Emergent Network Structure, evolvable Robustness and non-linear Effects of Point Mutations in an Artificial Genome Model

TL;DR

This paper investigates an artificial genome model to understand genetic network structure, robustness, and mutation effects, revealing emergent properties and non-linear mutation impacts through evolutionary simulations.

Contribution

It demonstrates how robustness and network reorganization can emerge in artificial genomes, highlighting non-linear mutation effects and structural neutrality.

Findings

Genomes show robustness against mutations and environmental fluctuations.

Point mutations cause non-linear rewiring and structural neutrality.

Evolved genomes display characteristic sequence and network patterns.

Abstract

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 (1999). 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 well 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 have strongly non-linear effects on network wiring, including as well structurally neutral mutations and simultaneous rewiring of multiple connections, which occasionally lead to strong reorganization of the attractor landscape and metastability of evolutionary dynamics. Evolved genomes exhibit characteristic patterns on both sequence and network level.