An Adaptive GA-PSO Approach with Gene Clustering to Infer S-system Models of Gene Regulatory Networks

The construction of gene regulatory networks from expression data is one of the most important issues in systems biology research. However, building such networks is a tedious task, especially when both the number of genes and the complexity of gene regulation increase. In this work, we adopt the S-system model to represent the gene network and establish a methodology to infer the model. Our work mainly includes an adaptive genetic algorithm-particle swarm optimization hybrid method to infer appropriate network parameters, and a gene clustering method to decompose a large network into several smaller networks for dimension reduction. To validate the proposed methods, different series of experiments have been conducted and the results show that the proposed methods can be used to infer S-system models of gene networks efficiently and successfully.