Inference of gene regulatory networks using S-system and differential evolution

In this work we present an improved evolutionary method for inferring S-system model of genetic networks from the time series data of gene expression. We employed Differential Evolution (DE) for optimizing the network parameters to capture the dynamics in gene expression data. In a preliminary investigation we ascertain the suitability of DE for a multimodal and strongly non-linear problem like gene network estimation. An extension of the fitness function for attaining the sparse structure of biological networks has been proposed. For estimating the parameter Values more accurately an enhancement of the optimization procedure has been also suggested. The effectiveness of the proposed method was justified performing experiments on a genetic network using different numbers of artificially created time series data.