Bacteria of the genus Shewanella are known for their versatile electron-accepting capacities, which allow them to couple the decomposition of organic matter to the reduction of the various terminal electron acceptors that they encounter in their stratified environments. Owing to their diverse metabolic capabilities, shewanellae are important for carbon cycling and have considerable potential for the remediation of contaminated environments and use in microbial fuel cells. Systems-level analysis of the model species Shewanella oneidensis MR-1 and other members of this genus has provided new insights into the signal-transduction proteins, regulators, and metabolic and respiratory subsystems that govern the remarkable versatility of the shewanellae.

Many Microbe Microarrays Database (M3D) is designed to facilitate the analysis and visualization of expression data in compendia compiled from multiple laboratories. M3D contains over a thousand Affymetrix microarrays for Escherichia coli, Saccharomyces cerevisiae and Shewanella oneidensis. The expression data is uniformly normalized to make the data generated by different laboratories and researchers more comparable. To facilitate computational analyses, M3D provides raw data (CEL file) and normalized data downloads of each compendium. In addition, web-based construction, visualization and download of custom datasets are provided to facilitate efficient interrogation of the compendium for more focused analyses. The experimental condition metadata in M3D is human curated with each chemical and growth attribute stored as a structured and computable set of experimental features with consistent naming conventions and units. All versions of the normalized compendia constructed for each species are maintained and accessible in perpetuity to facilitate the future interpretation and comparison of results published on M3D data. M3D is accessible at http://m3d.bu.edu/.

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Complex networks of genes, proteins, and small molecules interact to determine cellular function. The reverse engineering or inference of gene regulatory networks using DNA sequence data, protein–DNA binding data, and observed molecular abundance data has become a major interest of the biological community. We discuss recent successes and remaining challenges in the construction of gene network models and their use for gaining biological insight. We draw lessons from the detailed discussion of several recent gene network inference studies, focusing on the novel advances and limitations of each approach. These approaches differ in the strategies used to incorporate biological data, the level of physical detail and type of model used, and the purpose of the modeling in terms of biological discovery. The approaches we discuss aim to gain insights into the logic of combinatorial regulation occurring at a gene promoter, the activity of regulatory factors distinct from their abundance, the identification of key regulators in a gene network, and the prediction of drug compound mode of action. We suggest possibilities for future directions in network inference studies.