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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic

William A Bryant1, Michael J E Sternberg, John W Pinney

  • 1Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London, SW7 2AZ, UK. w.bryant@imperial.ac.uk

BMC Systems Biology
|March 28, 2013
PubMed
Summary

We developed ambient, a novel tool for analyzing high-throughput biological data. This method identifies affected metabolic subnetworks, offering a systems-level view of biological changes beyond traditional pathway enrichment.

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Area of Science:

  • Systems Biology
  • Metabolomics
  • Bioinformatics

Background:

  • High-throughput biological experiments generate vast datasets requiring advanced integration tools.
  • Current transcriptomic data analysis often relies on pathway enrichment, which may be limited.
  • The availability of species-specific metabolic models enables more objective, system-wide analyses.

Purpose of the Study:

  • To introduce ambient (Active Modules for Bipartite Networks), a computational approach for discovering biologically meaningful metabolic subnetworks.
  • To provide a tool for analyzing high-throughput data within a metabolic context.

Main Methods:

  • Ambient utilizes a simulated annealing approach to identify metabolic subnetworks (modules).
  • It analyzes changes in connected parts of a bipartite network across different conditions.
  • The method scores reactions or metabolites based on biological observations.

Main Results:

  • Ambient discovers metabolic subnetworks significantly affected by genetic or environmental changes.
  • The identified modules represent coherent changes within the metabolic network.
  • This approach offers a more detailed view of metabolic alterations compared to pathway enrichment.

Conclusions:

  • Ambient is an effective and flexible tool for high-throughput data analysis in metabolomics.
  • The approach is applicable to diverse biological systems without reliance on predefined pathways.
  • A Python implementation of ambient is publicly available for research use.