Systematic modeling for the insulin signaling network mediated by IRS(1) and IRS(2).
The hepatic insulin signaling mediated by insulin receptor substrates IRS1 and IRS2 plays a central role in maintaining glucose homeostasis under different physiological conditions. Although functions of individual components in the signaling network have been extensively studied, our knowledge is still limited with regard to how the signals are integrated and coordinated in the complex network to render their functional roles. In this study, we construct systematic models for the insulin signaling network mediated by IRS1 and IRS2, through the integration of current knowledge in the literature into mathematical models of insulin signaling pathways. We hypothesize that the specificity of the IRS signaling mechanisms emerges from the wiring and kinetics of the entire network. A discrete dynamic model is first constructed to account for the numerous dynamic features in the system, i.e., complex feedback circuits, different regulatory time-scales and cross-talks between pathways. Our simulation shows that the wiring of the network determines different functions of IRS1 and IRS2. We further collate and reconstruct a kinetic model of the network as a system of ordinary differential equations to provide an informative model for predicting phenotypes. A sensitivity analysis is applied to identify essential regulators for the signaling process.
Department of Mathematics, Michigan State University, East Lansing, MI 48824, United States.
Can
C
Huang
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, United States.
Ming
M
Wu
Department of Mathematics, Michigan State University, East Lansing, MI 48824, United States.
Jun
J
Du
Department of Mathematics, Michigan State University, East Lansing, MI 48824, United States. Electronic address: richardl@math.msu.edu.
Di
D
Liu
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, United States; Department of Chemical Engineering and Materials Science, Michigan State University, 2527 EB, East Lansing, MI 48824, United States; Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States. Electronic address: krischan@egr.msu.edu.
Christina
C
Chan
D000818:Animals; D003198:Computer Simulation; D007328:Insulin / Q000378:metabolism*; D055504:Insulin Receptor Substrate Proteins / Q000378:metabolism*; D007700:Kinetics; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods
C526292:IRS1 protein, human; C526297:IRS2 protein, human; D007328:Insulin; D055504:Insulin Receptor Substrate Proteins
Discrete model; Dynamic simulation; Kinetic model