Emergent Network Structure, Evolvable Robustness, And Nonlinear Effects Of Point Mutations In An Artificial Genome Model

Genetic regulation is a key component in development, but a clear understanding of the structure and dynamics of genetic networks is not yet at hand. In this paper we investigate these properties within an artificial genome model originally introduced by Reil [Proc. 5th European Conf. Artificial Life(Springer, 1999), pp. 457–466]. We analyze statistical properties of randomly generated genomes both on the sequence and network level, and show that this model correctly predicts the frequency of genes in genomes as found in experimental data. Using an evolutionary algorithm based on stabilizing selection for a phenotype, we show that dynamical robustness against single base mutations, as against random changes in initial states of regulatory dynamics that mimic stochastic fluctuations in environmental conditions, can emerge in parallel. Point mutations at the sequence level can have strongly nonlinear effects on network wiring, including structurally neutral mutations and simultaneous rewiring of multiple connections, which occasionally lead to strong reorganization of the attractor landscape and metastability of evolutionary dynamics. Similar to real genomes, evolved artificial genomes exhibit both highly conserved regions, as well as regions that are characterized by a high rate of accepted base substitutions.