Modeling genetic networks to aid in understanding their function

Abstract

Data on genetic interactions is accumulating at a very high rate. Here I introduce a new tool for analyzing this data at the level of genetic networks. The software program I helped write, called Ingeneue, lets the user take a picture of a genetic network and quickly turn this into a set of ordinary differential equations. It can then search for parameters of these equations that allow the network to produce specified patterns through time or space. I discuss some of the advantages of Ingeneue over other tools. I then discuss an exploration of the neurogenic network in Drosophila using Ingeneue. I find several interesting network level properties of the modeled neurogenic network. Among these are that it is highly robust to parameter variation, and that this variation leads to an evolutionary flexibility in the patterns it can form.