Reverse-Engineering Microbial Gene Networks with Large-Scale RNA Expression Data Sets
Michael Driscoll, Boston University
Identifying the global structure and dynamics of gene networks in microbes is an important first step towards their engineering for practical applications. Microbes are capable of a broad range of functions, from energy transduction (respiration) and chemical synthesis (growth) to signal processing (chemotaxis). Despite their differences, these functions are all regulated by gene networks.
We have developed a supervised learning method for the reconstruction of gene networks from large-scale RNA expression data sets. Our method combines existing genetic knowledge with a Bayesian inference approach to identify the most statistically probable network structure from a set of RNA expression experiments.
We are applying these network inference methods to the respiratory pathways of Shewanella oneidensis MR-1. Due to its ability to naturally respire and reduce heavy-metals such as Uranium, this gram-negative microbe is a promising candidate for environmental remediation. To generate the RNA expression data needed for these studies, we have designed the first high-density Affymetrix oligonucleotide microarray for Shewanella.
We hope that the computational methods and knowledge derived from our work in Shewanella will have wider applications for microbial engineering, including the improvement of antibiotics and the prospect of biologically-derived energy sources.
Abstract Author(s): Michael E. Driscoll, James J. Collins, Timothy S. Gardner