Inference of gene regulatory model by genetic algorithms

Presents an application of genetic algorithms (GAs) to the gene network inference problem; this is one of the active topics in recent bioinformatics. The objective is to predict a regulating network structure of the interacting genes from the observed outcome, i.e. expression pattern. The task consists of modeling the rules of regulation and inferring the network structure from the observed data. The GA is applied to training the model with observed data in order to predict the regulatory pathways, represented as an influence matrix. We have implemented a reverse engineering method based on GAs in a quantitative and linear biological framework. The merit of this approach is that it can be applied with a small amount of data, it can optimize large numbers of parameters simultaneously and it can be applied to nonlinear models. The GA implementation includes multi-stage evolution and matrix chromosomes. This method has been applied to both simulated and experimentally observed gene expression patterns. In this research, we used the knowledge of designing an electric circuit by a GA.