Evolving Genetic Regulatory Networks Using an Artificial Genome

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Evolving Genetic Regulatory Networks Using an Artificial Genome
Boolean models of genetic regulatory networks (GRNs) have been shown to exhibit many of the characteristic dynamics of real GRNs, with gene expression patterns settling to point attractors or limit cycles, or displaying chaotic behaviour, depending upon the connectivity of the network and the relative proportions of excitatory and inhibitory interactions. This range of behaviours is only apparent, however, when the nodes of the GRN are updated synchronously, a biologically implausible state of affairs. In this paper we demonstrate that evolution can produce GRNs with interesting dynamics under an asynchronous update scheme. We use an Artificial Genome to generate networks which exhibit limit cycle dynamics when updated synchronously, but collapse to a point attractor when updated asynchronously. Using a hill climbing algorithm the networks are then evolved using a fitness function which rewards patterns of gene expression which revisit as many previously seen states as possible. The f...
Jennifer Hallinan, Janet Wiles
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where APBC
Authors Jennifer Hallinan, Janet Wiles
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