IEEE 7^th BIBE Invited Plenary Keynote: Stochasticity and Networks in Genomic Data

摘要

Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and a realization that biological processes are driven by complex networks of interacting biological molecules. However, there is a gap between the gene lists emerging from genome sequencing projects and the network diagrams that are essential if we are to understand the link between genotype and phenotype. `Omic technologies such as DNA microarrays were once heralded as providing a window into those networks, but so far their success has been limited. Although many techniques have been developed to deal with microarray data, to date their ability to extract network relationships has been limited. We believed that by imposing constraints on the networks, based on associations reported through articles indexed in PubMed, we could more effectively extract biologically relevant results from microarray data and develop testable hypotheses that could then be validated in the laboratory. Using literature networks as constraints on a Bayesian network analysis of microarray data, we show that we are able to recover evidence for a wide range of known networks and pathways, even in experiments not explicitly designed to probe them. With a putative gene-interaction network, the problem of producing viable models of the cell remains.