Evolving Genetic Regulatory Networks Performing as Stochastic Switches

Created by W.Langdon from gp-bibliography.bib Revision:1.9225

@InProceedings{Leier:2006:AISB,

• author = "Andre Leier and Kevin Burrage",
• title = "Evolving Genetic Regulatory Networks Performing as Stochastic Switches",
• booktitle = "Proceedings of AISB'06: Adaptation in Artificial and Biological Systems",
• year = "2006",
• editor = "Tim Kovacs and James A. R. Marshall",
• volume = "3",
• pages = "150--157",
• address = "University of Bristol, UK",
• organisation = "AISB Society for the Study of Artificial Intelligence and the Simulation of Behaviour",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://aisb.org.uk/wp-content/uploads/2019/12/AISB06_vol3.pdf",
• URL = "https://eprints.qut.edu.au/46189/",
• size = "8 pages",
• abstract = "Recent studies have shown that small genetic regulatory networks (GRNs) can be evolved in silico displaying certain dynamics in the underlying mathematical model. It is expected that evolutionary approaches can help to gain a better understanding of biological design principles and assist in the engineering of genetic networks. To take the stochastic nature of GRNs into account, our evolutionary approach models GRNs as biochemical reaction networks based on simple enzyme kinetics and simulates them by using Gillespie stochastic simulation algorithm (SSA). We have already demonstrated the relevance of considering intrinsic stochasticity by evolving GRNs that show oscillatory dynamics in the SSA but not in the ODE regime. Here, we present and discuss first results in the evolution of GRNs performing as stochastic switches.",
• notes = "Advanced Computational Modelling Centre, University of Queensland, Brisbane, QLD 4072 Australia",

}

Genetic Programming entries for Andre Leier Kevin Burrage

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