Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction

Mariana Recamonde Mendoza, Ana L. C. Bazzan

Abstract: The discovery of the structure of genetic regulatory networks is of great interest for biologists and geneticists due to its pivotal role in organisms' metabolism. In the present paper we aim to investigate the inference power of genetic regulatory networks modeled as random boolean networks without the use of any prior biological information. The solutions space is explored by means of genetic algorithms, whose main goal is to find a consistent network given the target data obtained from biological experiments. We show that this approach succeeds in reconstructing a model with satisfactory level of accuracy, representing an useful tool to guide biologist towards the most probable interactions between the target genes.