期刊
BMC SYSTEMS BIOLOGY
卷 7, 期 -, 页码 -出版社
BMC
DOI: 10.1186/1752-0509-7-26
关键词
Metabolic networks; Simulated annealing; High-throughput data; Stress response; Network analysis
资金
- BBSRC [BB/G020434/1]
- University Research Fellowship from the Royal Society
- Biotechnology and Biological Sciences Research Council [BB/C519670/1, BB/G020434/1] Funding Source: researchfish
- BBSRC [BB/G020434/1] Funding Source: UKRI
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.
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