Evolving noisy oscillatory dynamics in genetic regulatory networks

Authors:
André Leier;P. Dwight Kuo;Wolfgang Banzhaf;Kevin Burrage
Affiliations:
Advanced Computational Modelling Centre, University of Queensland, Brisbane, Australia;Department of Bioengineering, University of California, San Diego, La Jolla, CA;Dept. of Computer Science, Memorial University of Newfoundland, St. John's, Canada;Advanced Computational Modelling Centre, University of Queensland, Brisbane, Australia
Venue:
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Year:
2006

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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.