Abstract
Gene expression is based on biochemical processes that are inherently stochastic. The resulting fluctuations in mRNA and protein levels can sometimes be exploited but generally need to be controlled for reliable function of regulatory networks. From models of these biochemical processes it is possible to obtain analytical expressions for the stochastic properties of the resulting distributions of expression levels. We present a review of the two main analytical techniques for modeling stochastic gene expression.
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Pedraza, J.M., van Oudenaarden, A. (2006). Noise in Gene Regulatory Networks. In: Deisboeck, T.S., Kresh, J.Y. (eds) Complex Systems Science in Biomedicine. Topics in Biomedical Engineering International Book Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-33532-2_7
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DOI: https://doi.org/10.1007/978-0-387-33532-2_7
Publisher Name: Springer, Boston, MA
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