The evolvability of gene regulatory networks for cortical arealisation

Computational models of gene regulatory networks are used to explore the interactions of development and evolvability. Pattern formation in the neocortex is modelled using Boolean Networks, Artificial Neural Networks and a novel Artificial Gene Network. The computational similarities between real gene networks and these models are described. This raises potential applications for reverse-engineering pattern forming gene networks, illuminating their unknown structure through statistical analyses. The ability of these networks to evolve is investigated under various conditions. The structure of the genotype to phenotype map in these artificial cases is probed to attempt to answer the question of what it is about this developmental system which makes it so highly evolvable?