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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 paper we investigate these properties within an artificial genomemodel originally introduced by Reil (1999). We analyze statistical propertiesof randomly 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 dynamical robustness against singlebase mutations, as well as against random changes in initial states ofregulatory dynamics that mimic stochastic fluctuations in environmentalconditions, can emerge in parallel. Point mutations at the sequence level havestrongly non-linear effects on network wiring, including as well structurallyneutral mutations and simultaneous rewiring of multiple connections, whichoccasionally lead to strong reorganization of the attractor landscape andmetastability of evolutionary dynamics. Evolved genomes exhibit characteristicpatterns on both sequence and network level.

EMERGENT NETWORK STRUCTURE, EVOLVABLE ROBUSTNESS, AND NONLINEAR EFFECTS OF POINT MUTATIONS IN AN ARTIFICIAL GENOME MODEL