0.012; Vn = 220.0 | Reaction: pERK_c => pERK_n, Rate Law: KimERKP*Vc*pERK_c-KexERKP*Vn*pERK_n |
KimRSKP = 0.025925065; KexRSKP = 0.129803956; Vc = 940.0; Vn = 220.0 | Reaction: pRSK_c => pRSK_n, Rate Law: KimRSKP*Vc*pRSK_c-KexRSKP*Vn*pRSK_n |
V115 = 13.74244; K115 = 2122.045 | Reaction: pMEK => MEK, Rate Law: cytoplasm*V115*pMEK/(K115+pMEK) |
V106 = 0.109304; K106 = 606.871 | Reaction: RsD => RsT; HRG, Rate Law: cytoplasm*V106*HRG*RsD/(K106+RsD) |
p47 = 0.001670815; m47 = 15.80783969 | Reaction: DUSP_n + ppERK_n => DUSP_n_ppERK_n, Rate Law: nucleus*(p47*DUSP_n*ppERK_n-m47*DUSP_n_ppERK_n) |
p58 = 2.70488E-4; Vn = 220.0 | Reaction: PreFmRNA => FmRNA, Rate Law: p58*Vn*PreFmRNA |
K102 = 237.2001; V102 = 0.09858154 | Reaction: A1_2 => A1, Rate Law: cytoplasm*V102*A1_2/(K102+A1_2) |
V108 = 0.03436149; K108 = 11.5048 | Reaction: RsT => RsD; A2_2, Rate Law: cytoplasm*V108*A2_2*RsT/(K108+RsT) |
p11 = 1.26129E-4; Vn = 220.0 | Reaction: PreDUSPmRNA => DUSPmRNA, Rate Law: p11*Vn*PreDUSPmRNA |
K31 = 185.9760682; V31 = 0.655214248; KF31 = 0.013844393; nF31 = 2.800340453; n31 = 1.988003164 | Reaction: => PreFOSmRNA; pCREB_n, pElk1_n, Fn, Rate Law: nucleus*V31*(pCREB_n*pElk1_n)^n31/(K31^n31+(pCREB_n*pElk1_n)^n31+(Fn/KF31)^nF31) |
p32 = 0.003284434; Vn = 220.0 | Reaction: PreFOSmRNA => c_FOSmRNA, Rate Law: p32*Vn*PreFOSmRNA |
p45 = 2.57E-4 | Reaction: FOSn =>, Rate Law: nucleus*p45*FOSn |
KimERK = 0.012; KexERK = 0.018; Vc = 940.0; Vn = 220.0 | Reaction: ERK_c => ERK_n, Rate Law: KimERK*Vc*ERK_c-KexERK*Vn*ERK_n |
K105 = 1.027895; V105 = 0.05393704 | Reaction: RsD => RsT; EGF, Rate Law: cytoplasm*V105*EGF*RsD/(K105+RsD) |
K107 = 424.6884; V107 = 5.291093 | Reaction: RsT => RsD; A1_2, Rate Law: cytoplasm*V107*A1_2*RsT/(K107+RsT) |
K57 = 0.637490056; V57 = 1.026834758; n57 = 3.584464176 | Reaction: => PreFmRNA; FOSn_2, Rate Law: nucleus*V57*FOSn_2^n57/(K57^n57+FOSn_2^n57) |
p33 = 6.01234209304622E-4 | Reaction: c_FOSmRNA =>, Rate Law: cytoplasm*p33*c_FOSmRNA |
p55 = 26.59483436 | Reaction: pDUSP_n_pERK_n => pDUSP_n + ERK_n, Rate Law: nucleus*p55*pDUSP_n_pERK_n |
KexDUSP = 0.070467899; Vc = 940.0; KimDUSP = 0.024269764; Vn = 220.0 | Reaction: DUSP_c => DUSP_n, Rate Law: KimDUSP*Vc*DUSP_c-KexDUSP*Vn*DUSP_n |
KimFOS = 0.54528521; Vc = 940.0; KexFOS = 0.133249762; Vn = 220.0 | Reaction: c_FOS_c => FOSn, Rate Law: KimFOS*Vc*c_FOS_c-KexFOS*Vn*FOSn |
V29 = 0.518529841; K29 = 21312.69109 | Reaction: Elk1_n => pElk1_n; ppERK_n, Rate Law: nucleus*V29*ppERK_n*Elk1_n/(K29+Elk1_n) |
KimFOSP = 0.54528521; KexFOSP = 0.133249762; Vc = 940.0; Vn = 220.0 | Reaction: pc_FOS_c => FOSn_2, Rate Law: KimFOSP*Vc*pc_FOS_c-KexFOSP*Vn*FOSn_2 |
KimERKPP = 0.011; KexERKPP = 0.013; Vc = 940.0; Vn = 220.0 | Reaction: ppERK_c => ppERK_n, Rate Law: KimERKPP*Vc*ppERK_c-KexERKPP*Vn*ppERK_n |
p59 = 0.001443889 | Reaction: FmRNA =>, Rate Law: cytoplasm*p59*FmRNA |
V112 = 0.8850982; K112 = 4665.217 | Reaction: Kin_2 => Kin; A3_2, Rate Law: cytoplasm*V112*A3_2*Kin_2/(K112+Kin_2) |
m54 = 2.335459127; p54 = 0.314470502 | Reaction: pDUSP_n + pERK_n => pDUSP_n_pERK_n, Rate Law: nucleus*(p54*pDUSP_n*pERK_n-m54*pDUSP_n_pERK_n) |
p23 = 4.81E-5 | Reaction: pDUSP_n =>, Rate Law: nucleus*p23*pDUSP_n |
V27 = 19.23118154; K27 = 441.5834425 | Reaction: CREB_n => pCREB_n; pRSK_n, Rate Law: nucleus*V27*pRSK_n*CREB_n/(K27+CREB_n) |
K111 = 858.3423; V111 = 0.02487469 | Reaction: Kin => Kin_2; HRG, Rate Law: cytoplasm*V111*HRG*Kin/(K111+Kin) |
K20 = 735598.6967; V20 = 0.157678678 | Reaction: DUSP_n => pDUSP_n; ppERK_n, Rate Law: nucleus*V20*ppERK_n*DUSP_n/(K20+DUSP_n) |
p63 = 4.13466150826031E-5 | Reaction: Fn =>, Rate Law: nucleus*cytoplasm*p63*Fn/nucleus |
m56 = 9.544308421; p56 = 0.01646825 | Reaction: pDUSP_n + ERK_n => pDUSP_n_ERK_n, Rate Law: nucleus*(p56*pDUSP_n*ERK_n-m56*pDUSP_n_ERK_n) |
K104 = 4046.71; V104 = 4.635749 | Reaction: A2_2 => A2, Rate Law: cytoplasm*V104*A2_2/(K104+A2_2) |
V6 = 19.4987234631759; K6 = 29.9407371620698; K5 = 29.94073716 | Reaction: ppERK_n => pERK_n; pERK_n, Rate Law: nucleus*V6*ppERK_n/(K6*(1+pERK_n/K5)+ppERK_n) |
K44 = 0.051168202; V44 = 0.078344305 | Reaction: FOSn_2 => FOSn, Rate Law: nucleus*V44*FOSn_2/(K44+FOSn_2) |
p53 = 0.686020478 | Reaction: pDUSP_n_ppERK_n => pDUSP_n + pERK_n, Rate Law: nucleus*p53*pDUSP_n_ppERK_n |
K103 = 1334.132; V103 = 0.3573399 | Reaction: A2 => A2_2; HRG, Rate Law: cytoplasm*V103*HRG*A2/(K103+A2) |
p60 = 0.002448164 | Reaction: => F; FmRNA, Rate Law: cytoplasm*p60*FmRNA |
p61 = 3.49860901414122E-5 | Reaction: F =>, Rate Law: cytoplasm*p61*F |
p46 = 4.81E-5 | Reaction: FOSn_2 =>, Rate Law: nucleus*p46*FOSn_2 |
p12 = 0.007875765 | Reaction: DUSPmRNA =>, Rate Law: cytoplasm*p12*DUSPmRNA |
V43 = 0.076717457; K43 = 1157.116021 | Reaction: FOSn => FOSn_2; pRSK_n, Rate Law: nucleus*V43*pRSK_n*FOSn/(K43+FOSn) |
K114 = 7.774197; V114 = 0.03957055 | Reaction: MEK => pMEK; Kin_2, Rate Law: cytoplasm*V114*Kin_2*MEK/(K114+MEK) |
V113 = 0.05377297; K113 = 20.50809 | Reaction: MEK => pMEK; RsT, Rate Law: cytoplasm*V113*RsT*MEK/(K113+MEK) |
V110 = 0.08258693; K110 = 425.5268 | Reaction: A3_2 => A3, Rate Law: cytoplasm*V110*A3_2/(K110+A3_2) |
V42 = 0.909968714; K42 = 3992.061328 | Reaction: FOSn => FOSn_2; ppERK_n, Rate Law: nucleus*V42*ppERK_n*FOSn/(K42+FOSn) |
p48 = 0.686020478 | Reaction: DUSP_n_ppERK_n => DUSP_n + pERK_n, Rate Law: nucleus*p48*DUSP_n_ppERK_n |
p52 = 0.001670815; m52 = 15.80783969 | Reaction: pDUSP_n + ppERK_n => pDUSP_n_ppERK_n, Rate Law: nucleus*(p52*pDUSP_n*ppERK_n-m52*pDUSP_n_ppERK_n) |
K30 = 15.04396629; V30 = 13.79479021 | Reaction: pElk1_n => Elk1_n, Rate Law: nucleus*V30*pElk1_n/(K30+pElk1_n) |
V109 = 0.1374307; K109 = 7424.816 | Reaction: A3 => A3_2; HRG, Rate Law: cytoplasm*V109*HRG*A3/(K109+A3) |
V101 = 0.01807448; K101 = 3475.168 | Reaction: A1 => A1_2; EGF, Rate Law: cytoplasm*V101*EGF*A1/(K101+A1) |
K2 = 350.0; V2 = 0.22; Fct = 0.7485; K1 = 307.041525298866 | Reaction: pERK_c => ppERK_c; pMEK, ERK_c, Rate Law: cytoplasm*V2*Fct*pMEK*pERK_c/(K2*(1+ERK_c/K1)+pERK_c) |
p34 = 7.64816282169636E-5 | Reaction: => c_FOS_c; c_FOSmRNA, Rate Law: cytoplasm*p34*c_FOSmRNA |
K2 = 350.0; V1 = 0.342848369838443; Fct = 0.7485; K1 = 307.041525298866 | Reaction: ERK_c => pERK_c; pMEK, pERK_c, Rate Law: cytoplasm*V1*Fct*pMEK*ERK_c/(K1*(1+pERK_c/K2)+ERK_c) |
V21 = 0.005648117; K21 = 387.8377182 | Reaction: pDUSP_n => DUSP_n, Rate Law: nucleus*V21*pDUSP_n/(K21+pDUSP_n) |
p13 = 0.001245747 | Reaction: => DUSP_c; DUSPmRNA, Rate Law: cytoplasm*p13*DUSPmRNA |
KimF = 0.019898797; KexF = 0.396950616; Vc = 940.0; Vn = 220.0 | Reaction: F => Fn, Rate Law: KimF*Vc*F-KexF*Vn*Fn |
KimDUSPP = 0.024269764; KexDUSPP = 0.070467899; Vc = 940.0; Vn = 220.0 | Reaction: pDUSP_c => pDUSP_n, Rate Law: KimDUSPP*Vc*pDUSP_c-KexDUSPP*Vn*pDUSP_n |
V10 = 29.24109258; n10 = 3.970849295; K10 = 169.0473748 | Reaction: => PreDUSPmRNA; ppERK_n, Rate Law: nucleus*V10*ppERK_n^n10/(K10^n10+ppERK_n^n10) |
K4 = 60.0; K3 = 160.0; V3 = 0.72 | Reaction: pERK_c => ERK_c; ppERK_c, Rate Law: cytoplasm*V3*pERK_c/(K3*(1+ppERK_c/K4)+pERK_c) |
States:
Name | Description |
---|---|
PreFmRNA | PreFmRNA |
A1 2 | A1_2 |
RsT | RsT |
A2 2 | A2_2 |
RsD | RsD |
Elk1 n | Elk1_n |
DUSP n | DUSP_n |
pDUSP n | pDUSP_n |
pDUSP n ERK n | pDUSP_n_ERK_n |
pERK c | pERK_c |
Fn | Fn |
DUSP c | DUSP_c |
pElk1 n | pElk1_n |
MEK | MEK |
pRSK c | pRSK_c |
pRSK n | pRSK_n |
pERK n | pERK_n |
A3 | A3 |
A1 | A1 |
FOSn | FOSn |
ppERK n | ppERK_n |
A3 2 | A3_2 |
CREB n | CREB_n |
Kin | Kin |
FmRNA | FmRNA |
ERK c | ERK_c |
DUSP n pERK n | DUSP_n_pERK_n |
A2 | A2 |
c FOS c | c_FOS_c |
DUSPmRNA | DUSPmRNA |
pMEK | pMEK |
pDUSP n pERK n | pDUSP_n_pERK_n |
c FOSmRNA | c_FOSmRNA |
FOSn 2 | FOSn_2 |
DUSP n ppERK n | DUSP_n_ppERK_n |
ppERK c | ppERK_c |
PreFOSmRNA | PreFOSmRNA |
pDUSP n ppERK n | pDUSP_n_ppERK_n |
PreDUSPmRNA | PreDUSPmRNA |
ERK n | ERK_n |
pCREB n | pCREB_n |
F | F |
Kin 2 | Kin_2 |
MODEL1101170000
— v0.0.1This is an SBML version of the model described in: **A kinetic model of dopamine- and calcium-dependent striatal synapti…
Details
Corticostriatal synapse plasticity of medium spiny neurons is regulated by glutamate input from the cortex and dopamine input from the substantia nigra. While cortical stimulation alone results in long-term depression (LTD), the combination with dopamine switches LTD to long-term potentiation (LTP), which is known as dopamine-dependent plasticity. LTP is also induced by cortical stimulation in magnesium-free solution, which leads to massive calcium influx through NMDA-type receptors and is regarded as calcium-dependent plasticity. Signaling cascades in the corticostriatal spines are currently under investigation. However, because of the existence of multiple excitatory and inhibitory pathways with loops, the mechanisms regulating the two types of plasticity remain poorly understood. A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. Calcium input modulated this loop through the PP2B (phosphatase 2B)-CK1 (casein kinase 1)-Cdk5 (cyclin-dependent kinase 5)-Thr75 pathway and PP2A, whereas calcium and dopamine input activated the loop via PKA activation by cyclic AMP (cAMP). The positive feedback loop displayed robust bi-stable responses following changes in the reaction parameters. Increased basal dopamine levels disrupted this dopamine-dependent plasticity. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction. link: http://identifiers.org/pubmed/20169176
MODEL1812040002
— v0.0.1the model depicts a unique endemic equilibrium with a transcritical bifurcation when the basic reproductive number is un…
Details
HIV-infected individuals are increasingly becoming susceptible to liver disease and, hence, liver-related mortality is on a rise. The presence of CD4+ in the liver and the presence of C-X-C chemokine receptor type 4 (CXCR4) on human hepatocytes provide a conducive environment for HIV invasion. In this study, a mathematical model is used to analyse the dynamics of HIV in the liver with the aim of investigating the existence of liver enzyme elevation in HIV mono-infected individuals. In the presence of HIV-specific cytotoxic T-lymphocytes, the model depicts a unique endemic equilibrium with a transcritical bifurcation when the basic reproductive number is unity. Results of the study show that the level of liver enzyme alanine aminotransferase (ALT) increases with increase in the rate of hepatocytes production. Numerical simulations reveal significant elevation of alanine aminotransferase with increase in viral load. The findings presuppose that while liver damage in HIV infection has mostly been associated with HIV/HBV coinfection and use of antiretroviral therapy (ART), it is possible to have liver damage solely with HIV infection. link: http://identifiers.org/pubmed/23291466
MODEL2001090002
— v0.0.1<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1…
Details
B cell chronic lymphocytic leukemia (B-CLL) is known to havesubstantial clinical heterogeneity. There is no cure, but treatments allow fordisease management. However, the wide range of clinical courses experiencedby B-CLL patients makes prognosis and hence treatment a significant chal-lenge. In an attempt to study disease progression across different patients viaa unified yet flexible approach, we present a mathematical model of B-CLLwith immune response, that can capture both rapid and slow disease progres-sion. This model includes four different cell populations in the peripheral bloodof humans: B-CLL cells, NK cells, cytotoxic T cells and helper T cells. Weanalyze existing data in the medical literature, determine ranges of values forparameters of the model, and compare our model outcomes to clinical patientdata. The goal of this work is to provide a tool that may shed light on factorsaffecting the course of disease progression in patients. This modeling tool canserve as a foundation upon which future treatments can be based. link: http://identifiers.org/doi/10.3934/dcdsb.2013.18.1053
MODEL1808210002
— v0.0.1Mathematical model of blood coagulation factor alpha-thrombin and conversion of fibrinogen to fibrin with ATIII inhibiti…
Details
In this study we report a kinetic model for the alpha-thrombin-catalyzed production of fibrin I and fibrin II at pH 7.4, 37 degrees C, gamma/2 0.17. The fibrin is produced by the action of human alpha-thrombin on plasma levels of human fibrinogen in the presence of the major inhibitor of alpha-thrombin in plasma, antithrombin III (AT). This model quantitatively accounts for the time dependence of alpha-thrombin-catalyzed release of fibrinopeptides A and B concurrent with the inactivation of alpha-thrombin by AT and delineates the concerted interactions of alpha-thrombin, fibrin(ogen), and AT during the production of a fibrin clot. The model also provides a method for estimating the concentration of alpha-thrombin required to produce a clot of known composition and predicts a direct relationship between the plasma concentration of fibrinogen and the amount of fibrin produced by a bolus of alpha-thrombin. The predicted relationship between the concentration of fibrinogen and the amount of fibrin produced in plasma provides a plausible explanation for the observed linkage between plasma concentrations of fibrinogen and the risk for ischemic heart disease. link: http://identifiers.org/pubmed/2071587
MODEL1910030001
— v0.0.1This model is based on paper: Combination of singularly perturbed vector field method and method of directly defining t…
Details
We propose a new method to solve a system of complex ordinary differential equations (ODEs) with hidden hierarchy. Given a complex system of the ODE, the hierarchy of the system is generally hidden. Once we reveal the hierarchy of the system, the system can be reduced into subsystems called slow and fast subsystems. This division of slow and fast subsystems reduces the analysis and hence reduces the computation time, which can be expensive. In our new method, we first apply the singularly perturbed vector field method that is the global quasi-linearization method. This method exposes the hierarchy of a given complex system. Subsequently, we apply a version of the homotopy analysis method called the method of directly defining the inverse mapping. We applied our new method to the immunotherapy of advanced prostate cancer. link: http://identifiers.org/pubmed/30384811
BIOMD0000000611
— v0.0.1Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on BiomarkersNote:The SBML model is…
Details
A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment. link: http://identifiers.org/pubmed/26312163
Parameters:
Name | Description |
---|---|
k6 = 0.009975373 | Reaction: Xa + VII => Xa + VIIa; Xa, VII, Xa, VII, Rate Law: k6*Xa*VII |
k30 = 0.10001522; k29 = 149.91541 | Reaction: Xa_Va + II => Xa_Va_II; Xa_Va, II, Xa_Va_II, Xa_Va, II, Xa_Va_II, Rate Law: k30*Xa_Va*II-k29*Xa_Va_II |
k32 = 0.21872155 | Reaction: mIIa + Xa_Va => IIa + Xa_Va; mIIa, Xa_Va, mIIa, Xa_Va, Rate Law: k32*mIIa*Xa_Va |
k10 = 8.9987819 | Reaction: TF_VIIa_X => TF_VIIa_Xa; TF_VIIa_X, TF_VIIa_X, Rate Law: k10*TF_VIIa_X |
k25 = 0.0013357963 | Reaction: IXa_VIIIa_X => VIIIa1_L + VIIIa2 + X + IXa; IXa_VIIIa_X, IXa_VIIIa_X, Rate Law: k25*IXa_VIIIa_X |
k22 = 42.71401 | Reaction: IXa_VIIIa_X => IXa_VIIIa + Xa; IXa_VIIIa_X, IXa_VIIIa_X, Rate Law: k22*IXa_VIIIa_X |
k26 = 0.0013946425 | Reaction: IXa_VIIIa => VIIIa1_L + VIIIa2 + IXa; IXa_VIIIa, IXa_VIIIa, Rate Law: k26*IXa_VIIIa |
mwea0d7c35_f4d2_4205_8c59_11ac05134dde = 1.0958881E-4 | Reaction: mwbdb849d8_2b25_4551_8de8_adc8bead2303 => mw931f65a6_3967_4ac2_9904_ba791b216fc2; mwbdb849d8_2b25_4551_8de8_adc8bead2303, mwbdb849d8_2b25_4551_8de8_adc8bead2303, Rate Law: mwea0d7c35_f4d2_4205_8c59_11ac05134dde*mwbdb849d8_2b25_4551_8de8_adc8bead2303 |
mw4fc81076_be53_4fc3_9ade_3587e8d60355 = 0.1857857 | Reaction: mw3cec90c2_500e_4f30_b6be_325ef5194755 => mwa6be116e_72f1_439e_bca6_eb61f79cc68e + mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f; mw3cec90c2_500e_4f30_b6be_325ef5194755, mw3cec90c2_500e_4f30_b6be_325ef5194755, Rate Law: mw4fc81076_be53_4fc3_9ade_3587e8d60355*mw3cec90c2_500e_4f30_b6be_325ef5194755 |
k21 = 0.048795021; k20 = 0.0013766033 | Reaction: IXa_VIIIa + X => IXa_VIIIa_X; IXa_VIIIa, X, IXa_VIIIa_X, IXa_VIIIa, X, IXa_VIIIa_X, Rate Law: k21*IXa_VIIIa*X-k20*IXa_VIIIa_X |
mw8482ca53_fca1_4841_ac2f_2469a76a758e = 0.12914436; mw1511789f_5e7b_43bf_b162_d930b027a867 = 0.006 | Reaction: Xa + Va => Xa_Va; Xa, Va, Xa_Va, Xa, Va, Xa_Va, Rate Law: mw8482ca53_fca1_4841_ac2f_2469a76a758e*Xa*Va-mw1511789f_5e7b_43bf_b162_d930b027a867*Xa_Va |
mwa2636601_825e_4846_aa2d_c35bd242ec99 = 0.032359973 | Reaction: mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43 => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + mwf5c3f9df_7ccf_4ca7_b241_471a66720da8; mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, Rate Law: mwa2636601_825e_4846_aa2d_c35bd242ec99*mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43 |
k16 = 3.764127E-5 | Reaction: Xa + II => Xa + IIa + mwbdb849d8_2b25_4551_8de8_adc8bead2303; Xa, II, Xa, II, Rate Law: k16*Xa*II |
mw7aeacec0_be36_49bf_8548_7a3e2b5fe3cb = 0.029887563 | Reaction: mwa4fcfa0c_6944_42fc_8c74_7865f13953c8 => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa + mwf5c3f9df_7ccf_4ca7_b241_471a66720da8; mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, Rate Law: mw7aeacec0_be36_49bf_8548_7a3e2b5fe3cb*mwa4fcfa0c_6944_42fc_8c74_7865f13953c8 |
k15 = 2.3887492 | Reaction: TF_VIIa_IX => TF_VIIa + IXa; TF_VIIa_IX, TF_VIIa_IX, Rate Law: k15*TF_VIIa_IX |
mw61fdd721_9193_442c_bc9e_f1058c4720e7 = 1.2943783E-5 | Reaction: mw6d041b25_87db_4394_9b8b_7ac61e01f359 => VIIa + Xa; mw6d041b25_87db_4394_9b8b_7ac61e01f359, mw6d041b25_87db_4394_9b8b_7ac61e01f359, Rate Law: mw61fdd721_9193_442c_bc9e_f1058c4720e7*mw6d041b25_87db_4394_9b8b_7ac61e01f359 |
mw7300dcac_9389_4201_88c7_7effa7fdb0f3 = 10.565569 | Reaction: mwe70b2c96_44b9_48eb_967a_7eb850a916a6 => mw6591152c_8b5a_4c9b_b095_956988a01ba0 + IXa; mwe70b2c96_44b9_48eb_967a_7eb850a916a6, mwe70b2c96_44b9_48eb_967a_7eb850a916a6, Rate Law: mw7300dcac_9389_4201_88c7_7effa7fdb0f3*mwe70b2c96_44b9_48eb_967a_7eb850a916a6 |
mw6843129b_7601_452f_be5d_977f7203bfb5 = 0.0345 | Reaction: mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820 => Xa + VIIIa; mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, Rate Law: mw6843129b_7601_452f_be5d_977f7203bfb5*mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820 |
k3 = 0.0019496187; k4 = 0.075680013 | Reaction: TF + VIIa => TF_VIIa; TF, VIIa, TF_VIIa, TF, VIIa, TF_VIIa, Rate Law: k4*TF*VIIa-k3*TF_VIIa |
k17 = 1.44895 | Reaction: IIa + VIII => IIa + VIIIa; IIa, VIII, IIa, VIII, Rate Law: k17*IIa*VIII |
mw234b484f_d2d5_4ae8_a077_217c600588d8 = 0.24027638 | Reaction: mw2e632a32_3823_4933_95cb_19567cbcc66a => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + mw18e5caa7_26eb_4521_b217_da75bb3193ad; mw2e632a32_3823_4933_95cb_19567cbcc66a, mw2e632a32_3823_4933_95cb_19567cbcc66a, Rate Law: mw234b484f_d2d5_4ae8_a077_217c600588d8*mw2e632a32_3823_4933_95cb_19567cbcc66a |
mwc85f8d37_7f39_41b2_8ea4_00b5adad2eac = 0.07934338; mw807b9a99_fb16_421f_b724_69f29f3fcfb2 = 1.9895374 | Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + VIIIa => mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, VIIIa, mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, VIIIa, mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, Rate Law: mwc85f8d37_7f39_41b2_8ea4_00b5adad2eac*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*VIIIa-mw807b9a99_fb16_421f_b724_69f29f3fcfb2*mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43 |
k38 = 1.0556718E-6 | Reaction: Xa + ATIII => Xa_ATIII; Xa, ATIII, Xa, ATIII, Rate Law: k38*Xa*ATIII |
k11 = 9.5; k12 = 0.032999929 | Reaction: TF_VIIa + Xa => TF_VIIa_Xa; TF_VIIa, Xa, TF_VIIa_Xa, TF_VIIa, Xa, TF_VIIa_Xa, Rate Law: k12*TF_VIIa*Xa-k11*TF_VIIa_Xa |
mw0e80d629_98c1_44a6_bd57_3a4027c87b4c = 2.0869571; mw70d2f292_be41_4999_99cb_9c146808db85 = 0.077518002 | Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa_VIIIa => mwa4fcfa0c_6944_42fc_8c74_7865f13953c8; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa, mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa, mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, Rate Law: mw70d2f292_be41_4999_99cb_9c146808db85*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*IXa_VIIIa-mw0e80d629_98c1_44a6_bd57_3a4027c87b4c*mwa4fcfa0c_6944_42fc_8c74_7865f13953c8 |
mwaec203ce_06d5_4003_bfdb_7244d3d77255 = 0.0011427258 | Reaction: mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b => IXa + Xa; mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, Rate Law: mwaec203ce_06d5_4003_bfdb_7244d3d77255*mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b |
k19 = 0.11749508; k18 = 0.0050724996 | Reaction: IXa + VIIIa => IXa_VIIIa; IXa, VIIIa, IXa_VIIIa, IXa, VIIIa, IXa_VIIIa, Rate Law: k19*IXa*VIIIa-k18*IXa_VIIIa |
k5 = 3.3894832E-4 | Reaction: TF_VIIa + VII => TF_VIIa + VIIa; TF_VIIa, VII, TF_VIIa, VII, Rate Law: k5*TF_VIIa*VII |
k42 = 3.2905257E-7 | Reaction: TF_VIIa + ATIII => TF_VIIa_ATIII; TF_VIIa, ATIII, TF_VIIa, ATIII, Rate Law: k42*TF_VIIa*ATIII |
k37 = 0.025386917 | Reaction: TF_VIIa + Xa_TFPI => TF_VIIa_Xa_TFPI; TF_VIIa, Xa_TFPI, TF_VIIa, Xa_TFPI, Rate Law: k37*TF_VIIa*Xa_TFPI |
k27 = 4.0233556E-4 | Reaction: IIa + V => IIa + Va; IIa, V, IIa, V, Rate Law: k27*IIa*V |
k13 = 20.6708; k14 = 0.010569458 | Reaction: TF_VIIa + IX => TF_VIIa_IX; TF_VIIa, IX, TF_VIIa_IX, TF_VIIa, IX, TF_VIIa_IX, Rate Law: k14*TF_VIIa*IX-k13*TF_VIIa_IX |
mwb01ef86f_18d8_45e7_a452_31878dcb3d49 = 30.668349; mwc0cb654e_d95f_4d4b_8dc2_3a21afd35a19 = 0.13081564 | Reaction: mw6591152c_8b5a_4c9b_b095_956988a01ba0 + IX => mwe70b2c96_44b9_48eb_967a_7eb850a916a6; mw6591152c_8b5a_4c9b_b095_956988a01ba0, IX, mwe70b2c96_44b9_48eb_967a_7eb850a916a6, mw6591152c_8b5a_4c9b_b095_956988a01ba0, IX, mwe70b2c96_44b9_48eb_967a_7eb850a916a6, Rate Law: mwc0cb654e_d95f_4d4b_8dc2_3a21afd35a19*mw6591152c_8b5a_4c9b_b095_956988a01ba0*IX-mwb01ef86f_18d8_45e7_a452_31878dcb3d49*mwe70b2c96_44b9_48eb_967a_7eb850a916a6 |
k7 = 1.1527134E-5 | Reaction: IIa + VII => IIa + VIIa; IIa, VII, IIa, VII, Rate Law: k7*IIa*VII |
mw7b89687a_3110_4d5f_a9ec_7ca8761f0d41 = 84.659935; mw05b4111c_4463_4be0_aa1e_5a8f50c7bf67 = 0.059664002 | Reaction: VIIa + X => mw6d041b25_87db_4394_9b8b_7ac61e01f359; VIIa, X, mw6d041b25_87db_4394_9b8b_7ac61e01f359, VIIa, X, mw6d041b25_87db_4394_9b8b_7ac61e01f359, Rate Law: mw05b4111c_4463_4be0_aa1e_5a8f50c7bf67*VIIa*X-mw7b89687a_3110_4d5f_a9ec_7ca8761f0d41*mw6d041b25_87db_4394_9b8b_7ac61e01f359 |
k39 = 3.55E-6 | Reaction: mIIa + ATIII => mIIa_ATIII; mIIa, ATIII, mIIa, ATIII, Rate Law: k39*mIIa*ATIII |
mw4d2fe532_2ccd_42c4_9b4b_759022a87484 = 1.4001578; mwb63aa5ed_b6d8_4241_9987_54828945aea3 = 0.1289308 | Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa_VIIIa_X => mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa_X, mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa_X, mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, Rate Law: mwb63aa5ed_b6d8_4241_9987_54828945aea3*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*IXa_VIIIa_X-mw4d2fe532_2ccd_42c4_9b4b_759022a87484*mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca |
mw44adf04a_f1e2_4ca9_9615_5a9f4d3bbea8 = 0.13304333; mwc189e7ea_7518_4a4f_be0f_03f2d073b29e = 83.206626 | Reaction: IXa + X => mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b; IXa, X, mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, IXa, X, mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, Rate Law: mw44adf04a_f1e2_4ca9_9615_5a9f4d3bbea8*IXa*X-mwc189e7ea_7518_4a4f_be0f_03f2d073b29e*mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b |
mw7be1d52f_926f_47e0_964b_d3303c8453b1 = 0.05 | Reaction: mw6d041b25_87db_4394_9b8b_7ac61e01f359 + mw6591152c_8b5a_4c9b_b095_956988a01ba0 => VIIa + Xa + mw6591152c_8b5a_4c9b_b095_956988a01ba0; mw6d041b25_87db_4394_9b8b_7ac61e01f359, mw6591152c_8b5a_4c9b_b095_956988a01ba0, mw6d041b25_87db_4394_9b8b_7ac61e01f359, mw6591152c_8b5a_4c9b_b095_956988a01ba0, Rate Law: mw7be1d52f_926f_47e0_964b_d3303c8453b1*mw6d041b25_87db_4394_9b8b_7ac61e01f359*mw6591152c_8b5a_4c9b_b095_956988a01ba0 |
mwaf2c7981_908c_4f4c_898e_2491a9f04e17 = 0.10523968; mw1ddc2a05_bc78_4434_a2d9_d06701483346 = 19.338228 | Reaction: mwa6be116e_72f1_439e_bca6_eb61f79cc68e + mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9 => mw3cec90c2_500e_4f30_b6be_325ef5194755; mwa6be116e_72f1_439e_bca6_eb61f79cc68e, mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9, mw3cec90c2_500e_4f30_b6be_325ef5194755, mwa6be116e_72f1_439e_bca6_eb61f79cc68e, mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9, mw3cec90c2_500e_4f30_b6be_325ef5194755, Rate Law: mwaf2c7981_908c_4f4c_898e_2491a9f04e17*mwa6be116e_72f1_439e_bca6_eb61f79cc68e*mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9-mw1ddc2a05_bc78_4434_a2d9_d06701483346*mw3cec90c2_500e_4f30_b6be_325ef5194755 |
k41 = 3.917682E-6 | Reaction: IIa + ATIII => IIa_ATIII; IIa, ATIII, IIa, ATIII, Rate Law: k41*IIa*ATIII |
mwd6b996b1_d7fe_42de_b17e_b2482109c54d = 0.1043597; mwc5dc3645_536d_4bb4_88c7_4aeac4f5a241 = 2.0649128 | Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + Va => mw2e632a32_3823_4933_95cb_19567cbcc66a; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, Va, mw2e632a32_3823_4933_95cb_19567cbcc66a, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, Va, mw2e632a32_3823_4933_95cb_19567cbcc66a, Rate Law: mwd6b996b1_d7fe_42de_b17e_b2482109c54d*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*Va-mwc5dc3645_536d_4bb4_88c7_4aeac4f5a241*mw2e632a32_3823_4933_95cb_19567cbcc66a |
k33 = 1.801577E-4; k34 = 4.5E-4 | Reaction: Xa + TFPI => Xa_TFPI; Xa, TFPI, Xa_TFPI, Xa, TFPI, Xa_TFPI, Rate Law: k34*Xa*TFPI-k33*Xa_TFPI |
mw95e328a0_be5b_4260_b6e4_d85c4c4aae9e = 0.050084768; mw9bcd5c0b_3384_4d5e_92ce_70b13d64e8b8 = 0.11573051 | Reaction: IIa + mwd68cbf38_9266_4dfb_aa00_f817c3421aec => mwa6be116e_72f1_439e_bca6_eb61f79cc68e; IIa, mwd68cbf38_9266_4dfb_aa00_f817c3421aec, mwa6be116e_72f1_439e_bca6_eb61f79cc68e, IIa, mwd68cbf38_9266_4dfb_aa00_f817c3421aec, mwa6be116e_72f1_439e_bca6_eb61f79cc68e, Rate Law: mw9bcd5c0b_3384_4d5e_92ce_70b13d64e8b8*IIa*mwd68cbf38_9266_4dfb_aa00_f817c3421aec-mw95e328a0_be5b_4260_b6e4_d85c4c4aae9e*mwa6be116e_72f1_439e_bca6_eb61f79cc68e |
k9 = 0.036245656; k8 = 1.3800407 | Reaction: TF_VIIa + X => TF_VIIa_X; TF_VIIa, X, TF_VIIa_X, TF_VIIa, X, TF_VIIa_X, Rate Law: k9*TF_VIIa*X-k8*TF_VIIa_X |
k31 = 29.479266 | Reaction: Xa_Va_II => Xa_Va + mIIa + mwbdb849d8_2b25_4551_8de8_adc8bead2303; Xa_Va_II, Xa_Va_II, Rate Law: k31*Xa_Va_II |
mwa4cc6bbe_c310_445f_bba7_a94868342831 = 10740.276; mw3b48c5e7_774a_4dc4_917f_8f8cff8d9c4b = 90.211653 | Reaction: IIa + mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b => IIa + mwfa9d903a_b5e5_4a38_a649_dfe4719577aa; IIa, mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b, IIa, mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b, Rate Law: mw3b48c5e7_774a_4dc4_917f_8f8cff8d9c4b*IIa*mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b/(mwa4cc6bbe_c310_445f_bba7_a94868342831+mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b) |
mw6b555ed1_194e_4fa4_9688_8105aa7c60c0 = 0.013215482 | Reaction: mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa + X + mwf5c3f9df_7ccf_4ca7_b241_471a66720da8; mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, Rate Law: mw6b555ed1_194e_4fa4_9688_8105aa7c60c0*mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca |
mwec1b7289_5544_4c2b_b9f6_bf6524cabda5 = 3.15; mwaa306898_0d0f_4748_b48a_fcd56bdc0b16 = 0.15 | Reaction: Xa + VIII => mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820; Xa, VIII, mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, Xa, VIII, mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, Rate Law: mwaa306898_0d0f_4748_b48a_fcd56bdc0b16*Xa*VIII-mwec1b7289_5544_4c2b_b9f6_bf6524cabda5*mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820 |
States:
Name | Description |
---|---|
IX | IX |
mwa4fcfa0c 6944 42fc 8c74 7865f13953c8 | APC_IXa_VIIIa |
mwedf22864 05a0 40c3 a0d5 ede45a3e7e8f | APC |
mw6a8501d2 9479 41ae 8616 1e8d0e1bbfa9 | PC |
ATIII | ATIII |
Xa Va II | Xa_Va_II |
mw931f65a6 3967 4ac2 9904 ba791b216fc2 | F12_deg |
Xa | Xa |
mwf5c3f9df 7ccf 4ca7 b241 471a66720da8 | VIIIa_deg |
TF VIIa X | TF_VIIa_X |
TF VIIa Xa | TF_VIIa_Xa |
X | X |
Xa Va | Xa_Va |
mwe0bb059d deaa 45fa b7dc ec1c4409c4ca | APC_IXa_VIIIa_X |
mw18e5caa7 26eb 4521 b217 da75bb3193ad | Va_deg |
mw7a1594c9 f04f 478c 9f5f ccbe0b95a820 | Xa_VIII |
TF VIIa | TF_VIIa |
VIIIa | VIIIa |
Va | Va |
IIa | IIa |
Xa TFPI | Xa_TFPI |
VIIa | VIIa |
IXa VIIIa X | IXa_VIIIa_X |
TF VIIa IX | TF_VIIa_IX |
mwd68cbf38 9266 4dfb aa00 f817c3421aec | Tmod |
mw3cec90c2 500e 4f30 b6be 325ef5194755 | IIa_Tmod_PC |
IXa | IXa |
mwbdb849d8 2b25 4551 8de8 adc8bead2303 | F12 |
IXa VIIIa | IXa_VIIIa |
II | II |
mwa6be116e 72f1 439e bca6 eb61f79cc68e | IIa_Tmod |
mw2e632a32 3823 4933 95cb 19567cbcc66a | APC_Va |
MODEL1202170000
— v0.0.1This model is from the article: An old paper revisited: ‘‘A mathematical model of carbohydrate energy metabolism. Int…
Details
We revisit an old Russian paper by V.V. Dynnik, R. Heinrich and E.E. Sel'kov (1980a,b) describing: "A mathematical model of carbohydrate energy metabolism. Interaction between glycolysis, the Krebs cycle and the H-transporting shuttles at varying ATPases load". We analyse the model mathematically and calculate the control coefficients as a function of ATPase loads. We also evaluate the structure of the metabolic network in terms of elementary flux modes. We show how this model can respond to an ATPase load as well as to the glucose supply. We also show how this simple model can help in understanding the articulation between the major blocks of energetic metabolism, i.e. glycolysis, the Krebs cycle and the H-transporting shuttles. link: http://identifiers.org/pubmed/18304584
BIOMD0000000232
— v0.0.1This a model from the article: Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production…
Details
Mitochondria play a central role in cellular energetic metabolism. The essential parts of this metabolism are the tricarboxylic acid (TCA) cycle, the respiratory chain and the adenosine triphosphate (ATP) synthesis machinery. Here a simplified model of these three metabolic components with a limited set of differential equations is presented. The existence of a steady state is demonstrated and results of numerical simulations are presented. The relevance of a simple model to represent actual in vivo behavior is discussed. link: http://identifiers.org/pubmed/19007794
Parameters:
Name | Description |
---|---|
At = 4.16 millimolar | Reaction: ADP = At-ATP, Rate Law: missing |
k2=0.152 per millimolar per second | Reaction: Pyr + NAD => AcCoA + NADH, Rate Law: mitochondrion*k2*Pyr*NAD |
JANT = NaN millimolar per second | Reaction: ATP => ADP, Rate Law: mitochondrion*JANT |
k8=3.6 per second | Reaction: OAA =>, Rate Law: mitochondrion*k8*OAA |
k7=0.04 per millimolar per second | Reaction: Pyr + ATP => OAA + ADP, Rate Law: mitochondrion*k7*Pyr*ATP |
Keq=0.3975 dimensionless; k6=0.0032 per second | Reaction: OAA => KG, Rate Law: mitochondrion*k6*(OAA-KG/Keq) |
k3=57.142 per millimolar per second | Reaction: OAA + AcCoA => Cit, Rate Law: mitochondrion*k3*OAA*AcCoA |
Jresp = NaN millimolar per second | Reaction: NADH + O2 + H => NAD + H2O + He, Rate Law: mitochondrion*Jresp |
Nt = 1.07 millimolar | Reaction: NADH = Nt-NAD, Rate Law: missing |
k1=0.038 millimolar per second | Reaction: => Pyr, Rate Law: mitochondrion*k1 |
k5=0.082361 per millimolar squared per second; At = 4.16 millimolar | Reaction: KG + ADP + NAD => OAA + ATP + NADH, Rate Law: mitochondrion*k5*KG*NAD*(At-ATP) |
k4=0.053 per millimolar per second | Reaction: Cit + NAD => KG + NADH, Rate Law: mitochondrion*k4*Cit*NAD |
JATP = NaN millimolar per second | Reaction: ADP + iP + He => ATP + H2O + H, Rate Law: mitochondrion*JATP |
Jleak = NaN millimolar per second | Reaction: He => H, Rate Law: mitochondrion*Jleak |
States:
Name | Description |
---|---|
O2 | [dioxygen; Oxygen] |
iP | [phosphate(3-); Orthophosphate] |
ATP | [ATP; ATP] |
NADH | [NADH; NADH] |
Cit | [citrate(3-); Citrate] |
Pyr | [pyruvate; Pyruvate] |
AcCoA | [acetyl-CoA; Acetyl-CoA] |
H2O | [water; H2O] |
OAA | [oxaloacetate(2-); Oxaloacetate] |
ADP | [ADP; ADP] |
He | [proton; H+] |
NAD | [NAD(+); NAD+] |
H | [proton; H+] |
KG | [2-oxoglutarate(2-); 2-Oxoglutarate] |
BIOMD0000000819
— v0.0.1This a model from the article: A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted trea…
Details
Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth. link: http://identifiers.org/pubmed/29351275
Parameters:
Name | Description |
---|---|
R_Td = 2.075E-7; P_DD = 0.0133297534723066 | Reaction: => IL_6R_on_D, Rate Law: compartment*R_Td*P_DD |
K_f = 2.35 1/(fmol*d) | Reaction: => IL_6__Cell_bound_IL_6R_complex_on_E; IL_6__L, IL_6R_on_E, Rate Law: compartment*K_f*IL_6__L*IL_6R_on_E |
P_S = 0.899999967997301; alpha_S = 0.6 1/d | Reaction: => Cancer_Stem_Cell_S, Rate Law: compartment*alpha_S*P_S*Cancer_Stem_Cell_S |
R_Ts = 1.66E-6 fmol; P_phiS = 539.99998079838 | Reaction: => IL_6R_on_S, Rate Law: compartment*R_Ts*P_phiS |
A_out = 2.0; alpha_E = 0.666487673615332 1/d | Reaction: => Differentiated_tumor_cell_D; Progenitor_tumor_cell_E, Rate Law: compartment*A_out*alpha_E*Progenitor_tumor_cell_E |
delta_D = 0.0612 1/d; phi_D = 0.0; gamma_D = 2.38 | Reaction: Differentiated_tumor_cell_D =>, Rate Law: compartment*delta_D*Differentiated_tumor_cell_D/(1+gamma_D*phi_D) |
K_r = 2.24 1/d | Reaction: IL_6__Cell_bound_IL_6R_complex_on_D => IL_6__L + IL_6R_on_D; IL_6__Cell_bound_IL_6R_complex_on_D, Rate Law: compartment*K_r*IL_6__Cell_bound_IL_6R_complex_on_D |
lambda = 0.4152 1/d | Reaction: IL_6__L =>, Rate Law: compartment*lambda*IL_6__L |
R_Te = 2.075E-7; P_etaE = 119.993373526503 | Reaction: => IL_6R_on_E, Rate Law: compartment*R_Te*P_etaE |
R_Te = 2.075E-7; D_etaE = 6.12E-4 | Reaction: IL_6__Cell_bound_IL_6R_complex_on_E => ; IL_6R_on_E, Rate Law: compartment*IL_6__Cell_bound_IL_6R_complex_on_E*R_Te*D_etaE/(IL_6R_on_E+IL_6__Cell_bound_IL_6R_complex_on_E) |
R_Td = 2.075E-7; D_DD = 6.12E-4 | Reaction: IL_6R_on_D => ; IL_6__Cell_bound_IL_6R_complex_on_D, Rate Law: compartment*IL_6R_on_D*R_Td*D_DD/(IL_6R_on_D+IL_6__Cell_bound_IL_6R_complex_on_D) |
R_Ts = 1.66E-6 fmol; D_phiS = 12.6 | Reaction: IL_6__Cell_bound_IL_6R_complex_on_S => ; IL_6R_on_S, Rate Law: compartment*IL_6__Cell_bound_IL_6R_complex_on_S*R_Ts*D_phiS/(IL_6R_on_S+IL_6__Cell_bound_IL_6R_complex_on_S) |
gamma_S = 2.38; phi_S = 0.0; delta_S = 0.0126 1/d | Reaction: Cancer_Stem_Cell_S =>, Rate Law: compartment*delta_S*Cancer_Stem_Cell_S/(1+gamma_S*phi_S) |
alpha_E = 0.666487673615332 1/d | Reaction: Progenitor_tumor_cell_E =>, Rate Law: compartment*alpha_E*Progenitor_tumor_cell_E |
P_S = 0.899999967997301; alpha_S = 0.6 1/d; A_in = 2.0 | Reaction: => Progenitor_tumor_cell_E; Cancer_Stem_Cell_S, Rate Law: compartment*A_in*alpha_S*(1-P_S)*Cancer_Stem_Cell_S |
K_p = 24.95 1/d | Reaction: IL_6__Cell_bound_IL_6R_complex_on_D => IL_6R_on_D; IL_6__Cell_bound_IL_6R_complex_on_D, Rate Law: compartment*K_p*IL_6__Cell_bound_IL_6R_complex_on_D |
delta_E = 0.0612 1/d; gamma_E = 2.38; phi_E = 0.0 | Reaction: Progenitor_tumor_cell_E =>, Rate Law: compartment*delta_E*Progenitor_tumor_cell_E/(1+gamma_E*phi_E) |
rho = 7.0E-7 fmol/d | Reaction: => IL_6__L; Cancer_Stem_Cell_S, Progenitor_tumor_cell_E, Differentiated_tumor_cell_D, Rate Law: compartment*rho*(Cancer_Stem_Cell_S+Progenitor_tumor_cell_E+Differentiated_tumor_cell_D) |
States:
Name | Description |
---|---|
IL 6 Cell bound IL 6R complex on S | [Receptor; Interleukin-6; Interleukin-6; Cancer Stem Cell] |
IL 6 Cell bound IL 6R complex on E | [Interleukin-6; Receptor; Interleukin-6; Ancestor] |
tumor | tumor |
Cancer Stem Cell S | [Head and Neck Squamous Cell Carcinoma; head and neck squamous cell carcinoma; Cancer Stem Cell] |
Progenitor tumor cell E | [head and neck squamous cell carcinoma; Head and Neck Squamous Cell Carcinoma; Ancestor] |
IL 6R on S | [Receptor; Interleukin-6 receptor subunit alpha; Interleukin-6; Cancer Stem Cell] |
Differentiated tumor cell D | [head and neck squamous cell carcinoma; Head and Neck Squamous Cell Carcinoma; Interleukin-6; differentiated] |
IL 6 Cell bound IL 6R complex on D | [Interleukin-6; Interleukin-6; Receptor; differentiated] |
IL 6R on E | [Interleukin-6; Receptor; Interleukin-6 receptor subunit alpha; Ancestor] |
IL 6R on D | [Interleukin-6; Receptor; Interleukin-6 receptor subunit alpha; differentiated] |
IL 6 L | [Interleukin-6] |
BIOMD0000000958
— v0.0.1We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmiss…
Details
We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic reproduction number, and we investigate the sensitivity of the model with respect to the variation of each one of its parameters. Numerical simulations show the suitability of the proposed COVID-19 model for the outbreak that occurred in Wuhan, China. link: http://identifiers.org/pubmed/32341628
MODEL2003160002
— v0.0.1Use of the bacterium Wolbachia is an innovative new strategy designed to break the cycle of dengue transmission. There a…
Details
Use of the bacterium Wolbachia is an innovative new strategy designed to break the cycle of dengue transmission. There are two main mechanisms by which Wolbachia could achieve this: by reducing the level of dengue virus in the mosquito and/or by shortening the host mosquito's lifespan. However, although Wolbachia shortens the lifespan, it also gives a breeding advantage which results in complex population dynamics. This study focuses on the development of a mathematical model to quantify the effect on human dengue cases of introducing Wolbachia into the mosquito population. The model consists of a compartment-based system of first-order differential equations; seasonal forcing in the mosquito population is introduced through the adult mosquito death rate. The analysis focuses on a single dengue outbreak typical of a region with a strong seasonally-varying mosquito population. We found that a significant reduction in human dengue cases can be obtained provided that Wolbachia-carrying mosquitoes persist when competing with mosquitoes without Wolbachia. Furthermore, using the Wolbachia strain WMel reduces the mosquito lifespan by at most 10% and allows them to persist in competition with non-Wolbachia-carrying mosquitoes. Mosquitoes carrying the WMelPop strain, however, are not likely to persist as it reduces the mosquito lifespan by up to 50%. When all other effects of Wolbachia on the mosquito physiology are ignored, cytoplasmic incompatibility alone results in a reduction in the number of human dengue cases. A sensitivity analysis of the parameters in the model shows that the transmission probability, the biting rate and the average adult mosquito death rate are the most important parameters for the outcome of the cumulative proportion of human individuals infected with dengue. link: http://identifiers.org/pubmed/25645184
MODEL1006230081
— v0.0.1This a model from the article: Modeling defective interfering virus therapy for AIDS: conditions for DIV survival. N…
Details
The administration of a genetically engineered defective interfering virus (DIV) that interferes with HIV-1 replication has been proposed as a therapy for HIV-1 infection and AIDS. The proposed interfering virus, which is designed to superinfect HIV-1 infected cells, carries ribozymes that cleave conserved regions in HIV-1 RNA that code for the viral envelope protein. Thus DIV infection of HIV-1 infected cells should reduce or eliminate viral production by these cells. The success of this therapeutic strategy will depend both on the intercellular interaction of DIV and HIV-1, and on the overall dynamics of virus and T cells in the body. To study these dynamical issues, we have constructed a mathematical model of the interaction of HIV-1, DIV, and CD4+ cells in vivo. The results of both mathematical analysis and numerical simulation indicate that survival of the engineered DIV purely on a peripheral blood HIV-1 infection is unlikely. However, analytical results indicate that DIV might well survive on HIV-1 infected CD4+ cells in lymphoid organs such as lymph nodes and spleen, or on other HIV-1 infected cells in these organs. link: http://identifiers.org/pubmed/7881191
MODEL1006230033
— v0.0.1This a model from the article: Modeling defective interfering virus therapy for AIDS: conditions for DIV survival. N…
Details
The administration of a genetically engineered defective interfering virus (DIV) that interferes with HIV-1 replication has been proposed as a therapy for HIV-1 infection and AIDS. The proposed interfering virus, which is designed to superinfect HIV-1 infected cells, carries ribozymes that cleave conserved regions in HIV-1 RNA that code for the viral envelope protein. Thus DIV infection of HIV-1 infected cells should reduce or eliminate viral production by these cells. The success of this therapeutic strategy will depend both on the intercellular interaction of DIV and HIV-1, and on the overall dynamics of virus and T cells in the body. To study these dynamical issues, we have constructed a mathematical model of the interaction of HIV-1, DIV, and CD4+ cells in vivo. The results of both mathematical analysis and numerical simulation indicate that survival of the engineered DIV purely on a peripheral blood HIV-1 infection is unlikely. However, analytical results indicate that DIV might well survive on HIV-1 infected CD4+ cells in lymphoid organs such as lymph nodes and spleen, or on other HIV-1 infected cells in these organs. link: http://identifiers.org/pubmed/7881191
BIOMD0000000875
— v0.0.1This is the general model without delay described by the equation system (1) in: **A model of HIV-1 pathogenesis that in…
Details
Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection. link: http://identifiers.org/pubmed/10701304
Parameters:
Name | Description |
---|---|
k = 3.43E-8 l/(s*#) | Reaction: T => T_i; V_I, Rate Law: plasma*k*V_I*T |
delta = 0.5 1/ms | Reaction: T_i =>, Rate Law: plasma*delta*T_i |
np = 0.5 1; N = 480.0 1; delta = 0.5 1/ms | Reaction: => V_I; T_i, Rate Law: plasma*(1-np)*N*delta*T_i |
delta1 = 0.03 1/ms | Reaction: T =>, Rate Law: plasma*delta1*T |
c = 3.0 1/ms | Reaction: V_I =>, Rate Law: plasma*c*V_I |
lambda = 10.0 #/(l*s) | Reaction: => T, Rate Law: plasma*lambda |
States:
Name | Description |
---|---|
V NI | V_NI |
T | [uninfected] |
T i | [infected cell] |
V I | [C14283] |
MODEL8102792069
— v0.0.1described in: **A model of HIV-1 pathogenesis that includes an intracellular delay.** Nelson PW, Murray JD, Perelson A…
Details
Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection. link: http://identifiers.org/pubmed/10701304
BIOMD0000000243
— v0.0.1This is the reduced model (model 8) described in: **Dynamics within the CD95 death-inducing signaling complex decide lif…
Details
This study explores the dilemma in cellular signaling that triggering of CD95 (Fas/APO-1) in some situations results in cell death and in others leads to the activation of NF-kappaB. We established an integrated kinetic mathematical model for CD95-mediated apoptotic and NF-kappaB signaling. Systematic model reduction resulted in a surprisingly simple model well approximating experimentally observed dynamics. The model postulates a new link between c-FLIP(L) cleavage in the death-inducing signaling complex (DISC) and the NF-kappaB pathway. We validated experimentally that CD95 stimulation resulted in an interaction of p43-FLIP with the IKK complex followed by its activation. Furthermore, we showed that the apoptotic and NF-kappaB pathways diverge already at the DISC. Model and experimental analysis of DISC formation showed that a subtle balance of c-FLIP(L) and procaspase-8 determines life/death decisions in a nonlinear manner. We present an integrated model describing the complex dynamics of CD95-mediated apoptosis and NF-kappaB signaling. link: http://identifiers.org/pubmed/20212524
Parameters:
Name | Description |
---|---|
k3 = 0.6693316 | Reaction: L_RF + FL => L_RF_FL, Rate Law: default*k3*L_RF*FL |
k2 = 1.277248E-4 | Reaction: L_RF + C8 => L_RF_C8, Rate Law: default*k2*L_RF*C8 |
k10 = 0.1205258 | Reaction: C8 + C3_star => p43_p41 + C3_star, Rate Law: default*k10*C8*C3_star |
k5 = 5.946569E-4 | Reaction: L_RF_FS + C8 => L_RF_C8_FS, Rate Law: default*k5*L_RF_FS*C8 |
k4 = 1.0E-5 | Reaction: L_RF + FS => L_RF_FS, Rate Law: default*k4*L_RF*FS |
k9 = 0.002249759 | Reaction: C3 + C8_star => C3_star + C8_star, Rate Law: default*k9*C3*C8_star |
k16 = 0.02229912 | Reaction: p43_FLIP_IKK_star =>, Rate Law: default*k16*p43_FLIP_IKK_star |
k14 = 0.3588224 | Reaction: NF_kB_IkB + p43_FLIP_IKK_star => NF_kB_IkB_P + p43_FLIP_IKK_star, Rate Law: default*k14*NF_kB_IkB*p43_FLIP_IKK_star |
k7 = 0.8875063 | Reaction: L_RF_FL + FS => L_RF_FL_FS, Rate Law: default*k7*L_RF_FL*FS |
k13 = 7.204261E-4 | Reaction: p43_FLIP + IKK => p43_FLIP_IKK_star, Rate Law: default*k13*p43_FLIP*IKK |
k11 = 0.02891451 | Reaction: C8_star =>, Rate Law: default*k11*C8_star |
k17 = 0.0064182 | Reaction: NF_kB_star =>, Rate Law: default*k17*NF_kB_star |
k12 = 0.1502914 | Reaction: C3_star =>, Rate Law: default*k12*C3_star |
k1 = 1.0 | Reaction: L + RF => L_RF, Rate Law: default*k1*L*RF |
k6 = 0.9999999 | Reaction: L_RF_FS + FL => L_RF_FL_FS, Rate Law: default*k6*L_RF_FS*FL |
k15 = 3.684162 | Reaction: NF_kB_IkB_P => NF_kB_star, Rate Law: default*k15*NF_kB_IkB_P |
k8 = 8.044378E-4 | Reaction: p43_p41 + p43_p41 => C8_star, Rate Law: default*k8*p43_p41*p43_p41 |
States:
Name | Description |
---|---|
C3 star | [Caspase-3] |
L RF FL | [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
C3 | [Caspase-3] |
p43 FLIP | [CASP8 and FADD-like apoptosis regulator] |
L | [CD95 ligand] |
IKK | [NF-kappa-B essential modulator; Inhibitor of nuclear factor kappa-B kinase subunit beta; Inhibitor of nuclear factor kappa-B kinase subunit alpha] |
p43 p41 | [Caspase-8] |
C8 | [Caspase-8] |
L RF C8 | [CD95 ligand; Caspase-8; Tumor necrosis factor receptor superfamily member 6; FAS-associated death domain protein] |
FL | [CASP8 and FADD-like apoptosis regulator] |
NF kB star | [NF-kappaB complex; Nuclear factor NF-kappa-B p105 subunit] |
p43 FLIP IKK star | p43-FLIP:IKK* |
L RF FS | [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
NF kB IkB | [IkBs:NFkB [cytosol]; NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit] |
C8 star | [Caspase-8] |
L RF | [FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6; CD95 ligand] |
L RF FL FS | [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
L RF FL FL | [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
FS | [CASP8 and FADD-like apoptosis regulator] |
NF kB IkB P | [NFkB Complex [cytosol]; Phospho-NF-kappaB Inhibitor [cytosol]; NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit] |
L RF C8 FS | [CD95 ligand; CASP8 and FADD-like apoptosis regulator; Caspase-8; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
RF | [FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
L RF FS FS | [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6] |
BIOMD0000000182
— v0.0.1Neves2008 - Role of cell shape and size in controlling intracellular signallingThe role of cell shape and size in the fl…
Details
The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells. link: http://identifiers.org/pubmed/18485874
Parameters:
Name | Description |
---|---|
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PPase_mek = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=15.7 0.001*dimensionless*m^(-3)*mol | Reaction: MEK_active_cyto => MEK_cyto; PP2A_cyto, Rate Law: Vmax_PPase_mek*0.00166112956810631*MEK_active_cyto*1/(Km+0.00166112956810631*MEK_active_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Kf_AC_activation=500.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr_AC_activation=1.0 s^(-1) | Reaction: G_a_s_cyto + AC_cyto_mem => AC_active_cyto_mem, Rate Law: (Kf_AC_activation*0.00166112956810631*G_a_s_cyto*AC_cyto_mem+(-Kr_AC_activation*AC_active_cyto_mem))*cyto_mem |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=0.77 0.001*dimensionless*m^(-3)*mol; Vmax_PPase_MAPK = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: MAPK_active_cyto => MAPK_cyto; PP2A_cyto, Rate Law: Vmax_PPase_MAPK*0.00166112956810631*MAPK_active_cyto*1/(Km+0.00166112956810631*MAPK_active_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Kr=0.2 s^(-1); Kf=1.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: BAR_cyto_mem + iso_extra => iso_BAR_cyto_mem, Rate Law: (Kf*BAR_cyto_mem*0.00166112956810631*iso_extra+(-Kr*iso_BAR_cyto_mem))*cyto_mem |
Vmax_PPase_Raf = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=15.7 0.001*dimensionless*m^(-3)*mol | Reaction: B_Raf_active_cyto => B_Raf_cyto; PP2A_cyto, Rate Law: Vmax_PPase_Raf*0.00166112956810631*B_Raf_active_cyto*1/(Km+0.00166112956810631*B_Raf_active_cyto)*cyto*1*1/KMOLE |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_pde4_p_pde4_p = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km_pde4_p=1.3 0.001*dimensionless*m^(-3)*mol | Reaction: cAMP_cyto => AMP_cyto; PDE4_P_cyto, Rate Law: Vmax_pde4_p_pde4_p*0.00166112956810631*cAMP_cyto*1/(Km_pde4_p+0.00166112956810631*cAMP_cyto)*cyto*1*1/KMOLE |
Km_PDE4=1.3 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PDE4_PDE4 = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: cAMP_cyto => AMP_cyto; PDE4_cyto, Rate Law: Vmax_PDE4_PDE4*0.00166112956810631*cAMP_cyto*1/(Km_PDE4+0.00166112956810631*cAMP_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Km_AC_active=32.0 0.001*dimensionless*m^(-3)*mol; Vmax_AC_active_AC_active = NaN item*μm^(-2)*s^(-1) | Reaction: ATP_cyto => cAMP_cyto; AC_active_cyto_mem, Rate Law: Vmax_AC_active_AC_active*0.00166112956810631*ATP_cyto*1/(Km_AC_active+0.00166112956810631*ATP_cyto)*cyto_mem |
Kf_G_binds_BAR=0.3 1000*dimensionless*m^3*mol^(-1)*s^(-1); I=0.0 dimensionless*A*m^(-2); Kr_G_binds_BAR=0.1 s^(-1) | Reaction: BAR_cyto_mem + G_protein_cyto => BAR_G_cyto_mem, Rate Law: (Kf_G_binds_BAR*BAR_cyto_mem*0.00166112956810631*G_protein_cyto+(-Kr_G_binds_BAR*BAR_G_cyto_mem))*cyto_mem |
Km=0.046296 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_MEK_activates_MAPK = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: MAPK_cyto => MAPK_active_cyto; MEK_active_cyto, Rate Law: Vmax_MEK_activates_MAPK*0.00166112956810631*MAPK_cyto*1/(Km+0.00166112956810631*MAPK_cyto)*cyto*1*1/KMOLE |
Km=6.0 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_pp_ptp = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: PTP_PKA_cyto => PTP_cyto; PTP_PP_cyto, Rate Law: Vmax_pp_ptp*0.00166112956810631*PTP_PKA_cyto*1/(Km+0.00166112956810631*PTP_PKA_cyto)*cyto*1*1/KMOLE |
Vmax_highKM_PDE = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=15.0 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3) | Reaction: cAMP_cyto => AMP_cyto; PDE_high_km_cyto, Rate Law: Vmax_highKM_PDE*0.00166112956810631*cAMP_cyto*1/(Km+0.00166112956810631*cAMP_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Kr_activate_Gs=0.0 1000000*dimensionless*m^6*mol^(-2)*s^(-1); Kf_activate_Gs=0.025 s^(-1) | Reaction: iso_BAR_G_cyto_mem => iso_BAR_cyto_mem + bg_cyto + G_a_s_cyto, Rate Law: (Kf_activate_Gs*iso_BAR_G_cyto_mem-Kr_activate_Gs*iso_BAR_cyto_mem*0.00166112956810631*bg_cyto*0.00166112956810631*G_a_s_cyto)*cyto_mem |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=0.15909 0.001*dimensionless*m^(-3)*mol; Vmax_Raf_activates_MEK = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: MEK_cyto => MEK_active_cyto; B_Raf_active_cyto, Rate Law: Vmax_Raf_activates_MEK*0.00166112956810631*MEK_cyto*1/(Km+0.00166112956810631*MEK_cyto)*cyto*1*1/KMOLE |
Vmax_PKA_P_PDE = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=0.5 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3) | Reaction: PDE4_cyto => PDE4_P_cyto; PKA_cyto, Rate Law: Vmax_PKA_P_PDE*0.00166112956810631*PDE4_cyto*1/(Km+0.00166112956810631*PDE4_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Km_grk=15.0 item*μm^(-2); Vmax_grk_GRK = NaN item*μm^(-2)*s^(-1) | Reaction: iso_BAR_cyto_mem => iso_BAR_p_cyto_mem; GRK_cyto, Rate Law: Vmax_grk_GRK*iso_BAR_cyto_mem*1/(Km_grk+iso_BAR_cyto_mem)*cyto_mem |
Km=0.5 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PKA_activates_Raf = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: B_Raf_cyto => B_Raf_active_cyto; PKA_cyto, Rate Law: Vmax_PKA_activates_Raf*0.00166112956810631*B_Raf_cyto*1/(Km+0.00166112956810631*B_Raf_cyto)*cyto*1*1/KMOLE |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr_GTPase=0.0 s^(-1); Kf_GTPase=0.06667 s^(-1) | Reaction: G_a_s_cyto => G_GDP_cyto, Rate Law: (Kf_GTPase*0.00166112956810631*G_a_s_cyto+(-Kr_GTPase*0.00166112956810631*G_GDP_cyto))*cyto*1*1/KMOLE |
Kr_G_binds_iso_BAR=0.1 s^(-1); I=0.0 dimensionless*A*m^(-2); Kf_G_binds_iso_BAR=10.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: iso_BAR_cyto_mem + G_protein_cyto => iso_BAR_G_cyto_mem, Rate Law: (Kf_G_binds_iso_BAR*iso_BAR_cyto_mem*0.00166112956810631*G_protein_cyto+(-Kr_G_binds_iso_BAR*iso_BAR_G_cyto_mem))*cyto_mem |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=0.46 0.001*dimensionless*m^(-3)*mol; Vmax_PTP = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: MAPK_active_cyto => MAPK_cyto; PTP_cyto, Rate Law: Vmax_PTP*0.00166112956810631*MAPK_active_cyto*1/(Km+0.00166112956810631*MAPK_active_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Km_GRK_bg=4.0 item*μm^(-2); Vmax_GRK_bg_GRK_bg = NaN item*μm^(-2)*s^(-1) | Reaction: iso_BAR_cyto_mem => iso_BAR_p_cyto_mem; GRK_bg_cyto, Rate Law: Vmax_GRK_bg_GRK_bg*iso_BAR_cyto_mem*1/(Km_GRK_bg+iso_BAR_cyto_mem)*cyto_mem |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr=2.8E-4 s^(-1); Kf=0.0059 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: R2C2_cyto + cAMP_cyto => c_R2C2_cyto, Rate Law: (Kf*0.00166112956810631*R2C2_cyto*0.00166112956810631*cAMP_cyto+(-Kr*0.00166112956810631*c_R2C2_cyto))*cyto*1*1/KMOLE |
Km_pp2a_4=8.0 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_pp2a_4_pp2a_4 = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: PDE4_P_cyto => PDE4_cyto; PP_PDE_cyto, Rate Law: Vmax_pp2a_4_pp2a_4*0.00166112956810631*PDE4_P_cyto*1/(Km_pp2a_4+0.00166112956810631*PDE4_P_cyto)*cyto*1*1/KMOLE |
I=0.0 dimensionless*A*m^(-2); Kf=1.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr=0.062 s^(-1) | Reaction: iso_extra + BAR_G_cyto_mem => iso_BAR_G_cyto_mem, Rate Law: (Kf*0.00166112956810631*iso_extra*BAR_G_cyto_mem+(-Kr*iso_BAR_G_cyto_mem))*cyto_mem |
Vmax_PTP_PKA = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=9.0 0.001*dimensionless*m^(-3)*mol | Reaction: MAPK_active_cyto => MAPK_cyto; PTP_PKA_cyto, Rate Law: Vmax_PTP_PKA*0.00166112956810631*MAPK_active_cyto*1/(Km+0.00166112956810631*MAPK_active_cyto)*cyto*1*1/KMOLE |
Vmax_AC_basal_AC_basal = NaN item*μm^(-2)*s^(-1); I=0.0 dimensionless*A*m^(-2); Km_AC_basal=1030.0 0.001*dimensionless*m^(-3)*mol | Reaction: ATP_cyto => cAMP_cyto; AC_cyto_mem, Rate Law: Vmax_AC_basal_AC_basal*0.00166112956810631*ATP_cyto*1/(Km_AC_basal+0.00166112956810631*ATP_cyto)*cyto_mem |
Kf=8.35 1000*dimensionless*m^3*mol^(-1)*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr=0.0167 s^(-1) | Reaction: c3_R2C2_cyto + cAMP_cyto => PKA_cyto, Rate Law: (Kf*0.00166112956810631*c3_R2C2_cyto*0.00166112956810631*cAMP_cyto+(-Kr*0.00166112956810631*PKA_cyto))*cyto*1*1/KMOLE |
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr_bg_binds_GRK=0.25 s^(-1); Kf_bg_binds_GRK=1.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) | Reaction: GRK_cyto + bg_cyto => GRK_bg_cyto, Rate Law: (Kf_bg_binds_GRK*0.00166112956810631*GRK_cyto*0.00166112956810631*bg_cyto+(-Kr_bg_binds_GRK*0.00166112956810631*GRK_bg_cyto))*cyto*1*1/KMOLE |
Kf_trimer=6.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr_trimer=0.0 s^(-1) | Reaction: bg_cyto + G_GDP_cyto => G_protein_cyto, Rate Law: (Kf_trimer*0.00166112956810631*bg_cyto*0.00166112956810631*G_GDP_cyto+(-Kr_trimer*0.00166112956810631*G_protein_cyto))*cyto*1*1/KMOLE |
Km=0.1 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PKA_P_PTP = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) | Reaction: PTP_cyto => PTP_PKA_cyto; PKA_cyto, Rate Law: Vmax_PKA_P_PTP*0.00166112956810631*PTP_cyto*1/(Km+0.00166112956810631*PTP_cyto)*cyto*1*1/KMOLE |
States:
Name | Description |
---|---|
iso extra | iso_extra |
PDE4 P cyto | [cAMP-specific 3',5'-cyclic phosphodiesterase 4A; IPR003607] |
GRK cyto | GRK_cyto |
bg cyto | bg_cyto |
PKA cyto | [Protein kinase, cAMP-dependent, catalytic, alphacAMP-dependent protein kinase catalytic subunit alpha] |
BAR cyto mem | [Beta-1 adrenergic receptor] |
B Raf cyto | [V-raf murine sarcoma viral oncogene B1-like protein] |
MEK active cyto | [Dual specificity mitogen-activated protein kinase kinase 1] |
c3 R2C2 cyto | [cAMP-dependent protein kinase complex] |
R2C2 cyto | [cAMP-dependent protein kinase complex] |
G a s cyto | [Guanine nucleotide-binding protein G(olf) subunit alpha] |
AMP cyto | [AMP; AMP] |
B Raf active cyto | [V-raf murine sarcoma viral oncogene B1-like protein] |
iso BAR cyto mem | [Beta-1 adrenergic receptor] |
c R2C2 cyto | [cAMP-dependent protein kinase complex] |
cAMP cyto | [3',5'-cyclic AMP; 3',5'-Cyclic AMP] |
GRK bg cyto | GRK_bg_cyto |
G GDP cyto | [GDP; IPR001019; GDP] |
PTP PKA cyto | [Tyrosine-protein phosphatase non-receptor type 7; Protein kinase, cAMP-dependent, catalytic, alphacAMP-dependent protein kinase catalytic subunit alpha] |
MEK cyto | [Dual specificity mitogen-activated protein kinase kinase 1] |
iso BAR p cyto mem | [Beta-1 adrenergic receptor] |
G protein cyto | [heterotrimeric G-protein complex] |
AC cyto mem | [Adenylate cyclase type 2; IPR001054] |
PDE4 cyto | [cAMP-specific 3',5'-cyclic phosphodiesterase 4A; IPR003607] |
PTP cyto | [Tyrosine-protein phosphatase non-receptor type 7] |
AC active cyto mem | [Adenylate cyclase type 2; IPR001054] |
MAPK cyto | [Mitogen-activated protein kinase 1] |
BAR G cyto mem | [Beta-1 adrenergic receptor] |
iso BAR G cyto mem | [Beta-1 adrenergic receptor; heterotrimeric G-protein complex] |
ATP cyto | [ATP; ATP] |
c2 R2C2 cyto | [cAMP-dependent protein kinase complex] |
MAPK active cyto | [Mitogen-activated protein kinase 1] |
MODEL1912100004
— v0.0.1Its a mathematcial model explaining regulation of HIF via FIH and oxygen. Model is further validated by Experimental dat…
Details
Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1α pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1α transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1α transcriptional activity, but paradoxically decreases HIF-1α stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1α to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1α transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations. link: http://identifiers.org/pubmed/23390316
MODEL1912100005
— v0.0.1Its a mathematcial model explaining regulation of HIF via FIH and oxygen. Model is further validated by Experimental dat…
Details
Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1α pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1α transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1α transcriptional activity, but paradoxically decreases HIF-1α stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1α to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1α transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations. link: http://identifiers.org/pubmed/23390316
BIOMD0000000651
— v0.0.1Feedback regulation in cell signalling: Lessons for cancer therapeuticsThis model is described in the article: [Feedba…
Details
The notion of feedback is fundamental for understanding signal transduction networks. Feedback loops attenuate or amplify signals, change the network dynamics and modify the input-output relationships between the signal and the target. Negative feedback provides robustness to noise and adaptation to perturbations, but as a double-edged sword can prevent effective pathway inhibition by a drug. Positive feedback brings about switch-like network responses and can convert analog input signals into digital outputs, triggering cell fate decisions and phenotypic changes. We show how a multitude of protein-protein interactions creates hidden feedback loops in signal transduction cascades. Drug treatments that interfere with feedback regulation can cause unexpected adverse effects. Combinatorial molecular interactions generated by pathway crosstalk and feedback loops often bypass the block caused by targeted therapies against oncogenic mutated kinases. We discuss mechanisms of drug resistance caused by network adaptations and suggest that development of effective drug combinations requires understanding of how feedback loops modulate drug responses. link: http://identifiers.org/pubmed/26481970
Parameters:
Name | Description |
---|---|
k8r = 0.01; k8f = 0.001 | Reaction: RasGDP => RasGTP; aRTK, Rate Law: compartment*(k8f*RasGDP*aRTK-k8r*RasGTP) |
k4f = 0.001; k4r = 0.01 | Reaction: mTORC1 => amTORC1; aAkt, Rate Law: compartment*(k4f*mTORC1*aAkt-k4r*amTORC1) |
k9f = 0.001; k9r = 0.01 | Reaction: Raf => aRaf; RasGTP, Rate Law: compartment*(k9f*Raf*RasGTP-k9r*aRaf) |
k6f = 0.1; k6r = 0.001 | Reaction: IRS => iIRS; aS6K, Rate Law: compartment*(k6f*IRS*aS6K-k6r*iIRS) |
k13f = 0.1; k13r = 0.001 | Reaction: RTK => iRTK; aERK, Rate Law: compartment*(k13f*RTK*aERK-k13r*iRTK) |
k2fa = 0.001; k2f = 0.001; k2r = 0.01 | Reaction: PI3K => aPI3K; aIRS, aRTK, Rate Law: compartment*((k2f*aIRS+k2fa*aRTK)*PI3K-k2r*aPI3K) |
k10r = 0.01; k10f = 0.001 | Reaction: MEK => aMEK; aRaf, Rate Law: compartment*(k10f*MEK*aRaf-k10r*aMEK) |
k5r = 0.01; k5f = 0.001 | Reaction: S6K => aS6K; amTORC1, Rate Law: compartment*(k5f*S6K*amTORC1-k5r*aS6K) |
k7fa = 0.01; k7r = 0.01; k7f = 0.01 | Reaction: RTK => aRTK; FOXO, Rate Law: compartment*((k7f+k7fa*FOXO)*RTK-k7r*aRTK) |
k3r = 0.01; k3f = 0.001 | Reaction: Akt => aAkt; aPI3K, Rate Law: compartment*(k3f*Akt*aPI3K-k3r*aAkt) |
k16r = 0.001; k16f = 0.01 | Reaction: MEK + MEKI => iMEK, Rate Law: compartment*(k16f*MEK*MEKI-k16r*iMEK) |
k11r = 0.01; k11f = 0.001 | Reaction: ERK => aERK; aMEK, Rate Law: compartment*(k11f*ERK*aMEK-k11r*aERK) |
k14f = 0.1; k14r = 0.001 | Reaction: FOXO => iFOXO; aAkt, Rate Law: compartment*(k14f*FOXO*aAkt-k14r*iFOXO) |
k12r = 0.001; k12f = 0.01 | Reaction: Raf => iRaf; aERK, Rate Law: compartment*(k12f*Raf*aERK-k12r*iRaf) |
k15r = 0.001; k15f = 0.01 | Reaction: Akt + AktI => iAkt, Rate Law: compartment*(k15f*Akt*AktI-k15r*iAkt) |
k1f = 0.01; k1r = 0.01 | Reaction: IRS => aIRS, Rate Law: compartment*(k1f*IRS-k1r*aIRS) |
States:
Name | Description |
---|---|
iAkt | [RAC-alpha serine/threonine-protein kinase] |
Akt | [RAC-alpha serine/threonine-protein kinase] |
iIRS | [urn:miriam:sbo:SBO%3A0000015] |
RasGDP | [GTPase HRas] |
aERK | [Mitogen-activated protein kinase 3] |
iRaf | [RAF proto-oncogene serine/threonine-protein kinase] |
iMEK | [Dual specificity mitogen-activated protein kinase kinase 1] |
aRaf | [RAF proto-oncogene serine/threonine-protein kinase] |
amTORC1 | [Serine/threonine-protein kinase mTOR] |
S6K | [Ribosomal protein S6 kinase beta-1] |
RTK | [Epithelial discoidin domain-containing receptor 1] |
MEKI | [inhibitor] |
PI3K | [urn:miriam:uniprot:C17270] |
MEK | [Dual specificity mitogen-activated protein kinase kinase 1] |
AktI | [inhibitor] |
aAkt | [RAC-alpha serine/threonine-protein kinase] |
IRS | [Insulin receptor substrate 1] |
iRTK | [Epithelial discoidin domain-containing receptor 1] |
aS6K | [Ribosomal protein S6 kinase beta-1] |
aIRS | [Insulin receptor substrate 1] |
aMEK | [Dual specificity mitogen-activated protein kinase kinase 1] |
mTORC1 | [Serine/threonine-protein kinase mTOR] |
Raf | [RAF proto-oncogene serine/threonine-protein kinase] |
FOXO | [Forkhead box protein O1] |
RasGTP | [GTPase HRas] |
aRTK | [Epithelial discoidin domain-containing receptor 1] |
ERK | [Mitogen-activated protein kinase 3] |
iFOXO | [Forkhead box protein O1] |
aPI3K | [Phosphatidylinositol-4,5-Bisphosphate 3-Kinase] |
BIOMD0000000622
— v0.0.1NguyenLK2011 - Ubiquitination dynamics in Ring1B-Bmi1 systemThis theoretical model investigates the dynamics of Ring1B/B…
Details
In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7. link: http://identifiers.org/pubmed/22194680
Parameters:
Name | Description |
---|---|
k1=2.0; k2=0.2 | Reaction: Bmi1 + R1B => Z, Rate Law: compartment*(k1*Bmi1*R1B-k2*Z) |
v=7.5E-6 | Reaction: => R1B, Rate Law: compartment*v |
k1=0.02; k2=0.2 | Reaction: Z => Zub, Rate Law: compartment*Z*(k1*Z+k2*Zub) |
k=0.001 | Reaction: R1Bubd => R1B; USP7tot, Rate Law: compartment*k*USP7tot*R1Bubd |
k1=0.2; k2=0.2 | Reaction: R1B => R1Bub, Rate Law: compartment*R1B*(k1*R1B+k2*R1Bub) |
k1=3.0E-5 | Reaction: R1Bubd =>, Rate Law: compartment*k1*R1Bubd |
k=0.005 | Reaction: R1Buba => R1B; USP7tot, Rate Law: compartment*k*USP7tot*R1Buba |
k1=0.002; k2=2.0; k3=0.2 | Reaction: H2A => H2Auba; R1Bub, Zub, R1Buba, Rate Law: compartment*H2A*(k1*R1Bub+k2*Zub+k3*R1Buba) |
k1=0.01 | Reaction: R1B => R1Bubd, Rate Law: compartment*k1*R1B |
kc=0.005; Km=0.0025 | Reaction: Zub => Z; USP7tot, Rate Law: compartment*kc*USP7tot*Zub/(Km+Zub) |
k1=0.002 | Reaction: Bmi1 => Bmi1ubd, Rate Law: compartment*k1*Bmi1 |
k=0.0075 | Reaction: R1Bub => R1B; USP7tot, Rate Law: compartment*k*USP7tot*R1Bub |
k1=0.012; k2=2.0E-5 | Reaction: Zub => R1Buba + Bmi1, Rate Law: compartment*(k1*Zub-k2*R1Buba*Bmi1) |
States:
Name | Description |
---|---|
Bmi1ubd | [Polycomb complex protein BMI-1] |
R1Bubd | [E3 ubiquitin-protein ligase RING1] |
Z | [Polycomb complex protein BMI-1; E3 ubiquitin-protein ligase RING1] |
Bmi1 | [Polycomb complex protein BMI-1] |
R1Buba | [E3 ubiquitin-protein ligase RING1] |
R1Bub | [E3 ubiquitin-protein ligase RING1] |
H2A | [Histone H2AX] |
Zub | [E3 ubiquitin-protein ligase RING1; Polycomb complex protein BMI-1] |
H2Auba | [Histone H2AX] |
R1B | [E3 ubiquitin-protein ligase RING1] |
MODEL8687196544
— v0.0.1This a model from the article: A quantitative analysis of cardiac myocyte relaxation: a simulation study. Niederer S…
Details
The determinants of relaxation in cardiac muscle are poorly understood, yet compromised relaxation accompanies various pathologies and impaired pump function. In this study, we develop a model of active contraction to elucidate the relative importance of the [Ca2+]i transient magnitude, the unbinding of Ca2+ from troponin C (TnC), and the length-dependence of tension and Ca2+ sensitivity on relaxation. Using the framework proposed by one of our researchers, we extensively reviewed experimental literature, to quantitatively characterize the binding of Ca2+ to TnC, the kinetics of tropomyosin, the availability of binding sites, and the kinetics of crossbridge binding after perturbations in sarcomere length. Model parameters were determined from multiple experimental results and modalities (skinned and intact preparations) and model results were validated against data from length step, caged Ca2+, isometric twitches, and the half-time to relaxation with increasing sarcomere length experiments. A factorial analysis found that the [Ca2+]i transient and the unbinding of Ca2+ from TnC were the primary determinants of relaxation, with a fivefold greater effect than that of length-dependent maximum tension and twice the effect of tension-dependent binding of Ca2+ to TnC and length-dependent Ca2+ sensitivity. The affects of the [Ca2+]i transient and the unbinding rate of Ca2+ from TnC were tightly coupled with the effect of increasing either factor, depending on the reference [Ca2+]i transient and unbinding rate. link: http://identifiers.org/pubmed/16339881
BIOMD0000000042
— v0.0.1This model was automatically converted from model BIOMD0000000042 by using [libSBML](http://sbml.org/Software/libSBML)…
Details
We report sustained oscillations in glycolysis conducted in an open system (a continuous-flow, stirred tank reactor; CSTR) with inflow of yeast extract as well as glucose. Depending on the operating conditions, we observe simple or complex periodic oscillations or chaos. We report the response of the system to instantaneous additions of small amounts of several substrates as functions of the amount added and the phase of the addition. We simulate oscillations and perturbations by a kinetic model based on the mechanism of glycolysis in a CSTR. We find that the response to particular perturbations forms an efficient tool for elucidating the mechanism of biochemical oscillations. link: http://identifiers.org/pubmed/17029704
Parameters:
Name | Description |
---|---|
V2 = 1.5; K2 = 0.0016; k2 = 0.017; K2ATP = 0.01 | Reaction: F6P + ATP => FBP + ADP; AMP, Rate Law: compartment*V2*ATP*F6P^2/((K2*(1+k2*(ATP/AMP)^2)+F6P^2)*(K2ATP+ATP)) |
k9f = 10.0; k9b = 10.0 | Reaction: AMP + ATP => ADP, Rate Law: compartment*(k9f*AMP*ATP-k9b*ADP^2) |
k3b = 50.0; k3f = 1.0 | Reaction: FBP => GAP, Rate Law: compartment*(k3f*FBP-k3b*GAP^2) |
V4 = 10.0; K4GAP = 1.0; K4NAD = 1.0 | Reaction: GAP + NAD => DPG + NADH, Rate Law: compartment*V4*NAD*GAP/((K4GAP+GAP)*(K4NAD+NAD)) |
K1GLC = 0.1; V1 = 0.5; K1ATP = 0.063 | Reaction: GLC + ATP => F6P + ADP, Rate Law: compartment*V1*ATP*GLC/((K1GLC+GLC)*(K1ATP+ATP)) |
flow = 0.011 | Reaction: => ATP, Rate Law: compartment*(3.5-ATP)*flow |
V6 = 10.0; K6ADP = 0.3; K6PEP = 0.2 | Reaction: PEP + ADP => PYR + ATP, Rate Law: compartment*V6*ADP*PEP/((K6PEP+PEP)*(K6ADP+ADP)) |
k5f = 1.0; k5b = 0.5 | Reaction: DPG + ADP => PEP + ATP, Rate Law: compartment*(k5f*DPG*ADP-k5b*PEP*ATP) |
k8b = 1.43E-4; k8f = 1.0 | Reaction: ACA + NADH => EtOH + NAD, Rate Law: compartment*(k8f*NADH*ACA-k8b*NAD*EtOH) |
V7 = 2.0; K7PYR = 0.3 | Reaction: PYR => ACA, Rate Law: compartment*V7*PYR/(K7PYR+PYR) |
k10 = 0.05 | Reaction: F6P => P, Rate Law: compartment*k10*F6P |
States:
Name | Description |
---|---|
ATP | [ATP; ATP] |
DPG | [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate] |
NADH | [NADH; NADH] |
P | P |
PYR | [pyruvate; Pyruvate] |
EtOH | [ethanol; Ethanol] |
FBP | [keto-D-fructose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate] |
GLC | [glucose; C00293] |
F6P | [CHEBI_20935; beta-D-Fructose 6-phosphate] |
AMP | [AMP; AMP] |
ACA | [acetaldehyde; Acetaldehyde] |
GAP | [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate] |
PEP | [phosphoenolpyruvate; Phosphoenolpyruvate] |
ADP | [ADP; ADP] |
NAD | [NAD(+); NAD+] |
BIOMD0000000213
— v0.0.1This is an SBML version of the folate cycle model model from: **A mathematical model of the folate cycle: new insights…
Details
A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the "methyl trap"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased. link: http://identifiers.org/pubmed/15496403
Parameters:
Name | Description |
---|---|
MTCH_VmaxF = 800000.0; MTCH_VmaxR = 20000.0; MTCH_Km_5_10_CHTHF = 250.0; MTCH_Km_10fTHF = 100.0 | Reaction: _5_10_CHTHF => _10fTHF, Rate Law: MTCH_VmaxF*_5_10_CHTHF/(MTCH_Km_5_10_CHTHF+_5_10_CHTHF)-MTCH_VmaxR*_10fTHF/(MTCH_Km_10fTHF+_10fTHF) |
FTD_Vmax = 14000.0; FTD_Km_10fTHF = 20.0 | Reaction: _10fTHF => THF, Rate Law: FTD_Vmax*_10fTHF/(FTD_Km_10fTHF+_10fTHF) |
DHFR_Km_NADPH = 4.0; DHFR_Vmax = 50.0; DHFR_Km_DHF = 0.5 | Reaction: DHF => THF; NADPH, Rate Law: DHFR_Vmax*NADPH/(DHFR_Km_NADPH+NADPH)*DHF/(DHFR_Km_DHF+DHF) |
AICART_Vmax = 45000.0; AICART_Km_10fTHF = 5.9; AICART_Km_AICAR = 100.0 | Reaction: _10fTHF => THF; AICAR, Rate Law: AICART_Vmax*AICAR/(AICART_Km_AICAR+AICAR)*_10fTHF/(AICART_Km_10fTHF+_10fTHF) |
MTD_VmaxR = 594000.0; MTD_Km_5_10_CHTHF = 10.0; MTD_VmaxF = 200000.0; MTD_Km_5_10_CH2THF = 2.0 | Reaction: _5_10_CH2THF => _5_10_CHTHF, Rate Law: MTD_VmaxF*_5_10_CH2THF/(MTD_Km_5_10_CH2THF+_5_10_CH2THF)-MTD_VmaxR*_5_10_CHTHF/(MTD_Km_5_10_CHTHF+_5_10_CHTHF) |
MS_Vmax = 500.0; MS_Km_Hcy = 0.1; MS_Km_5mTHF = 25.0; MS_Kd = 1.0 | Reaction: _5mTHF => THF; Hcy, Rate Law: MS_Vmax*_5mTHF/MS_Km_5mTHF*Hcy/MS_Km_Hcy/(MS_Kd/MS_Km_5mTHF+_5mTHF/MS_Km_5mTHF+Hcy/MS_Km_Hcy+_5mTHF*Hcy/(MS_Km_5mTHF*MS_Km_Hcy)) |
PGT_Km_10fTHF = 4.9; PGT_Km_GAR = 520.0; PGT_Vmax = 16200.0 | Reaction: _10fTHF => THF; GAR, Rate Law: PGT_Vmax*GAR/(PGT_Km_GAR+GAR)*_10fTHF/(PGT_Km_10fTHF+_10fTHF) |
TS_Km_dUMP = 6.3; TS_Vmax = 50.0; TS_Km_5_10_CH2THF = 14.0 | Reaction: _5_10_CH2THF => DHF; dUMP, Rate Law: TS_Vmax*dUMP/(TS_Km_dUMP+dUMP)*_5_10_CH2THF/(TS_Km_5_10_CH2THF+_5_10_CH2THF) |
NE_k2 = 12.0; NE_k1 = 0.15 | Reaction: THF => _5_10_CH2THF; HCOOH, Rate Law: HCOOH*NE_k1*THF-NE_k2*_5_10_CH2THF |
FTS_Km_HCOOH = 43.0; FTS_Km_THF = 3.0; FTS_Vmax = 2000.0 | Reaction: THF => _10fTHF; HCOOH, Rate Law: FTS_Vmax*HCOOH/(FTS_Km_HCOOH+HCOOH)*THF/(FTS_Km_THF+THF) |
SHMT_Km_Ser = 600.0; SHMT_Km_THF = 50.0; SHMT_VmaxR = 25000.0; SHMT_VmaxF = 40000.0 | Reaction: THF => _5_10_CH2THF; Ser, Gly, Rate Law: SHMT_VmaxF*Ser/(SHMT_Km_Ser+Ser)*THF/(SHMT_Km_THF+THF)-SHMT_VmaxR*Gly/(SHMT_Km_Ser+Gly)*_5_10_CH2THF/(SHMT_Km_THF+_5_10_CH2THF) |
MTHFR_Km_5_10_CH2THF = 50.0; MTHFR_Vmax = 6000.0; MTHFR_Km_NADPH = 16.0 | Reaction: _5_10_CH2THF => _5mTHF; NADPH, Rate Law: MTHFR_Vmax*NADPH/(MTHFR_Km_NADPH+NADPH)*_5_10_CH2THF/(MTHFR_Km_5_10_CH2THF+_5_10_CH2THF) |
States:
Name | Description |
---|---|
10fTHF | [10-formyltetrahydrofolic acid; 10-Formyltetrahydrofolate] |
5mTHF | [5-methyltetrahydrofolic acid; 5-Methyltetrahydrofolate] |
5 10 CHTHF | [(6R)-5,10-methenyltetrahydrofolic acid; 5,10-Methenyltetrahydrofolate] |
5 10 CH2THF | [(6R)-5,10-methylenetetrahydrofolic acid; 5,10-Methylenetetrahydrofolate] |
THF | [(6S)-5,6,7,8-tetrahydrofolic acid; Tetrahydrofolate] |
DHF | [dihydrofolic acid; Dihydrofolate] |
MODEL6655501972
— v0.0.1This is an SBML version of the folate cycle model model from: **A mathematical model of the folate cycle: new insights…
Details
A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the "methyl trap"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased. link: http://identifiers.org/pubmed/15496403
MODEL1007200000
— v0.0.1This is the model described in the article: In silico experimentation with a model of hepatic mitochondrial folate met…
Details
In eukaryotes, folate metabolism is compartmentalized and occurs in both the cytosol and the mitochondria. The function of this compartmentalization and the great changes that occur in the mitochondrial compartment during embryonic development and in rapidly growing cancer cells are gradually becoming understood, though many aspects remain puzzling and controversial.We explore the properties of cytosolic and mitochondrial folate metabolism by experimenting with a mathematical model of hepatic one-carbon metabolism. The model is based on known biochemical properties of mitochondrial and cytosolic enzymes. We use the model to study questions about the relative roles of the cytosolic and mitochondrial folate cycles posed in the experimental literature. We investigate: the control of the direction of the mitochondrial and cytosolic serine hydroxymethyltransferase (SHMT) reactions, the role of the mitochondrial bifunctional enzyme, the role of the glycine cleavage system, the effects of variations in serine and glycine inputs, and the effects of methionine and protein loading.The model reproduces many experimental findings and gives new insights into the underlying properties of mitochondrial folate metabolism. Particularly interesting is the remarkable stability of formate production in the mitochondria in the face of large changes in serine and glycine input. The model shows that in the presence of the bifunctional enzyme (as in embryonic tissues and cancer cells), the mitochondria primarily support cytosolic purine and pyrimidine synthesis via the export of formate, while in adult tissues the mitochondria produce serine for gluconeogenesis. link: http://identifiers.org/pubmed/17150100
BIOMD0000000871
— v0.0.1This model represents NIK-dependent p100 processing into p52 followed by binding to RelB and NIK-dependent IkBd degradat…
Details
Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose–response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis–Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context. link: http://identifiers.org/doi/10.1073/pnas.1816000116
Parameters:
Name | Description |
---|---|
k1=0.0228 | Reaction: RelB =>, Rate Law: compartment*k1*RelB |
k=1000.0; Kd=50.0; canon = 1.0 | Reaction: => p100, Rate Law: compartment*k*canon/(Kd+canon) |
k1=0.05 | Reaction: p100_NIK => p52 + NIK, Rate Law: compartment*k1*p100_NIK |
k2=0.00144; k1=9.6E-4 | Reaction: RelB + p52 => RelB_p52, Rate Law: compartment*(k1*RelB*p52-k2*RelB_p52) |
k1=1.6E-5; k2=2.4E-4 | Reaction: p100 => IkBd, Rate Law: compartment*(k1*p100^2-k2*IkBd) |
k1=3.8E-4 | Reaction: IkBd =>, Rate Law: compartment*k1*IkBd |
k1=0.005; k2=2.4E-4 | Reaction: IkBd + NIK => IkBd_NIK, Rate Law: compartment*(k1*IkBd*NIK-k2*IkBd_NIK) |
k=42.0; Kd=50.0; canon = 1.0 | Reaction: => RelB, Rate Law: compartment*k*canon/(Kd+canon) |
States:
Name | Description |
---|---|
IkBd | [Nuclear factor NF-kappa-B p100 subunit] |
NIK | [Mitogen-activated protein kinase kinase kinase 14] |
p100 NIK | [Nuclear factor NF-kappa-B p100 subunit; Mitogen-activated protein kinase kinase kinase 14] |
p52 | [Nuclear factor NF-kappa-B p100 subunit] |
IkBd NIK | [Nuclear factor NF-kappa-B p100 subunit; Mitogen-activated protein kinase kinase kinase 14] |
RelB | [Transcription factor RelB] |
p100 | [Nuclear factor NF-kappa-B p100 subunit] |
RelB p52 | [Nuclear factor NF-kappa-B p100 subunit; Transcription factor RelB] |
BIOMD0000000291
— v0.0.1This a model from the article: Mathematical model of binding of albumin-bilirubin complex to the surface of carbon p…
Details
We proposed a mathematical model and estimated the parameters of adsorption of albumin-bilirubin complex to the surface of carbon pyropolymer. Design data corresponded to the results of experimental studies. Our findings indicate that modeling of this process should take into account fractal properties of the surface of carbon pyropolymer. link: http://identifiers.org/pubmed/16307060
Parameters:
Name | Description |
---|---|
k6 = 3.226E-7; k8 = 1.011E-7; k9 = 0.01685; k10 = 0.1325; n = 1.0 | Reaction: x2 = (k6*x7*x6-k8*x2)+k9*x1*x7^(n+1)+k10*x4*x7, Rate Law: (k6*x7*x6-k8*x2)+k9*x1*x7^(n+1)+k10*x4*x7 |
k7 = 0.00301; k5 = 0.005489; k9 = 0.01685; n = 1.0 | Reaction: x3 = (k5*x7^n*x5-k7*x3)+k9*x1*x7^(n+1), Rate Law: (k5*x7^n*x5-k7*x3)+k9*x1*x7^(n+1) |
K_AlB = 95000.0; K_AlB2 = 3000.0; k3 = 5.095E-6; k9 = 0.01685; k10 = 0.1325; k4 = 2.656E-5; n = 1.0 | Reaction: x1 = (((K_AlB*k3*x5*x6-K_AlB2*k4*x1*x6)-k3*x1)-k9*x1*x7^(n+1))+k4*x4+k10*x4*x7, Rate Law: (((K_AlB*k3*x5*x6-K_AlB2*k4*x1*x6)-k3*x1)-k9*x1*x7^(n+1))+k4*x4+k10*x4*x7 |
K_AlB2 = 3000.0; k10 = 0.1325; k4 = 2.656E-5 | Reaction: x4 = (K_AlB2*k4*x1*x6-k4*x4)-k10*x4*x7, Rate Law: (K_AlB2*k4*x1*x6-k4*x4)-k10*x4*x7 |
n = 1.0 | Reaction: x7 = (C0-x2)-n*x3, Rate Law: missing |
States:
Name | Description |
---|---|
x5 | [Serum albumin] |
x1 | [Serum albumin; Bilirubin] |
x7 | [macromolecule; carbon atom] |
x4 | [Serum albumin; Bilirubin] |
x2 | [macromolecule; carbon atom; Bilirubin] |
x6 | [bilirubin; Bilirubin] |
x3 | [macromolecule; carbon atom; Serum albumin] |
BIOMD0000000865
— v0.0.1This is a mathematical mechanistic immunobiochemical model that incorporates T cell pathways that control programmed cel…
Details
It was recently reported that acute influenza infection of the lung promoted distal melanoma growth in the dermis of mice. Melanoma-specific CD8+ T cells were shunted to the lung in the presence of the infection, where they expressed high levels of inflammation-induced cell-activation blocker PD-1, and became incapable of migrating back to the tumor site. At the same time, co-infection virus-specific CD8+ T cells remained functional while the infection was cleared. It was also unexpectedly found that PD-1 blockade immunotherapy reversed this effect. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates T cell pathways that control PD-1 expression. A core component of our model is a kinetic motif, which we call a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6. The different activity levels of the PD-1 DIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes. Collectively, the model allowed us to put forward a few working hypotheses as follows: (i) the melanoma-specific CD8+ T cells re-circulating with the blood flow enter the lung where they express high levels of inflammation-induced cell-activation blocker PD-1 in the presence of infection; (ii) when PD-1 receptors interact with abundant PD-L1, constitutively expressed in the lung, T cells loose motility; (iii) at the same time, virus-specific cells adapt to strong stimulation by their cognate antigen by lowering the transiently-elevated expression of PD-1, remaining functional and mobile in the inflamed lung, while the infection is cleared. The role that T cell receptor (TCR) activation and feedback loops play in the underlying processes are also highlighted and discussed. We hope that the results reported in our study could potentially contribute to the advancement of immunological approaches to cancer treatment and, as well, to a better understanding of a broader complexity of fundamental interactions between pathogens and tumors. link: http://identifiers.org/pubmed/30745900
Parameters:
Name | Description |
---|---|
U_a_k_P = 0.0215814688039663; n_b = 2.0; a_b = 100.0; A_b = 10.0; r_b = 2.0; m_b = 2.0; K_b = 1.0; M_b = 10.0; k_b = 25.0 | Reaction: => B; I, C, Rate Law: compartment*(a_b*U_a_k_P^n_b/(A_b^n_b+U_a_k_P^n_b)+k_b*I^m_b/(K_b^m_b+I^m_b))*M_b^r_b/(M_b^r_b+C^r_b) |
mu_b = 1.0 | Reaction: B =>, Rate Law: compartment*mu_b*B |
U_a_k_P = 0.0215814688039663; n_c = 3.0; A_c = 0.01; r_c = 2.0; M_c = 10.0; a_c = 0.75 | Reaction: => C; B, I, Rate Law: compartment*a_c*U_a_k_P^n_c/(A_c^n_c+U_a_k_P^n_c)*M_c^r_c/(M_c^r_c+B^r_c+I^r_c+C^r_c) |
mu_c = 0.1 | Reaction: C =>, Rate Law: compartment*mu_c*C |
mu_p = 0.1 | Reaction: P =>, Rate Law: compartment*mu_p*P |
mu_i = 1.0 | Reaction: I =>, Rate Law: compartment*mu_i*I |
U_a_k_P = 0.0215814688039663; n_p = 3.0; sigma_p_tilde = 0.1; A_p = 0.1; a_p = 0.75; M_p = 10.0; r_p = 4.0 | Reaction: => P; B, Rate Law: compartment*(sigma_p_tilde+a_p*U_a_k_P^n_p/(A_p^n_p+U_a_k_P^n_p))*M_p^r_p/(M_p^r_p+B^r_p) |
U_a_k_P = 0.0215814688039663; Q_i = 1.0; n_i = 2.0; a_i = 75.0; k_i = 7.5; m_i = 2.0; q_i = 7.5; s_i = 2.0; sigma_i = 0.3; K_i = 1.0; Phi_L_P = 1.0; A_i = 1.0 | Reaction: => I; B, I, Rate Law: compartment*Phi_L_P*(sigma_i+a_i*U_a_k_P^n_i/(A_i^n_i+U_a_k_P^n_i)+k_i*B^m_i/(K_i^m_i+B^m_i)+q_i*I^s_i/(Q_i^s_i+I^s_i)) |
States:
Name | Description |
---|---|
B | [PR:000001831] |
I | [C17926] |
C | [C26149] |
P | [PR:000001919] |
MODEL1908270001
— v0.0.1This is a mathematical model investigating the effects of continuous and intermittent PD-L1 and anti-PD-L1 therapy upon…
Details
The use of immune checkpoint inhibitors is becoming more commonplace in clinical trials across the nation. Two important factors in the tumour-immune response are the checkpoint protein programmed death-1 (PD-1) and its ligand PD-L1. We propose a mathematical tumour-immune model using a system of ordinary differential equations to study dynamics with and without the use of anti-PD-1. A sensitivity analysis is conducted, and series of simulations are performed to investigate the effects of intermittent and continuous treatments on the tumour-immune dynamics. We consider the system without the anti-PD-1 drug to conduct a mathematical analysis to determine the stability of the tumour-free and tumorous equilibria. Through simulations, we found that a normally functioning immune system may control tumour. We observe treatment with anti-PD-1 alone may not be sufficient to eradicate tumour cells. Therefore, it may be beneficial to combine single agent treatments with additional therapies to obtain a better antitumour response. link: http://identifiers.org/doi/10.1080/23737867.2018.1440978
BIOMD0000000571
— v0.0.1Nishio2008 - Design of the phosphotransferase system for enhanced glucose uptake in E. coli.This model is described in t…
Details
The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate. link: http://identifiers.org/pubmed/18197177
Parameters:
Name | Description |
---|---|
fast = 1.0E9 (60*s)^(-1); Kb=40000.0 mol^(-1)*l; one_per_M=1.0 mol^(-1)*l | Reaction: CRP + cAMP => CRP_cAMP; CRP, cAMP, CRP_cAMP, Rate Law: cyt*fast*one_per_M*(Kb^2*(CRP*cAMP)^2-CRP_cAMP^2) |
kmd=0.0866 (60*s)^(-1) | Reaction: mRNA_crr => ; mRNA_crr, Rate Law: cyt*kmd*mRNA_crr |
fast = 1.0E9 (60*s)^(-1); Kb=2.0E8 mol^(-1)*l | Reaction: Mlc + Mlcsite_ptsGp1 => Mlc_Mlcsite_ptsGp1; Mlc, Mlcsite_ptsGp1, Mlc_Mlcsite_ptsGp1, Rate Law: cyt*fast*(Kb*Mlc*Mlcsite_ptsGp1-Mlc_Mlcsite_ptsGp1) |
fast = 1.0E9 (60*s)^(-1); Kb=2.22E7 mol^(-1)*l | Reaction: CRP_cAMP + CRPsiteI_crp => CRP_cAMP_CRPsiteI_crp; CRP_cAMP, CRPsiteI_crp, CRP_cAMP_CRPsiteI_crp, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsiteI_crp-CRP_cAMP_CRPsiteI_crp) |
Kmich=9.61 mol*l^(-1); Q=389.0 (60*s)^(-1) | Reaction: IICB + Glc6P => IICB_P + Glucose; IICB, Glc6P, Rate Law: cyt*Q*IICB*Glc6P/(Kmich+Glc6P) |
kmd=0.0889 (60*s)^(-1) | Reaction: mRNA_ptsH => ; mRNA_ptsH, Rate Law: cyt*kmd*mRNA_ptsH |
Q=4800.0 (60*s)^(-1); Kmich=2.0E-5 mol*l^(-1) | Reaction: IICB_P + Glucose => IICB + Glc6P; IICB_P, Glucose, Rate Law: cyt*Q*IICB_P*Glucose/(Kmich+Glucose) |
km=0.892 (60*s)^(-1); TCRPsite_ptsIp1 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_ptsI; CRP_cAMP_CRPsite_ptsIp1, ptsIp1, CRP_cAMP_CRPsite_ptsIp1, ptsIp1, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsIp1/TCRPsite_ptsIp1*ptsIp1 |
Kmich=0.001 mol*l^(-1); Q=100.0 (60*s)^(-1) | Reaction: ATP => cAMP; CYA, CYA, ATP, Rate Law: cyt*Q*CYA*ATP/(Kmich+ATP) |
TCRPsite_ptsGp2 = 2.43E-10 mol*l^(-1); km=2.0 (60*s)^(-1); TMlcsite_ptsGp2 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_ptsG; CRP_cAMP_CRPsite_ptsGp2, Mlc_Mlcsite_ptsGp2, ptsGp2, CRP_cAMP_CRPsite_ptsGp2, Mlc_Mlcsite_ptsGp2, ptsGp2, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsGp2/TCRPsite_ptsGp2*(1-Mlc_Mlcsite_ptsGp2/TMlcsite_ptsGp2)*ptsGp2 |
fast = 1.0E9 (60*s)^(-1); Kb=2430000.0 mol^(-1)*l | Reaction: Mlc + Mlcsite_mlcp1 => Mlc_Mlcsite_mlcp1; Mlc, Mlcsite_mlcp1, Mlc_Mlcsite_mlcp1, Rate Law: cyt*fast*(Kb*Mlc*Mlcsite_mlcp1-Mlc_Mlcsite_mlcp1) |
Kb=7000000.0 mol^(-1)*l; fast = 1.0E9 (60*s)^(-1) | Reaction: IICB + Mlc => IICB_Mlc; IICB, Mlc, IICB_Mlc, Rate Law: cyt*fast*(Kb*IICB*Mlc-IICB_Mlc) |
Kb=1350000.0 mol^(-1)*l; fast = 1.0E9 (60*s)^(-1) | Reaction: Mlc + Mlcsite_mlcp2 => Mlc_Mlcsite_mlcp2; Mlc, Mlcsite_mlcp2, Mlc_Mlcsite_mlcp2, Rate Law: cyt*fast*(Kb*Mlc*Mlcsite_mlcp2-Mlc_Mlcsite_mlcp2) |
fast = 1.0E9 (60*s)^(-1); Kb=1.0E8 mol^(-2)*l^2 | Reaction: CYA + IIA_P => IIA_P_CYA; CYA, IIA_P, IIA_P_CYA, Rate Law: cyt*fast*(Kb*CYA*IIA_P^2-IIA_P_CYA) |
kx=2.4E8 mol^(-1)*l*(60*s)^(-1) | Reaction: IICB_P + IIA => IICB + IIA_P; IIA, IICB_P, Rate Law: cyt*kx*IIA*IICB_P |
fast = 1.0E9 (60*s)^(-1); Kb=2700000.0 mol^(-1)*l | Reaction: CRP_cAMP + CRPsiteII_crp => CRP_cAMP_CRPsiteII_crp; CRP_cAMP, CRPsiteII_crp, CRP_cAMP_CRPsiteII_crp, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsiteII_crp-CRP_cAMP_CRPsiteII_crp) |
kx=6.6E8 mol^(-1)*l*(60*s)^(-1) | Reaction: IICB + IIA_P => IICB_P + IIA; IICB, IIA_P, Rate Law: cyt*kx*IICB*IIA_P |
kp=11.0 (60*s)^(-1) | Reaction: => CRP; mRNA_crp, mRNA_crp, Rate Law: cyt*kp*mRNA_crp |
kpd=0.1 (60*s)^(-1) | Reaction: CRP_cAMP_CRPsite_ptsIp1 => CRPsite_ptsIp1; CRP_cAMP_CRPsite_ptsIp1, Rate Law: cyt*kpd*CRP_cAMP_CRPsite_ptsIp1 |
kmd=0.0797 (60*s)^(-1) | Reaction: mRNA_ptsI => ; mRNA_ptsI, Rate Law: cyt*kmd*mRNA_ptsI |
Q=480000.0 (60*s)^(-1); Kmich=0.002 mol*l^(-1) | Reaction: EI_P + Pyr => EI + PEP; EI_P, Pyr, Rate Law: cyt*2*Q*EI_P*Pyr^2/(Kmich^2+Pyr^2) |
TCRPsite_ptsGp1 = 2.43E-10 mol*l^(-1); km=892.0 (60*s)^(-1); TMlcsite_ptsGp1 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_ptsG; CRP_cAMP_CRPsite_ptsGp1, Mlc_Mlcsite_ptsGp1, ptsGp1, CRP_cAMP_CRPsite_ptsGp1, Mlc_Mlcsite_ptsGp1, ptsGp1, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsGp1/TCRPsite_ptsGp1*(1-Mlc_Mlcsite_ptsGp1/TMlcsite_ptsGp1)*ptsGp1 |
km=334.5 (60*s)^(-1) | Reaction: => mRNA_crr; crr, crr, Rate Law: cyt*km*crr |
kpd=400.0 (60*s)^(-1) | Reaction: cAMP => ; cAMP, Rate Law: cyt*kpd*cAMP |
kx=3.66E9 mol^(-1)*l*(60*s)^(-1) | Reaction: IIA + HPr_P => IIA_P + HPr; IIA, HPr_P, Rate Law: cyt*kx*IIA*HPr_P |
kx=4.8E8 mol^(-1)*l*(60*s)^(-1) | Reaction: HPr_P + EI => HPr + EI_P; EI, HPr_P, Rate Law: cyt*kx*EI*HPr_P |
Q=9000.0 (60*s)^(-1); Kmich=0.001 mol*l^(-1) | Reaction: ATP => cAMP; IIA_P_CYA, IIA_P_CYA, ATP, Rate Law: cyt*Q*IIA_P_CYA*ATP/(Kmich+ATP) |
kmd=0.3014 (60*s)^(-1) | Reaction: mRNA_mlc => ; mRNA_mlc, Rate Law: cyt*kmd*mRNA_mlc |
kx=1.2E10 mol^(-1)*l*(60*s)^(-1) | Reaction: HPr + EI_P => HPr_P + EI; HPr, EI_P, Rate Law: cyt*kx*HPr*EI_P |
fast = 1.0E9 (60*s)^(-1); Kb=6.67E7 mol^(-1)*l | Reaction: CRP_cAMP + CRPsite_cyaA => CRP_cAMP_CRPsite_cyaA; CRP_cAMP, CRPsite_cyaA, CRP_cAMP_CRPsite_cyaA, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsite_cyaA-CRP_cAMP_CRPsite_cyaA) |
km=1.875 (60*s)^(-1); TMlcsite_mlcp2 = 2.43E-10 mol*l^(-1); TCRPsite_mlcp2 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_mlc; CRP_cAMP_CRPsite_mlcp2, Mlc_Mlcsite_mlcp2, mlcp2, CRP_cAMP_CRPsite_mlcp2, Mlc_Mlcsite_mlcp2, mlcp2, Rate Law: cyt*km*CRP_cAMP_CRPsite_mlcp2/TCRPsite_mlcp2*(1-Mlc_Mlcsite_mlcp2/TMlcsite_mlcp2)*mlcp2 |
fast = 1.0E9 (60*s)^(-1); Kb=1.0E7 mol^(-1)*l | Reaction: CRP_cAMP + CRPsite_ptsIp1 => CRP_cAMP_CRPsite_ptsIp1; CRP_cAMP, CRPsite_ptsIp1, CRP_cAMP_CRPsite_ptsIp1, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsite_ptsIp1-CRP_cAMP_CRPsite_ptsIp1) |
km=17.95 (60*s)^(-1); TCRPsite_ptsHp1 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_ptsH; CRP_cAMP_CRPsite_ptsHp1, ptsHp1, CRP_cAMP_CRPsite_ptsHp1, ptsHp1, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsHp1/TCRPsite_ptsHp1*ptsHp1 |
kx=2.82E9 mol^(-1)*l*(60*s)^(-1) | Reaction: IIA_P + HPr => IIA + HPr_P; HPr, IIA_P, Rate Law: cyt*kx*HPr*IIA_P |
km=6.244 (60*s)^(-1); TCRPsite_ptsIp0 = 2.43E-10 mol*l^(-1); TMlcsite_ptsIp0 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_ptsI; CRP_cAMP_CRPsite_ptsIp0, Mlc_Mlcsite_ptsIp0, ptsIp0, CRP_cAMP_CRPsite_ptsIp0, Mlc_Mlcsite_ptsIp0, ptsIp0, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsIp0/TCRPsite_ptsIp0*(1-Mlc_Mlcsite_ptsIp0/TMlcsite_ptsIp0)*ptsIp0 |
km=1.875 (60*s)^(-1); TCRPsite_mlcp1 = 2.43E-10 mol*l^(-1); TMlcsite_mlcp1 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_mlc; CRP_cAMP_CRPsite_mlcp1, Mlc_Mlcsite_mlcp1, mlcp1, CRP_cAMP_CRPsite_mlcp1, Mlc_Mlcsite_mlcp1, mlcp1, Rate Law: cyt*km*(1-CRP_cAMP_CRPsite_mlcp1/TCRPsite_mlcp1)*(1-Mlc_Mlcsite_mlcp1/TMlcsite_mlcp1)*mlcp1 |
kmd=0.217 (60*s)^(-1) | Reaction: mRNA_ptsG => ; mRNA_ptsG, Rate Law: cyt*kmd*mRNA_ptsG |
km=71.8 (60*s)^(-1); TCRPsite_ptsHp0 = 2.43E-10 mol*l^(-1); TMlcsite_ptsHp0 = 2.43E-10 mol*l^(-1) | Reaction: => mRNA_ptsH; CRP_cAMP_CRPsite_ptsHp0, Mlc_Mlcsite_ptsHp0, ptsHp0, CRP_cAMP_CRPsite_ptsHp0, Mlc_Mlcsite_ptsHp0, ptsHp0, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsHp0/TCRPsite_ptsHp0*(1-Mlc_Mlcsite_ptsHp0/TMlcsite_ptsHp0)*ptsHp0 |
Q=108000.0 (60*s)^(-1); Kmich=3.0E-4 mol*l^(-1) | Reaction: EI + PEP => EI_P + Pyr; EI, PEP, Rate Law: cyt*2*Q*EI*PEP^2/(Kmich^2+PEP^2) |
States:
Name | Description |
---|---|
Mlc Mlcsite ptsIp0 | [Protein mlc; Protein mlc] |
CYA | [Adenylate cyclase] |
mRNA ptsI | [messenger RNA] |
HPr P | [Phosphocarrier protein HPr] |
CRPsite mlcp1 | [cAMP-activated global transcriptional regulator CRP; Protein mlc] |
CRP cAMP | [cAMP-activated global transcriptional regulator CRP; 3',5'-cyclic AMP] |
IICB | [Fused glucose-specific PTS enzymes: IIB component/IIC component2.7.1.69PTS glucose EIICB componentPTS glucose transporter subunit IIBCPTS glucose-specific subunit IIBCPTS system glucose-specific EIIBC component2.7.1.191PTS system glucose-specific EIICB componentPTS system glucose-specific transporter subunit IIBCPTS system, glucose-specific IIBC componentPtsG] |
mRNA ptsH | [messenger RNA] |
cAMP | [3',5'-cyclic AMP] |
Mlc Mlcsite ptsGp2 | [Protein mlc; Protein mlc] |
Mlc Mlcsite ptsGp1 | [Protein mlc; Protein mlc] |
CRPsiteII crp | [cAMP-activated global transcriptional regulator CRP] |
CRP cAMP CRPsiteII crp | [cAMP-activated global transcriptional regulator CRP; 3',5'-cyclic AMP] |
EI | [Phosphoenolpyruvate-protein phosphotransferase] |
mRNA mlc | [messenger RNA] |
HPr | [Phosphocarrier protein HPr] |
Pyr | [pyruvic acid] |
Mlcsite mlcp1 | [Protein mlc] |
EI P | [Phosphoenolpyruvate-protein phosphotransferase] |
IICB Mlc | [Fused glucose-specific PTS enzymes: IIB component/IIC component2.7.1.69PTS glucose EIICB componentPTS glucose transporter subunit IIBCPTS glucose-specific subunit IIBCPTS system glucose-specific EIIBC component2.7.1.191PTS system glucose-specific EIICB componentPTS system glucose-specific transporter subunit IIBCPTS system, glucose-specific IIBC componentPtsG; Protein mlc] |
CRPsiteI crp | [cAMP-activated global transcriptional regulator CRP] |
IIA | [PTS system glucose-specific EIIA component] |
Mlc Mlcsite mlcp2 | [Protein mlc; Protein mlc] |
Mlc Mlcsite ptsHp0 | [Protein mlc; Protein mlc] |
IICB P | [Fused glucose-specific PTS enzymes: IIB component/IIC component2.7.1.69PTS glucose EIICB componentPTS glucose transporter subunit IIBCPTS glucose-specific subunit IIBCPTS system glucose-specific EIIBC component2.7.1.191PTS system glucose-specific EIICB componentPTS system glucose-specific transporter subunit IIBCPTS system, glucose-specific IIBC componentPtsG] |
CRPsite mlcp2 | [cAMP-activated global transcriptional regulator CRP; Protein mlc] |
CRP | [cAMP-activated global transcriptional regulator CRP] |
Mlc | [Protein mlc] |
PEP | [phosphoenolpyruvic acid] |
CRPsite ptsIp1 | [cAMP-activated global transcriptional regulator CRP; Phosphoenolpyruvate-protein phosphotransferase] |
mRNA ptsG | [messenger RNA] |
Mlc Mlcsite mlcp1 | [Protein mlc; Protein mlc] |
mRNA crr | [messenger RNA] |
CRP cAMP CRPsite cyaA | [cAMP-activated global transcriptional regulator CRP; 3',5'-cyclic AMP] |
MODEL8686121468
— v0.0.1This a model from the article: A modification of the Hodgkin--Huxley equations applicable to Purkinje fibre action and…
Details
link: http://identifiers.org/pubmed/14480151
MODEL0406151557
— v0.0.1This a model from the article: A model of sino-atrial node electrical activity based on a modification of the DiFrance…
Details
DiFrancesco & Noble's (1984) equations (Phil. Trans. R. Soc. Lond. B (in the press.] have been modified to apply to the mammalian sino-atrial node. The modifications are based on recent experimental work. The modified equations successfully reproduce action potential and pacemaker activity in the node. Slightly different versions have been developed for peripheral regions that show a maximum diastolic potential near –75 mV and for central regions that do not hyperpolarize beyond –60 to –65 mV. Variations in extracellular potassium influence the frequency of pacemaker activity in the s.a. node model very much less than they do in the Purkinje fibre model. This corresponds well to the experimental observation that the node is less sensitive to external [K] than are Purkinje fibres. Activation of the Na-K exchange pump in the model by increasing intracellular sodium can suppress pacemaker activity. This phenomenon may contribute to the mechanism of overdrive suppression. link: http://identifiers.org/pubmed/6149553
MODEL1006230073
— v0.0.1This a model from the article: The role of sodium-calcium exchange during the cardiac action potential. Noble D, Nob…
Details
link: http://identifiers.org/pubmed/1785860
MODEL1006230089
— v0.0.1This a model from the article: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, a…
Details
The guinea-pig ventricular cell model, originally developed by Noble et al in 1991, has been greatly extended to include accumulation and depletion of calcium in a diadic space between the sarcolemma and the sarcoplasmic reticulum where, according to contempory understanding, the majority of calcium-induced calcium release is triggered. The calcium in this space is also assumed to play the major role in calcium-induced inactivation of the calcium current. Delayed potassium current equations have been developed to include the rapid (IKr) and slow (IKs) components of the delayed rectifier current based on the data of of Heath and Terrar, along with data from Sanguinetti and Jurkiewicz. Length- and tension-dependent changes in mechanical and electrophysiological processes have been incorporated as described recently by Kohl et al. Drug receptor interactions have started to be developed, using the sodium channel as the first target. The new model has been tested against experimental data on action potential clamp, and on force-interval and duration-interval relations; it has been found to reliably reproduce experimental observations. link: http://identifiers.org/pubmed/9487284
MODEL1006230080
— v0.0.1This a model from the article: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, a…
Details
The guinea-pig ventricular cell model, originally developed by Noble et al in 1991, has been greatly extended to include accumulation and depletion of calcium in a diadic space between the sarcolemma and the sarcoplasmic reticulum where, according to contempory understanding, the majority of calcium-induced calcium release is triggered. The calcium in this space is also assumed to play the major role in calcium-induced inactivation of the calcium current. Delayed potassium current equations have been developed to include the rapid (IKr) and slow (IKs) components of the delayed rectifier current based on the data of of Heath and Terrar, along with data from Sanguinetti and Jurkiewicz. Length- and tension-dependent changes in mechanical and electrophysiological processes have been incorporated as described recently by Kohl et al. Drug receptor interactions have started to be developed, using the sodium channel as the first target. The new model has been tested against experimental data on action potential clamp, and on force-interval and duration-interval relations; it has been found to reliably reproduce experimental observations. link: http://identifiers.org/pubmed/9487284
MODEL1006230063
— v0.0.1This a model from the article: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, a…
Details
The guinea-pig ventricular cell model, originally developed by Noble et al in 1991, has been greatly extended to include accumulation and depletion of calcium in a diadic space between the sarcolemma and the sarcoplasmic reticulum where, according to contempory understanding, the majority of calcium-induced calcium release is triggered. The calcium in this space is also assumed to play the major role in calcium-induced inactivation of the calcium current. Delayed potassium current equations have been developed to include the rapid (IKr) and slow (IKs) components of the delayed rectifier current based on the data of of Heath and Terrar, along with data from Sanguinetti and Jurkiewicz. Length- and tension-dependent changes in mechanical and electrophysiological processes have been incorporated as described recently by Kohl et al. Drug receptor interactions have started to be developed, using the sodium channel as the first target. The new model has been tested against experimental data on action potential clamp, and on force-interval and duration-interval relations; it has been found to reliably reproduce experimental observations. link: http://identifiers.org/pubmed/9487284
MODEL1507180068
— v0.0.1Nogales2008 - Genome-scale metabolic network of Pseudomonas putida (iJN746)This model is described in the article: [A g…
Details
BACKGROUND: Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. RESULTS: We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. CONCLUSION: Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production. link: http://identifiers.org/pubmed/18793442
MODEL1507180046
— v0.0.1Nogales2012 - Genome-scale metabolic network of Synechocystis sp. (iJN678)This model is described in the article: [Deta…
Details
Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms. link: http://identifiers.org/pubmed/22308420
MODEL1002240000
— v0.0.1This is a reconstruction of the metabolic network of the yeast Saccharomyces cerevisiae as described in the article:…
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BACKGROUND: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism. RESULTS: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. CONCLUSION: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states. link: http://identifiers.org/pubmed/18687109
BIOMD0000000728
— v0.0.1A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the inte…
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A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the interaction of the maturation promotion factor (MPF) and cyclin. The model retrieves the dynamics observed in early embryos and explains how multiple cycles of MPF activity can be produced and how the internal clock that determines durations and number of cycles can be adjusted by modulating the rate of change in MPF or cyclin concentrations. Experiments are suggested for verifying the role of MPF activity in determining the length of the somatic cell cycle. link: http://identifiers.org/pubmed/1825521
Parameters:
Name | Description |
---|---|
i = 1.2 | Reaction: => C, Rate Law: cell*i |
e = 3.46616 | Reaction: => M; C, Rate Law: cell*e*C |
f = 1.0 | Reaction: => M; C, Rate Law: cell*f*C*M^2 |
g = 10.0 | Reaction: M =>, Rate Law: cell*g*M/(M+1) |
States:
Name | Description |
---|---|
M | [MPF complex] |
C | [Guanidine] |
BIOMD0000000107
— v0.0.1Novak1993 - Cell cycle M-phase control The model reproduces Figure 9 of the paper. Please note that active MPF and cycli…
Details
To contribute to a deeper understanding of M-phase control in eukaryotic cells, we have constructed a model based on the biochemistry of M-phase promoting factor (MPF) in Xenopus oocyte extracts, where there is evidence for two positive feedback loops (MPF stimulates its own production by activating Cdc25 and inhibiting Wee1) and a negative feedback loop (MPF stimulates its own destruction by indirectly activating the ubiquitin pathway that degrades its cyclin subunit). To uncover the full dynamical possibilities of the control system, we translate the regulatory network into a set of differential equations and study these equations by graphical techniques and computer simulation. The positive feedback loops in the model account for thresholds and time lags in cyclin-induced and MPF-induced activation of MPF, and the model can be fitted quantitatively to these experimental observations. The negative feedback loop is consistent with observed time lags in MPF-induced cyclin degradation. Furthermore, our model indicates that there are two possible mechanisms for autonomous oscillations. One is driven by the positive feedback loops, resulting in phosphorylation and abrupt dephosphorylation of the Cdc2 subunit at an inhibitory tyrosine residue. These oscillations are typical of oocyte extracts. The other type is driven by the negative feedback loop, involving rapid cyclin turnover and negligible phosphorylation of the tyrosine residue of Cdc2. The early mitotic cycles of intact embryos exhibit such characteristics. In addition, by assuming that unreplicated DNA interferes with M-phase initiation by activating the phosphatases that oppose MPF in the positive feedback loops, we can simulate the effect of addition of sperm nuclei to oocyte extracts, and the lengthening of cycle times at the mid-blastula transition of intact embryos. link: http://identifiers.org/pubmed/8126097
Parameters:
Name | Description |
---|---|
kcak = 0.25 | Reaction: dimer => dimer_p, Rate Law: kcak*dimer |
k25 = 0.0 | Reaction: p_dimer => dimer, Rate Law: k25*p_dimer |
k2 = 0.0 | Reaction: p_dimer_p =>, Rate Law: k2*p_dimer_p |
k3 = 0.01 | Reaction: cyclin + cdc2 => dimer, Rate Law: k3*cyclin*cdc2 |
kinh = 0.025 | Reaction: p_dimer_p => p_dimer, Rate Law: kinh*p_dimer_p |
total_UbE = 1.0 | Reaction: UbE = total_UbE-UbE_star, Rate Law: missing |
total_IE = 1.0 | Reaction: IE = total_IE-IE_p, Rate Law: missing |
K_a = 0.1; ka = 0.01; total_cdc25 = 1.0 | Reaction: cdc25 => cdc25_p; dimer_p, Rate Law: ka*dimer_p*(total_cdc25-cdc25_p)/((K_a+total_cdc25)-cdc25_p) |
k1AA = 1.0 | Reaction: => cyclin, Rate Law: k1AA |
total_wee1 = 1.0; K_e = 0.3; ke = 0.0133 | Reaction: wee1 => wee1_p; dimer_p, Rate Law: ke*dimer_p*(total_wee1-wee1_p)/((K_e+total_wee1)-wee1_p) |
total_wee1 = 1.0 | Reaction: wee1 = total_wee1-wee1_p, Rate Law: missing |
K_f = 0.3; kfPPase = 0.1 | Reaction: wee1_p => wee1, Rate Law: kfPPase*wee1_p/(K_f+wee1_p) |
kg = 0.0065; K_g = 0.01; total_IE = 1.0 | Reaction: IE => IE_p; dimer_p, Rate Law: kg*dimer_p*(total_IE-IE_p)/((K_g+total_IE)-IE_p) |
K_d = 0.01; kd_anti_IE = 0.095 | Reaction: UbE_star => UbE, Rate Law: kd_anti_IE*UbE_star/(K_d+UbE_star) |
kc = 0.1; K_c = 0.01; total_UbE = 1.0 | Reaction: UbE => UbE_star; IE_p, Rate Law: kc*IE_p*(total_UbE-UbE_star)/((K_c+total_UbE)-UbE_star) |
total_cdc2 = 100.0 | Reaction: cdc2 = total_cdc2-(dimer+p_dimer+p_dimer_p+dimer_p), Rate Law: missing |
kbPPase = 0.125; K_b = 0.1 | Reaction: cdc25_p => cdc25, Rate Law: kbPPase*cdc25_p/(K_b+cdc25_p) |
K_h = 0.01; khPPAse = 0.087 | Reaction: IE_p => IE, Rate Law: khPPAse*IE_p/(K_h+IE_p) |
kwee = 0.0 | Reaction: dimer_p => p_dimer_p; wee1, Rate Law: kwee*dimer_p |
total_cdc25 = 1.0 | Reaction: cdc25 = total_cdc25-cdc25_p, Rate Law: missing |
States:
Name | Description |
---|---|
wee1 p | [Wee1-like protein kinase 2-A] |
cdc2 | [Cyclin-dependent kinase 1-A] |
p dimer p | [Cyclin-dependent kinase 1-A; IPR015454] |
cdc25 p | [M-phase inducer phosphatase 3] |
dimer p | [Cyclin-dependent kinase 1-A; IPR015454] |
dimer | [Cyclin-dependent kinase 1-A; IPR015454] |
cdc25 | [M-phase inducer phosphatase 3] |
IE | intermediary enzyme |
UbE star | [ubiquitin conjugating enzyme complex] |
UbE | [ubiquitin conjugating enzyme complex] |
cyclin | [IPR015454] |
IE p | phosphorylated intermediary enzyme |
wee1 | [Wee1-like protein kinase 2-A] |
p dimer | [Cyclin-dependent kinase 1-A; IPR015454] |
BIOMD0000000007
— v0.0.1Novak1997 - Cell CycleModeling the control of DNA replication in fission yeast. This model is described in the article:…
Details
A central event in the eukaryotic cell cycle is the decision to commence DNA replication (S phase). Strict controls normally operate to prevent repeated rounds of DNA replication without intervening mitoses ("endoreplication") or initiation of mitosis before DNA is fully replicated ("mitotic catastrophe"). Some of the genetic interactions involved in these controls have recently been identified in yeast. From this evidence we propose a molecular mechanism of "Start" control in Schizosaccharomyces pombe. Using established principles of biochemical kinetics, we compare the properties of this model in detail with the observed behavior of various mutant strains of fission yeast: wee1(-) (size control at Start), cdc13Delta and rum1(OP) (endoreplication), and wee1(-) rum1Delta (rapid division cycles of diminishing cell size). We discuss essential features of the mechanism that are responsible for characteristic properties of Start control in fission yeast, to expose our proposal to crucial experimental tests. link: http://identifiers.org/pubmed/9256450
Parameters:
Name | Description |
---|---|
k4 = 0.1875 | Reaction: R =>, Rate Law: k4*R |
Kmu = 0.01; ku = 0.2; kur = 0.1; Kmur = 0.01 | Reaction: UbEB => UbE; IE, Rate Law: IE*ku*UbEB/(Kmu+UbEB)-kur*UbE/(Kmur+UbE) |
k3 = 0.09375 | Reaction: => R, Rate Law: k3 |
k6 = NaN | Reaction: G1K => ; UbE2, Rate Law: G1K*k6 |
Kmwr = 0.1; kw = 1.0; Kmw = 0.1; kwr = 0.25 | Reaction: Wee1B => Wee1; MPF, Rate Law: kwr*Wee1B/(Kmwr+Wee1B)-kw*MPF*Wee1/(Kmw+Wee1) |
k7r = 0.1; k7 = 100.0 | Reaction: G2K + R => G2R, Rate Law: G2K*k7*R-G2R*k7r |
kwee = NaN; k25 = NaN | Reaction: G2K => PG2; Wee1, Cdc25, Rate Law: G2K*kwee-k25*PG2 |
beta = 0.05 | Reaction: MPF = G2K+beta*PG2, Rate Law: missing |
kur2 = 0.3; Kmu2 = 0.05; ku2 = 1.0; Kmur2 = 0.05 | Reaction: UbE2B => UbE2; MPF, Rate Law: ku2*MPF*UbE2B/(Kmu2+UbE2B)-kur2*UbE2/(Kmur2+UbE2) |
k1 = 0.015 | Reaction: => G2K, Rate Law: k1 |
k8 = 10.0; k8r = 0.1 | Reaction: G1K + R => G1R, Rate Law: G1K*k8*R-G1R*k8r |
k2prime = 0.05 | Reaction: G2R => R, Rate Law: G2R*k2prime |
Kmp = 0.001; Mass = 0.49; kp = 3.25 | Reaction: R => ; SPF, Rate Law: kp*Mass*R*SPF/(Kmp+R) |
kc = 1.0; Kmcr = 0.1; Kmc = 0.1; kcr = 0.25 | Reaction: Cdc25B => Cdc25; MPF, Rate Law: Cdc25B*kc*MPF/(Cdc25B+Kmc)-Cdc25*kcr/(Cdc25+Kmcr) |
k6prime = 0.0 | Reaction: G1R => R, Rate Law: G1R*k6prime |
Kmi = 0.01; Kmir = 0.01; ki = 0.4; kir = 0.1 | Reaction: IEB => IE; MPF, Rate Law: IEB*ki*MPF/(IEB+Kmi)-IE*kir/(IE+Kmir) |
k5 = 0.00175 | Reaction: => G1K, Rate Law: k5 |
alpha = 0.25; Cig1 = 0.0 | Reaction: SPF = Cig1+alpha*G1K+MPF, Rate Law: missing |
k2 = NaN | Reaction: G2K => ; UbE, Rate Law: G2K*k2 |
States:
Name | Description |
---|---|
MPF | [G2/mitotic-specific cyclin cdc13; Cyclin-dependent kinase 1] |
G2R | [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin cdc13; Cyclin-dependent kinase inhibitor rum1] |
G2K | [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin cdc13] |
PG2R | [G2/mitotic-specific cyclin cdc13; Cyclin-dependent kinase 1; Cyclin-dependent kinase inhibitor rum1] |
IEB | BoundIntermediaryEnzyme |
UbE | [proteasome complex] |
Cdc13Total | [G2/mitotic-specific cyclin cdc13] |
Cig2Total | [G2/mitotic-specific cyclin cig2] |
G1K | [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin cig2] |
SPF | [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin cdc13; G2/mitotic-specific cyclin cig2] |
Wee1 | [Mitosis inhibitor protein kinase mik1; Mitosis inhibitor protein kinase wee1] |
UbE2 | [proteasome complex] |
Cdc25B | [M-phase inducer phosphatase] |
Wee1B | [Mitosis inhibitor protein kinase wee1; Mitosis inhibitor protein kinase mik1] |
PG2 | [G2/mitotic-specific cyclin cdc13; Cyclin-dependent kinase 1] |
IE | IntermediaryEnzyme |
Cdc25 | [M-phase inducer phosphatase] |
G1R | [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin cig2; Cyclin-dependent kinase inhibitor rum1] |
UbEB | [proteasome complex] |
R | [Cyclin-dependent kinase inhibitor rum1] |
Rum1Total | [Cyclin-dependent kinase inhibitor rum1] |
UbE2B | [proteasome complex] |
MODEL2003190004
— v0.0.1Mathematical model of the fission yeast cell cycle with checkpoint controls at the G1/S, G2/M and metaphase/anaphase tra…
Details
All events of the fission yeast cell cycle can be orchestrated by fluctuations of a single cyclin-dependent protein kinase, the Cdc13/Cdc2 heterodimer. The G1/S transition is controlled by interactions of Cdc13/Cdc2 and its stoichiometric inhibitor, Rum1. The G2/M transition is regulated by a kinase-phosphatase pair, Wee1 and Cdc25, which determine the phosphorylation state of the Tyr-15 residue of Cdc2. The meta/anaphase transition is controlled by interactions between Cdc13/Cdc2 and the anaphase promoting complex, which labels Cdc13 subunits for proteolysis. We construct a mathematical model of fission yeast growth and division that encompasses all three crucial checkpoint controls. By numerical simulations we show that the model is consistent with a broad selection of cell cycle mutants, and we predict the phenotypes of several multiple-mutant strains that have not yet been constructed. link: http://identifiers.org/pubmed/9652094
MODEL2005040001
— v0.0.1Progress through the division cycle of present day eukaryotic cells is controlled by a complex network consisting of (i)…
Details
Progress through the division cycle of present day eukaryotic cells is controlled by a complex network consisting of (i) cyclin-dependent kinases (CDKs) and their associated cyclins, (ii) kinases and phosphatases that regulate CDK activity, and (iii) stoichiometric inhibitors that sequester cyclin-CDK dimers. Presumably regulation of cell division in the earliest ancestors of eukaryotes was a considerably simpler affair. Nasmyth (1995) recently proposed a mechanism for control of a putative, primordial, eukaryotic cell cycle, based on antagonistic interactions between a cyclin-CDK and the anaphase promoting complex (APC) that labels the cyclin subunit for proteolysis. We recast this idea in mathematical form and show that the model exhibits hysteretic behaviour between alternative steady states: a Gl-like state (APC on, CDK activity low, DNA unreplicated and replication complexes assembled) and an S/M-like state (APC off, CDK activity high, DNA replicated and replication complexes disassembled). In our model, the transition from G1 to S/M ('Start') is driven by cell growth, and the reverse transition ('Finish') is driven by completion of DNA synthesis and proper alignment of chromosomes on the metaphase plate. This simple and effective mechanism for coupling growth and division and for accurately copying and partitioning a genome consisting of numerous chromosomes, each with multiple origins of replication, could represent the core of the eukaryotic cell cycle. Furthermore, we show how other controls could be added to this core and speculate on the reasons why stoichiometric inhibitors and CDK inhibitory phosphorylation might have been appended to the primitive alternation between cyclin accumulation and degradation. link: http://identifiers.org/pubmed/10098216
BIOMD0000000111
— v0.0.1The model reproduces the time evolution of several species as depicted in Fig 4 of the paper. Events have been used to r…
Details
Much is known about the genes and proteins controlling the cell cycle of fission yeast. Can these molecular components be spun together into a consistent mechanism that accounts for the observed behavior of growth and division in fission yeast cells? To answer this question, we propose a mechanism for the control system, convert it into a set of 14 differential and algebraic equations, study these equations by numerical simulation and bifurcation theory, and compare our results to the physiology of wild-type and mutant cells. In wild-type cells, progress through the cell cycle (G1–>S–>G2–>M) is related to cyclic progression around a hysteresis loop, driven by cell growth and chromosome alignment on the metaphase plate. However, the control system operates much differently in double-mutant cells, wee1(-) cdc25Delta, which are defective in progress through the latter half of the cell cycle (G2 and M phases). These cells exhibit "quantized" cycles (interdivision times clustering around 90, 160, and 230 min). We show that these quantized cycles are associated with a supercritical Hopf bifurcation in the mechanism, when the wee1 and cdc25 genes are disabled. (c) 2001 American Institute of Physics. link: http://identifiers.org/pubmed/12779461
Parameters:
Name | Description |
---|---|
k1 = 0.03 min_inv | Reaction: => cdc13T; M, Rate Law: k1*M |
k8 = 0.25 min_inv; J8 = 0.001 dimensionless | Reaction: slp1 =>, Rate Law: k8*slp1/(J8+slp1) |
k6 = 0.1 min_inv | Reaction: slp1T =>, Rate Law: k6*slp1T |
J5 = 0.3 dimensionless; k5_double_prime = 0.3 min_inv; k5_prime = 0.005 min_inv | Reaction: => slp1T; MPF, Rate Law: k5_prime+k5_double_prime*MPF^4/(J5^4+MPF^4) |
J9 = 0.01 dimensionless; k9 = 0.1 min_inv | Reaction: => IEP; MPF, Rate Law: k9*MPF*(1-IEP)/((J9+1)-IEP) |
k3_prime = 1.0 min_inv; k3_double_prime = 10.0 min_inv; J3 = 0.01 dimensionless | Reaction: => ste9; slp1, Rate Law: (k3_prime+k3_double_prime*slp1)*(1-ste9)/((J3+1)-ste9) |
kwee = 0.0 min_inv | Reaction: => preMPF; cdc13T, Rate Law: kwee*(cdc13T-preMPF) |
mu = 0.005 min_inv | Reaction: => M, Rate Law: mu*M |
k2_double_prime = 1.0 min_inv; k2_prime = 0.03 min_inv; k2_triple_prime = 0.1 min_inv | Reaction: preMPF => ; ste9, slp1, Rate Law: (k2_prime+k2_double_prime*ste9+k2_triple_prime*slp1)*preMPF |
k7 = 1.0 min_inv; J7 = 0.001 dimensionless | Reaction: => slp1; IEP, slp1T, Rate Law: k7*IEP*(slp1T-slp1)/((J7+slp1T)-slp1) |
k4_prime = 2.0 min_inv; k4 = 35.0 min_inv; J4 = 0.01 dimensionless | Reaction: ste9 => ; SK, MPF, Rate Law: (k4_prime*SK+k4*MPF)*ste9/(J4+ste9) |
J10 = 0.01 dimensionless; k10 = 0.04 min_inv | Reaction: IEP =>, Rate Law: k10*IEP/(J10+IEP) |
k13 = 0.1 min_inv; TF = 0.0 dimensionless | Reaction: => SK, Rate Law: k13*TF |
Trimer = 0.0 dimensionless | Reaction: MPF = (cdc13T-preMPF)*(cdc13T-Trimer)/cdc13T, Rate Law: missing |
k14 = 0.1 min_inv | Reaction: SK =>, Rate Law: k14*SK |
k11 = 0.1 min_inv | Reaction: => rum1T, Rate Law: k11 |
k12 = 0.01 min_inv; k12_prime = 1.0 min_inv; k12_double_prime = 3.0 min_inv | Reaction: rum1T => ; SK, MPF, Rate Law: (k12+k12_prime*SK+k12_double_prime*MPF)*rum1T |
k25 = 0.0 min_inv | Reaction: preMPF =>, Rate Law: k25*preMPF |
States:
Name | Description |
---|---|
preMPF | [Cyclin-dependent kinase 1; IPR015454] |
MPF | [Cyclin-dependent kinase 1; IPR015454] |
rum1T | [Cyclin-dependent kinase inhibitor rum1] |
M | Cell Mass |
IEP | IEP |
SK | [G2/mitotic-specific cyclin cig1] |
slp1T | [WD repeat-containing protein slp1] |
slp1 | [WD repeat-containing protein slp1] |
ste9 | [WD repeat-containing protein srw1] |
cdc13T | [IPR015454] |
MODEL2006080001
— v0.0.1<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1…
Details
Inhibition of protein synthesis by cycloheximide blocks subsequent division of a mammalian cell, but only if the cell is exposed to the drug before the "restriction point" (i.e. within the first several hours after birth). If exposed to cycloheximide after the restriction point, a cell proceeds with DNA synthesis, mitosis and cell division and halts in the next cell cycle. If cycloheximide is later removed from the culture medium, treated cells will return to the division cycle, showing a complex pattern of division times post-treatment, as first measured by Zetterberg and colleagues. We simulate these physiological responses of mammalian cells to transient inhibition of growth, using a set of nonlinear differential equations based on a realistic model of the molecular events underlying progression through the cell cycle. The model relies on our earlier work on the regulation of cyclin-dependent protein kinases during the cell division cycle of yeast. The yeast model is supplemented with equations describing the effects of retinoblastoma protein on cell growth and the synthesis of cyclins A and E, and with a primitive representation of the signaling pathway that controls synthesis of cyclin D. link: http://identifiers.org/pubmed/15363676
MODEL1006230050
— v0.0.1This a model from the article: Population dynamics of immune responses to persistent viruses. Nowak MA, Bangham CR.…
Details
Mathematical models, which are based on a firm understanding of biological interactions, can provide nonintuitive insights into the dynamics of host responses to infectious agents and can suggest new avenues for experimentation. Here, a simple mathematical approach is developed to explore the relation between antiviral immune responses, virus load, and virus diversity. The model results are compared to data on cytotoxic T cell responses and viral diversity in infections with the human T cell leukemia virus (HTLV-1) and the human immunodeficiency virus (HIV-1). link: http://identifiers.org/pubmed/8600540