Stochastic models for regulatory networks of the genetic toggle switch

Tian, Tianhai & Burrage, Kevin (2006) Stochastic models for regulatory networks of the genetic toggle switch. Proceedings of the National Academy of Sciences of the United States of AmericaNational Academy of Sciences, 103(22), pp. 8372-8377.

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Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

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ID Code: 44251
Item Type: Journal Article
Refereed: Yes
Keywords: genetic regulatory network, stochastic modeling, stochastic simulation, noise
DOI: 10.1073/pnas.0507818103
ISSN: 1091-6490
Deposited On: 24 Aug 2011 22:09
Last Modified: 29 Feb 2012 14:36

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