Topology and dynamics of an artificial genetic regulatory network model

Abstract

This thesis presents some of the methods of studying models of regulatory networks using mathematical and computational formalisms. A basic review of the biology behind gene regulation is introduced along with the formalisms used for modelling networks of such regulatory interactions. Topological measures of large-scale complex networks are discussed and then applied to a specific artificial regulatory network model created through a duplication and divergence mechanism. Such networks share topological features with natural transcriptional regulatory networks. Thus, it may be the case that the topologies inherent in natural networks may be primarily due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks are also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model.