A method for modeling gene regulatory network with personal computer cluster

Abstract

It is one of the serious challenges in bioinformatics that the algorithms are used to study cell's molecular pathways and to simulate cell's molecular network so as to reveal tumor's molecular characteristics at a molecular level. In this paper we aim at disclosing gene nonlinear interactions under parallel computing environment. First based on graph-coloring scheme, determine the types of the higher order logical relationship among multi-genes to get their expression pattern. Secondly gets the sample supporting degree for the logical expression patterns. Thirdly take the supporting degree for the weight of the regulatory network model to show the probability with which the logical relation happens among these samples, and further build a weighted and directed regulatory network of gene expression. Finally apply this method to the colon cancer mRNA micro-array dataset to build a higher order logical regulatory network and to visualize the tumors' molecular signal pathways. Results show that with this way we can not only extract multi-gene's nonlinear logical relations hidden in the gene expression profile but also analyze effectively tumor cell's signal pathways and build a regulatory network of gene expression, which provides a tool for the study on tumor-gene's molecular bioinformatics. © 2009 Springer Berlin Heidelberg.