Insulin receptor binding kinetics: Modeling and simulation studies

S. Wanant, M. J. Quon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

Biological actions of insulin regulate glucose metabolism and other essential physiological functions. Binding of insulin to its cell surface receptor initiates signal transduction pathways that mediate cellular responses. Thus, it is of great interest to understand the mechanisms underlying insulin receptor binding kinetics. Interestingly, negative cooperative interactions are observed at high insulin concentrations while positive cooperativity may be present at low insulin concentrations. Clearly, insulin receptor binding kinetics cannot be simply explained by a classical bimolecular reaction. Mature insulin receptors have a dimeric structure capable of binding two molecules of insulin. The binding affinity of the receptor for the second insulin molecule is significantly lower than for the first bound insulin molecule. In addition, insulin receptor aggregation occurs in response to ligand binding and aggregation may also influence binding kinetics. In this study, we develop a mathematical model for insulin receptor binding kinetics that explicitly represents the divalent nature of the insulin receptor and incorporates receptor, aggregation into the kinetic model. Model parameters are based upon published data where available. Computer simulations with our model are capable of reproducing both negative and positive cooperativity at the appropriate insulin concentrations. This model may be a useful tool for helping to understand the mechanisms underlying insulin receptor binding and the coupling of receptor binding to downstream signaling events. (C) 2000 Academic Press.

Original languageEnglish (US)
Pages (from-to)355-364
Number of pages10
JournalJournal of Theoretical Biology
Volume205
Issue number3
DOIs
StatePublished - Aug 7 2000

ASJC Scopus subject areas

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

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