Analysis of time course microarray data for dynamic inference of gene regulatory networks

Gene regulatory network inference has manifold applications in the field of computational biology and facilitates the study of cellular responses and biological processes, gene functions and accurate prediction of molecular behavior in organisms. The process of analyzing networks to model cellular and metabolic pathways and signaling mechanisms in organisms is a challenging, albeit extremely important problem in the field of computational biology.;In this thesis, we introduce a novel approach derived from combining Boolean logic based gene network inference algorithms and concepts of statistical co-information theory to dynamically model regulatory networks from time course based microarray experiments on multiple high-content datasets. We test our established framework on two datasets derived from a powerful, high-density chicken cDNA microarray based time-course transcriptional scans across multiple tissues of broiler chickens divergently selected for two extreme phenotypes---fat and lean. The analysis and inference of gene regulatory networks modeled from the two datasets give us powerful insights into understanding cellular and metabolic pathways and biological processes that determine phenotypic diversity in the broiler chicken.