Adaptive Reverse Engineering of Gene Regulatory Networks using Genetic Algorithms

An increasingly popular model of regulation is to represent networks of genes as if they directly affect each other. Although such gene networks are phenomenological because they do not explicitly represent the proteins and metabolites that mediate cell interactions, they are a logical way of describing phenomena observed with transcription profiling. In this paper, we present a computational tool, based on genetic algorithms (GAs), which is able to predict with observed data the regulatory pathways that are represented as influence matrix. The ability to create gene networks from experimental data and use them to reason about their dynamics and design principles increase our understanding of cellular function