/* * Dynamic rerouting of the carbohydrate flux is key to counteracting * oxidative stress * * Model Status * * This CellML model runs in COR and OpenCell and the units are * consistent throughout. It reproduces the published results and * was converted from SBML with the help of Lukas Endler. Validation * was done in both CellML and Matlab, Matlab was used to simulate * variations in GAP and R concentrations and to reproduce figures * 3A and B. * * Model Structure * * ABSTRACT: Heterotrimeric G protein signaling is regulated by * signaling modules composed of heterotrimeric G proteins, active * G protein-coupled receptors (Rs), which activate G proteins, * and GTPase-activating proteins (GAPs), which deactivate G proteins. * We term these modules GTPase-cycle modules. The local concentrations * of these proteins are spatially regulated between plasma membrane * microdomains and between the plasma membrane and cytosol, but * no data or models are available that quantitatively explain * the effect of such regulation on signaling. We present a computational * model of the GTPase-cycle module that predicts that the interplay * of local G protein, R, and GAP concentrations gives rise to * 16 distinct signaling regimes and numerous intermediate signaling * phenomena. The regimes suggest alternative modes of the GTPase-cycle * module that occur based on defined local concentrations of the * component proteins. In one mode, signaling occurs while G protein * and receptor are unclustered and GAP eliminates signaling; in * another, G protein and receptor are clustered and GAP can rapidly * modulate signaling but does not eliminate it. Experimental data * from multiple GTPase-cycle modules is interpreted in light of * these predictions. The latter mode explains previously paradoxical * data in which GAP does not alter maximal current amplitude of * G protein-activated ion channels, but hastens signaling. The * predictions indicate how variations in local concentrations * of the component proteins create GTPase-cycle modules with distinctive * phenotypes. They provide a quantitative framework for investigating * how regulation of local concentrations of components of the * GTPase-cycle module affects signaling. * * The original paper reference is cited below: * * Computational modeling reveals how interplay between components * of a GTPase-cycle module regulates signal transduction, Scott * J. Bornheimer, Mano R. Maurya, Marilyn Gist Farquhar, and Shankar * Subramaniam, 2004, PNAS, volume 101, 15899-15904. PubMed ID: * 15520372 * * model diagram * * [[Image file: bornheimer_2004.png]] * * Biochemical reaction scheme of the GTPase cycle. */ import nsrunit; unit conversion on; // unit molar predefined unit molar_per_second=1E3 meter^(-3)*second^(-1)*mole^1; unit per_second=1 second^(-1); unit per_molar_per_second=.001 meter^3*second^(-1)*mole^(-1); math main { realDomain time second; time.min=0; extern time.max; extern time.delta; real A(time) molar; when(time=time.min) A=0; real G(time) molar; when(time=time.min) G=0; real GA(time) molar; when(time=time.min) GA=0; real T(time) molar; when(time=time.min) T=0.000468; real R(time) molar; when(time=time.min) R=1e-6; real GT(time) molar; when(time=time.min) GT=0; real GD(time) molar; when(time=time.min) GD=1e-8; real Pi_(time) molar; when(time=time.min) Pi_=0.0044; real D(time) molar; when(time=time.min) D=0.000149; real RG(time) molar; when(time=time.min) RG=0; real RGT(time) molar; when(time=time.min) RGT=0; real GAT(time) molar; when(time=time.min) GAT=0; real GAD(time) molar; when(time=time.min) GAD=0; real RGD(time) molar; when(time=time.min) RGD=0; real RGA(time) molar; when(time=time.min) RGA=0; real RGAT(time) molar; when(time=time.min) RGAT=0; real RGAD(time) molar; when(time=time.min) RGAD=0; real A1(time) molar_per_second; real A2(time) molar_per_second; real A3(time) molar_per_second; real A4(time) molar_per_second; real A5(time) molar_per_second; real A6(time) molar_per_second; real R1(time) molar_per_second; real R2(time) molar_per_second; real R3(time) molar_per_second; real R4(time) molar_per_second; real R5(time) molar_per_second; real R6(time) molar_per_second; real T1(time) molar_per_second; real T2(time) molar_per_second; real T3(time) molar_per_second; real T4(time) molar_per_second; real P1(time) molar_per_second; real P2(time) molar_per_second; real P3(time) molar_per_second; real P4(time) molar_per_second; real D1(time) molar_per_second; real D2(time) molar_per_second; real D3(time) molar_per_second; real D4(time) molar_per_second; real Z(time) dimensionless; real v(time) per_second; real G_tot molar; G_tot=1e-8; real P_minus1 per_second; P_minus1=0.013; real P_minus2 per_second; P_minus2=25; real P_minus3 per_second; P_minus3=0.013; real P_minus4 per_second; P_minus4=25; real A1.k1 per_molar_per_second; A1.k1=8780000; real A1.k2 per_second; A1.k2=8; real T1.k1 per_molar_per_second; T1.k1=529000; real T1.k2 per_second; T1.k2=8.38e-6; real R1.k1 per_molar_per_second; R1.k1=636000000; real R1.k2 per_second; R1.k2=0.0179; real P1.k2 per_molar_per_second; P1.k2=9.03e-7; real D1.k1 per_second; D1.k1=0.0001; real D1.k2 per_molar_per_second; D1.k2=62.3; real T3.k1 per_molar_per_second; T3.k1=853000; real T3.k2 per_second; T3.k2=0.00468; real R2.k1 per_molar_per_second; R2.k1=132000000; real R2.k2 per_second; R2.k2=1.28; real A2.k1 per_molar_per_second; A2.k1=386000; real A2.k2 per_second; A2.k2=0.0408; real A3.k1 per_molar_per_second; A3.k1=64100; real A3.k2 per_second; A3.k2=0.95; real R3.k1 per_molar_per_second; R3.k1=94700000; real R3.k2 per_second; R3.k2=0.00227; real P3.k2 per_molar_per_second; P3.k2=2.22e-9; real D3.k1 per_second; D3.k1=2; real D3.k2 per_molar_per_second; D3.k2=1470000; real T2.k1 per_molar_per_second; T2.k1=44700; real T2.k2 per_second; T2.k2=8.32e-8; real P2.k2 per_molar_per_second; P2.k2=0.244; real D2.k1 per_second; D2.k1=0.0001; real D2.k2 per_molar_per_second; D2.k2=3.83; real A4.k1 per_molar_per_second; A4.k1=74300; real A4.k2 per_second; A4.k2=0.00572; real R4.k1 per_molar_per_second; R4.k1=22800000; real R4.k2 per_second; R4.k2=5.43e-5; real T4.k1 per_molar_per_second; T4.k1=1620000; real T4.k2 per_second; T4.k2=0.00875; real R5.k1 per_molar_per_second; R5.k1=6200000; real R5.k2 per_second; R5.k2=0.0433; real A5.k1 per_molar_per_second; A5.k1=6300000; real A5.k2 per_second; A5.k2=0.478; real P4.k2 per_molar_per_second; P4.k2=0.00297; real A6.k1 per_molar_per_second; A6.k1=13000; real A6.k2 per_second; A6.k2=0.685; real R6.k1 per_molar_per_second; R6.k1=49400000; real R6.k2 per_second; R6.k2=0.00421; real D4.k1 per_second; D4.k1=2.75; real D4.k2 per_molar_per_second; D4.k2=2940; // // A:time=((-1)*(A1+A2+A3+A4+A5+A6)); G:time=(D1-(A1+T1+R1)); GA:time=(A1-T2+D2-R4); T:time=((-1)*(T1+T3+T2+T4)); R:time=((-1)*(R1+R2+R3+R4+R5+R6)); GT:time=(T1-P1-R2-A2); GD:time=(P1-D1-A3-R3); Pi_:time=(P1+P3+P2+P4); D:time=(D1+D3+D2+D4); RG:time=(R1-T3+D3-A4); RGT:time=(T3+R2-P3-A5); GAT:time=(A2+T2-P2-R5); GAD:time=(A3+P2-D2-R6); RGD:time=(R3+P3-D3-A6); RGA:time=(A4+R4-T4+D4); RGAT:time=(T4+R5+A5-P4); RGAD:time=(P4+A6+R6-D4); // Z=((GT+RGT+RGAT+GAT)/G_tot); v=((P_minus4*RGAT+P_minus2*GAT+P_minus3*RGT+P_minus1*GT)/G_tot); // A1=(A1.k1*G*A-A1.k2*GA); // T1=(T1.k1*G*T-T1.k2*GT); // R1=(R1.k1*G*R-R1.k2*RG); // P1=(P_minus1*GT-P1.k2*GD*Pi_); // D1=(D1.k1*GD-D1.k2*G*D); // T3=(T3.k1*RG*T-T3.k2*RGT); // R2=(R2.k1*GT*R-R2.k2*RGT); // A2=(A2.k1*GT*A-A2.k2*GAT); // A3=(A3.k1*GD*A-A3.k2*GAD); // R3=(R3.k1*GD*R-R3.k2*RGD); // P3=(P_minus3*RGT-P3.k2*RGD*Pi_); // D3=(D3.k1*RGD-D3.k2*RG*D); // T2=(T2.k1*GA*T-T2.k2*GAT); // P2=(P_minus2*GAT-P2.k2*GAD*Pi_); // D2=(D2.k1*GAD-D2.k2*GA*D); // A4=(A4.k1*RG*A-A4.k2*RGA); // R4=(R4.k1*GA*R-R4.k2*RGA); // T4=(T4.k1*RGA*T-T4.k2*RGAT); // R5=(R5.k1*GAT*R-R5.k2*RGAT); // A5=(A5.k1*RGT*A-A5.k2*RGAT); // P4=(P_minus4*RGAT-P4.k2*RGAD*Pi_); // A6=(A6.k1*RGD*A-A6.k2*RGAD); // R6=(R6.k1*GAD*R-R6.k2*RGAD); // D4=(D4.k1*RGAD-D4.k2*RGA*D); }