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.
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Leier, A., Kuo, P.D., Banzhaf, W., Burrage, K. (2006). Evolving Noisy Oscillatory Dynamics in Genetic Regulatory Networks. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds) Genetic Programming. EuroGP 2006. Lecture Notes in Computer Science, vol 3905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11729976_26
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DOI: https://doi.org/10.1007/11729976_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33143-8
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