A "Trimer of Dimers"-Based Model for the Chemotactic Signal Transduction Network in Bacterial Chemotaxis

Xiangrong Xin, Hans G. Othmer

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

The network that controls chemotaxis in Escherichia coli is one of the most completely characterized signal transduction systems to date. Receptor clustering accounts for characteristics such as high sensitivity, precise adaptation over a wide dynamic range of ligand concentrations, and robustness to variations in the amounts of intracellular proteins. To gain insights into the structure-function relationship of receptor clusters and understand the mechanism behind the high-performance signaling, we develop and analyze a model for a single trimer of dimers. This new model extends an earlier model (Spiro et al. in Proc. Natl. Acad. Sci. 94:7263-7268, 1997) to incorporate the recent experimental findings that the core structure of receptor clusters is the trimer of receptor dimers. We show that the model can reproduce most of the experimentally-observed behaviors, including excitation, adaptation, high sensitivity, and robustness to parameter variations. In addition, the model makes a number of new predictions as to how the adaptation time varies with the expression level of various proteins involved in signal transduction. Our results provide a more mechanistically-based description of the structure-function relationship for the signaling system, and show the key role of the interaction among dimer members of the trimer in the chemotactic response of cells.

Original languageEnglish (US)
Pages (from-to)2339-2382
Number of pages44
JournalBulletin of mathematical biology
Volume74
Issue number10
DOIs
StatePublished - Oct 2012

Bibliographical note

Funding Information:
Acknowledgements We thank Sandy Parkinson and David Odde for helpful discussions at various stages of the model development. This work was supported by NIH grant GM029123 to HGO and by the University of Minnesota Supercomputing Institute.

Keywords

  • Adaptation
  • E. coli
  • Robustness
  • Sensitivity

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