Systematic modeling for the insulin signaling network mediated by IRS1 and IRS2

Can Huang, Ming Wu, Jun Du, Di Liu, Christina Chan

    Research output: Contribution to journalArticle

    • 4 Citations

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)40-52
    Number of pages13
    JournalJournal of Theoretical Biology
    Volume355
    DOIs
    StatePublished - Aug 21 2014

    Profile

    insulin
    Insulin
    kinetics
    Anthralin
    Model
    Insulin Receptor
    Homeostasis
    Phenotype
    Glucose
    Liver
    Electric wiring
    Kinetics
    dynamic models
    homeostasis
    mathematical models
    phenotype
    glucose
    insulin receptors
    Discrete dynamics
    Signaling pathways

    Keywords

    • Discrete model
    • Dynamic simulation
    • Kinetic model

    ASJC Scopus subject areas

    • Applied Mathematics
    • Statistics and Probability
    • Modeling and Simulation
    • Agricultural and Biological Sciences(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Immunology and Microbiology(all)
    • Medicine(all)

    Cite this

    Systematic modeling for the insulin signaling network mediated by IRS1 and IRS2. / Huang, Can; Wu, Ming; Du, Jun; Liu, Di; Chan, Christina.

    In: Journal of Theoretical Biology, Vol. 355, 21.08.2014, p. 40-52.

    Research output: Contribution to journalArticle

    Huang, Can; Wu, Ming; Du, Jun; Liu, Di; Chan, Christina / Systematic modeling for the insulin signaling network mediated by IRS1 and IRS2.

    In: Journal of Theoretical Biology, Vol. 355, 21.08.2014, p. 40-52.

    Research output: Contribution to journalArticle

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    abstract = "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.",
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