Information theoretic dynamic Bayesian network approach for reconstructing genetic networks

A holistic understanding of genetic interactions, in the post-genomic era, is vital for analysing complex biological systems. In this paper, we present an information theory based novel gene regulatory network inference method using the dynamic Bayesian network (DBN) framework. The proposed approach, with strong theoretical underpinnings, employs mutual information based conditional independence tests to assess the regulatory relationships among genes. The method is flexible, computationally fast and allows a-priori incorporation of biological domain knowledge. We apply it to the analysis of synthetic data as well as Saccharomyces cerevisiae (yeast cell cycle) gene expression data. Performance measures applied to simulation studies show the superior performance of the proposed metho