/* * 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: gupta_2009.png]] * * Schematic diagram of the reaction network for LPS/ KDO2-lipid * A stimulated eicosanoid metabolism and signaling pathway. */ import nsrunit; unit conversion on; unit hour=3600 second^1; unit per_hour=2.7777778E-4 second^(-1); unit pmol_per_ug_DNA=1E-21 kilogram^1*mole^1; unit pmol_per_ug_DNA_per_hour=2.7777778E-25 kilogram^1*second^(-1)*mole^1; unit ug_DNA_per_pmol_per_hour=2.7777778E17 kilogram^(-1)*second^(-1)*mole^(-1); math main { realDomain time hour; time.min=0; extern time.max; extern time.delta; real v_1(time) pmol_per_ug_DNA_per_hour; real v_2 pmol_per_ug_DNA_per_hour; real v_3 pmol_per_ug_DNA_per_hour; real v_4(time) pmol_per_ug_DNA_per_hour; real v_5 pmol_per_ug_DNA_per_hour; real v_6(time) pmol_per_ug_DNA_per_hour; real v_7 pmol_per_ug_DNA_per_hour; real v_8(time) pmol_per_ug_DNA_per_hour; real v_9(time) pmol_per_ug_DNA_per_hour; real v_10(time) pmol_per_ug_DNA_per_hour; real v_11(time) pmol_per_ug_DNA_per_hour; real v_12(time) pmol_per_ug_DNA_per_hour; real v_13(time) pmol_per_ug_DNA_per_hour; real v_14(time) pmol_per_ug_DNA_per_hour; real v_15(time) pmol_per_ug_DNA_per_hour; real v_16(time) pmol_per_ug_DNA_per_hour; real v_17(time) pmol_per_ug_DNA_per_hour; real v_18(time) pmol_per_ug_DNA_per_hour; real v_19(time) pmol_per_ug_DNA_per_hour; real v_20(time) pmol_per_ug_DNA_per_hour; real v_21(time) pmol_per_ug_DNA_per_hour; real v_22(time) pmol_per_ug_DNA_per_hour; real k_1 pmol_per_ug_DNA_per_hour; k_1=355.637; real k_2 pmol_per_ug_DNA_per_hour; k_2=1e-15; real k_3 per_hour; k_3=1e-15; real k_4 per_hour; k_4=1e-15; real k_5 ug_DNA_per_pmol_per_hour; k_5=1e-15; real k_6 per_hour; k_6=0.33; real k_7 per_hour; k_7=1e-15; real k_8 per_hour; k_8=0.007; real k_9 per_hour; k_9=0.187; real k_10 ug_DNA_per_pmol_per_hour; k_10=0.024; real k_11 per_hour; k_11=0.111; real k_12 per_hour; k_12=0.098; real k_13 per_hour; k_13=0.204; real k_14 per_hour; k_14=1e-15; real k_15 per_hour; k_15=0.061; real k_16 per_hour; k_16=1e-15; real k_17 per_hour; k_17=3.116; real k_18 per_hour; k_18=0.054; real k_19 per_hour; k_19=0.029; real k_20 per_hour; k_20=0.014; real k_21 per_hour; k_21=0.034; real k_22 per_hour; k_22=0.116; real LPS(time) dimensionless; real PIP_2 dimensionless; PIP_2=1; real DG pmol_per_ug_DNA; DG=0; real GPCho pmol_per_ug_DNA; GPCho=1; real AA(time) pmol_per_ug_DNA; when(time=time.min) AA=25; real HETE(time) pmol_per_ug_DNA; when(time=time.min) HETE=0; real PGH_2(time) pmol_per_ug_DNA; when(time=time.min) PGH_2=0; real PGE_2(time) pmol_per_ug_DNA; when(time=time.min) PGE_2=0; real PGF_2a(time) pmol_per_ug_DNA; when(time=time.min) PGF_2a=0; real PGD_2(time) pmol_per_ug_DNA; when(time=time.min) PGD_2=0; real PGJ_2(time) pmol_per_ug_DNA; when(time=time.min) PGJ_2=0; real dPGD_2(time) pmol_per_ug_DNA; when(time=time.min) dPGD_2=0; real dPGJ_2(time) pmol_per_ug_DNA; when(time=time.min) dPGJ_2=0; // // LPS=(if (time<=(.5 hour)) time*(2 per_hour) else if ((time>(.5 hour)) and (time<(2 hour))) 1-(time-(.5 hour))/(1.5 hour) else 0); v_1=(LPS*PIP_2*k_1); v_2=(PIP_2*k_2); v_3=(DG*k_3); v_4=(AA*k_4); v_5=(DG*GPCho*k_5); v_6=(LPS*GPCho*k_6); v_7=(GPCho*k_7); v_8=(AA*k_8); v_9=(HETE*k_9); v_10=(DG*AA*k_10); v_11=(LPS*AA*k_11); v_12=(AA*k_12); v_13=(PGH_2*k_13); v_14=(PGE_2*k_14); v_15=(PGH_2*k_15); v_16=(PGF_2a*k_16); v_17=(PGH_2*k_17); v_18=(PGD_2*k_18); v_19=(PGD_2*k_19); v_20=(dPGD_2*k_20); v_21=(PGJ_2*k_21); v_22=(dPGJ_2*k_22); AA:time=(v_1+v_2+v_3-v_4+v_5+v_6+v_7-v_8-v_10-v_11-v_12); HETE:time=(v_8-v_9); PGH_2:time=(v_10+v_11+v_12-v_13-v_15-v_17); PGE_2:time=(v_13-v_14); PGF_2a:time=(v_15-v_16); PGD_2:time=(v_17-v_18-v_19); PGJ_2:time=(v_18-v_21); dPGD_2:time=(v_19-v_20); dPGJ_2:time=(v_21-v_22); }