AMBIENT: Active Modules for Bipartite Networks - using high-throughput transcriptomic data to dissect metabolic response

BMC Systems Biology

John W Pinney¹  Michael JE Sternberg¹  William A Bryant¹ 

[1]Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2AZ, UK

关键词: Network analysis;    Stress response;    High-throughput data;    Simulated annealing;    Metabolic networks;   

Others  :  1142948
DOI  :  10.1186/1752-0509-7-26

 received in 2012-09-21, accepted in 2013-03-01,  发布年份 2013

【 摘 要 】

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 webcite.

【 授权许可】

   
2013 Bryant et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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