A dynamic analysis of IRS-PKR signaling in liver cells: A discrete modeling approach

Ming Wu, Xuerui Yang, Christina Chan

    Research output: Contribution to journalArticle

    • 21 Citations

    Abstract

    A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNAdependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.

    Original languageEnglish (US)
    Article numbere8040
    JournalPLoS One
    Volume4
    Issue number12
    DOIs
    StatePublished - 2009

    Profile

    Insulin
    Liver
    Spontaneous Fractures
    insulin
    Fibrin
    hepatocytes
    Fovea Centralis
    Anthralin
    cells
    eIF-2 Kinase
    Systems Biology
    Computer Simulation
    Phenotype
    Tennis
    Dynamic models
    Feedback
    dynamic models
    protein kinases
    phenotype
    Biological Sciences

    ASJC Scopus subject areas

    • Agricultural and Biological Sciences(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Medicine(all)

    Cite this

    A dynamic analysis of IRS-PKR signaling in liver cells : A discrete modeling approach. / Wu, Ming; Yang, Xuerui; Chan, Christina.

    In: PLoS One, Vol. 4, No. 12, e8040, 2009.

    Research output: Contribution to journalArticle

    Wu, Ming; Yang, Xuerui; Chan, Christina / A dynamic analysis of IRS-PKR signaling in liver cells : A discrete modeling approach.

    In: PLoS One, Vol. 4, No. 12, e8040, 2009.

    Research output: Contribution to journalArticle

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