Bioinformatics Advance Access published online on October 12, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm490
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Steady-state expression of self-regulated genes

aDepartment of Mathematics, University of Fribourg, Chemin du Musée 23, CH-1700 Fribourg,b,Institute of Biotechnology, University of Lausanne, CH-1015 Lausanne, Switzerland
*To whom correspondence should be addressed. Prof. Christian Mazza, E-mail: christian.mazza{at}unifr.ch
| Abstract |
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Motivation: Regulatory gene networks contain generic modules such as feedback loops that are essential for the regulation of many biological functions Guido et al., 2006. The study of the stochastic mechanisms of gene regulation is instrumental for the understanding of how cells maintain their expression at levels commensurate with their biological role, as well as to engineer gene expression switches of appropriate behavior. The lack of precise knowledge on the steadystate distribution of gene expression requires the use of Gillespie algorithms and Monte-Carlo approximations.
Methodology: In this study, we provide new exact formulas and efficient numerical algorithms for computing/modeling the steadystate of a class of self regulated genes, and we use it to model/compute the stochastic expression of a gene of interest in an engineered network introduced in mammalian cells.
Results: Stochastic models often reveal counter-intuitive experimental behaviors, and we find that this genetic architecture displays an unimodal behavior in mammalian cells, which was unexpected given its known bimodal response in unicellular organisms. We provide a molecular rationale for this behavior and we implement it in the mathematical picture to explain the experimental results obtained from this network.
Contact: nicolas.mermod{at}unil.ch
Associate Editor: Dr. Jonathan Wren
Received on May 23, 2007; revised on August 29, 2007; accepted on September 24, 2007