Analysis of Regulatory and Interaction Networks from Clusters of Co-expressed Genes

Extracting biological insight from high-throughput genomic studies of human diseases remains a major challenge, primarily due to Our inability to recognize, evaluate and rationalize the relevant biological processes recorded from vast amounts of data. We will discuss an integrated framework combining fine-grained clustering of temporal gene expression data, selection of maximally informative clusters, based of their ability to capture the underlying dynamic transcriptional response, and the Subsequent analysis of the resulting network of interactions among genes in individual clusters. The latter are developed based on the identification of common regulators among the genes in each cluster through mining literature data. We characterize the structure of the networks in terms of fundamental graph properties, and explore biologically the implications of the scale-free character of the resulting graphs. We demonstrate the biological importance of the highly connected hubs of the networks and show how these can be further exploited as targets for potential therapies during the early onset of inflammation and for characterizing the mechanism of action of anti-inflammatory drugs. We conclude by identifying two possible challenges in network biology, namely, the nature of the interactions and the potentially limited information content of the temporal,gene expression experiments, and discuss expected implications.