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.

LanguageEnglish (US)
Pages40-52
Number of pages13
JournalJournal of Theoretical Biology
Volume355
DOIs
StatePublished - Aug 21 2014

Profile

Insulin
insulin
Modeling
kinetics
Insulin Receptor
Electric wiring
dynamic models
homeostasis
Homeostasis
Theoretical Models
mathematical models
Discrete Dynamics
Signaling Pathways
Phenotype
Crosstalk
phenotype
Glucose
Kinetic Model
liver
glucose

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

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