Network computation and evolutionary computation provide analytic tools to explore the fundamental computational properties of biological systems, abstracting away from the myriad details of real biology. The value of such computational modelling depends on matching the level of abstraction to the research aims of the project. Network analysis tools have been widely used to model genetic regulatory networks at a variety of levels from biologically detailed to abstract Boolean models. In this project we are investigating how models of genetic regulatory networks can be derived from artificial genomes represented as nucleotide sequences. We report simulations mapping an artificial genome to a network, and the application of such genetic networks to the ontogeny of organisms.