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Genetic regulation is a key component in development, but a clearunderstanding of the structure and dynamics of genetic networks is not yet athand. In this work we investigate these properties within an artificial genomemodel originally introduced by Reil. We analyze statistical properties ofrandomly generated genomes both on the sequence- and network level, and showthat this model correctly predicts the frequency of genes in genomes as foundin experimental data. Using an evolutionary algorithm based on stabilizingselection for a phenotype, we show that robustness against single basemutations, as well as against random changes in initial network states thatmimic stochastic fluctuations in environmental conditions, can emerge inparallel. Evolved genomes exhibit characteristic patterns on both sequence andnetwork level.

Network Structure and Dynamics, and Emergence of Robustness by Stabilizing Selection in an Artificial Genome