Estimation of gene regulatory network by genetic algorithm and pairwise correlation analysis

Constructing genetic network model from microarray data is an important approach to understanding the functions of the genes. Proposed in this paper is the use of pair-wise correlation analysis to capture regulation and co-regulation among genes. It considers pair-wise p-metrics correlation between the expression of the genes and also the change in expression of the genes. The method is used alongside meta heuristic approach to construct gene regulatory networks of difference and differential equation model from microarray data. The evolutionary algorithms are used to find the structure of the model with highest criteria. Using the result of the analysis improves the performance of the meta heuristics and allows us to extract relations from gene expression of E. coli and S. cerevisiae.