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Evolving noisy oscillatory dynamics in genetic regulatory networks

By Andre Leier, P. Dwight Kuo, Wolfgang Banzhaf and Kevin Burrage

Abstract

We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise

Topics: Computer Science, Theory & Methods, Stochastic Simulation, Escherichia-coli, Expression, Evolution, Cells
Publisher: Springer
Year: 2006
DOI identifier: 10.1007/11729976_26
OAI identifier: oai:eprints.qut.edu.au:46032
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