Stochastic models for regulatory networks of the genetic toggle switch

Tian, TH and Burrage, K (2006) Stochastic models for regulatory networks of the genetic toggle switch. Proceedings of The National Academy of Sciences of The United States of America, 103 22: 8372-8377. doi:10.1073/pnas.0507818103


Author Tian, TH
Burrage, K
Title Stochastic models for regulatory networks of the genetic toggle switch
Journal name Proceedings of The National Academy of Sciences of The United States of America   Check publisher's open access policy
ISSN 0027-8424
Publication date 2006-01-01
Sub-type Article (original research)
DOI 10.1073/pnas.0507818103
Volume 103
Issue 22
Start page 8372
End page 8377
Total pages 6
Editor Randy Schekman
Place of publication Washington, USA
Publisher National Academy of Sciences
Language eng
Subject C1
239901 Biological Mathematics
230116 Numerical Analysis
279999 Biological Sciences not elsewhere classified
780101 Mathematical sciences
780105 Biological sciences
Abstract Bistability arises within a wide range of biological systems from the A 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.
Keyword Genetic Regulatory Network
Stochastic Modeling
Stochastic Simulation
Noise
Multidisciplinary Sciences
Escherichia-coli
Chemical-kinetics
Positive-feedback
Lac Operon
Expression
Bistability
Cells
Robustness
Simulation
Q-Index Code C1

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 114 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 123 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 15 Aug 2007, 19:16:00 EST