Bio-inspired reverse engineering of regulatory networks

Regulatory networks are complex networks. This paper addresses the challenge of modelling these networks. The Boolean representation is chosen and supported as a suitable representation for an abstract approach. In in-silico experiments, two different bio-inspired techniques are applied to the reverse engineering of a Boolean regulatory network: as a search method a Genetic Algorithm is applied and an indirect method based on Artificial Development and tuned to this application, is proposed. Both methods are challenged at reverse engineering a known network - the yeast cell-cycle network model. Presented results show that they are both successful in reverse engineering the considered network.