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.
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.
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.
%0 Journal Article
%1 Bryant2013AMBIENT
%A Bryant, William A.
%A Sternberg, Michael J.
%A Pinney, John W.
%D 2013
%J BMC systems biology
%K bipartite metabolic-networks networks
%N 1
%P 26+
%R 10.1186/1752-0509-7-26
%T AMBIENT: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.
%U http://dx.doi.org/10.1186/1752-0509-7-26
%V 7
%X 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.
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.
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.
@article{Bryant2013AMBIENT,
abstract = {
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.
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.
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.
},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Bryant, William A. and Sternberg, Michael J. and Pinney, John W.},
biburl = {https://www.bibsonomy.org/bibtex/2d25fd2dc24ac7dd9d5f4e401a72252a9/karthikraman},
citeulike-article-id = {12214788},
citeulike-linkout-0 = {http://dx.doi.org/10.1186/1752-0509-7-26},
citeulike-linkout-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3656802/},
citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/23531303},
citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=23531303},
doi = {10.1186/1752-0509-7-26},
interhash = {bcd575fce8aef103c9e214fe73164622},
intrahash = {d25fd2dc24ac7dd9d5f4e401a72252a9},
issn = {1752-0509},
journal = {BMC systems biology},
keywords = {bipartite metabolic-networks networks},
number = 1,
pages = {26+},
pmcid = {PMC3656802},
pmid = {23531303},
posted-at = {2013-03-27 04:44:00},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {{AMBIENT}: Active Modules for Bipartite Networks--using high-throughput transcriptomic data to dissect metabolic response.},
url = {http://dx.doi.org/10.1186/1752-0509-7-26},
volume = 7,
year = 2013
}