Inference of gene regulatory model by genetic algorithms

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