Abstract
Over the past few years, it has been increasingly recognized that stochastic mechanisms play a key role in the dynamics of biological systems. Genetic networks are one example where molecular-level fluctuations are of particular importance. Here stochasticity in the expression of gene products can result in genetically identical cells displaying significant variation in biochemical or physical attributes. This variation can influence individual and population-level fitness.
Cells also receive noisy signals from their environments, perform detection and transduction with stochastic biochemistry. Several mechanisms, including cascades and feedback loops, allow the cell to manipulate noisy signals and maintain signal fidelity. Furthermore through a biochemical implementation of Bayes’s rule, it has been shown that genetic networks can act as inference modules, inferring from intracellular conditions the likely state of the extracellular environment.
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Charlebois, D.A., Perkins, T.J., Kaern, M. (2011). Stochastic Gene Expression and the Processing and Propagation of Noisy Signals in Genetic Networks. In: Niiranen, S., Ribeiro, A. (eds) Information Processing and Biological Systems. Intelligent Systems Reference Library, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19621-8_5
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DOI: https://doi.org/10.1007/978-3-642-19621-8_5
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