Gene Interactions Sub-networks and Soft Computing

Abstract

Analysis of gene interaction networks is crucial for understanding fundamental cellular processes involving growth, development, hormone secretion and cellular communication.A gene interaction network comprises of proteins and genes binding to each other, and acting as a complex input-output system for controlling cellular functions. A small set of genes take part in a cellular process of interest, while a single gene may be involved in more than one cellular process at the same time. Soft computing is a consortium of methodologies that works synergistically and provides flexible information processing capability for handling real life ambiguous situations. The tools include fuzzy sets, evolutionary computing, neurocomputing, and their hybridizations. We discuss some existing literature pertaining to the use of soft computing and other classical methodologies in the reverse engineering of gene interaction networks. As a case study we describe here a soft computing based strategy for biclustering and the use of rank correlation, for extracting rank correlated gene interaction sub-networks from microarray data. Experimental results on time series gene expression data from Yeast were biologically validated based on standard databases and information from literature.