title: AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.
abstract: BACKGROUND
  With the continued proliferation of high-throughput biological experiments, there is a pressing need for tools to integrate the data produced in ways that produce biologically meaningful conclusions. Many microarray studies have analysed transcriptomic data from a pathway perspective, for instance by testing for KEGG pathway enrichment in sets of upregulated genes. However, the increasing availability of species-specific metabolic models provides the opportunity to analyse these data in a more objective, system-wide manner.
  
  
  RESULTS
  Here we introduce ambient (Active Modules for Bipartite Networks), a simulated annealing approach to the discovery of metabolic subnetworks (modules) that are significantly affected by a given genetic or environmental change. The metabolic modules returned by ambient are connected parts of the bipartite network that change coherently between conditions, providing a more detailed view of metabolic changes than standard approaches based on pathway enrichment.
  
  
  CONCLUSIONS
  ambient is an effective and flexible tool for the analysis of high-throughput data in a metabolic context. The same approach can be applied to any system in which reactions (or metabolites) can be assigned a score based on some biological observation, without the limitation of predefined pathways. A Python implementation of ambient is available at http://www.theosysbio.bio.ic.ac.uk/ambient.
authors.affiliation: Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2AZ, UK. w.bryant@imperial.ac.uk
authors.forename: William A
authors.initials: WA
authors.lastname: Bryant
authors.forename: Michael J E
authors.initials: MJ
authors.lastname: Sternberg
authors.forename: John W
authors.initials: JW
authors.lastname: Pinney
mesh_terms: D000465:Algorithms; D053858:Metabolic Networks and Pathways; D055432:Metabolomics; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D049490:Systems Biology / Q000379:methods*; D059467:Transcriptome