An Investigation of In-Silico Evolving Networks

Abstract

This honors thesis project builds on the work of Paul Francois, Eric Siggia, and Vincent Hakim that uses in-silico genetic network models to study evolution. Genetic networks are represented as systems of ordinary differential equations (ODEs). Each term in the differential equation corresponds to a chemical reaction, and the solution of the ODE, found using the fourth order Runge-Kutta algorithm, yields the levels of proteins in the system over time which is due to the expression of genes in the network. After replicating specific networks, a more general program was written which began with a very simple network and evolved to a more complex system through successive rounds of mutation and selection. Selection was achieved by solving for the levels of protein over time, and then scoring the network based on how closely these levels approached the desired behavior profile. A genetic network model similar to one presented in the original papers was successfully evolved. Then the same program was used to attempt to evolve an oscillating network system.