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    Rights statement: © 2011 Xiao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the generalized singular value decomposition

Research output: Contribution to journalJournal articlepeer-review

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  • Xiaolin Xiao
  • Neil Dawson
  • Lynsey MacIntyre
  • Brian Morris
  • Judith Pratt
  • David Watson
  • Desmond Higham
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Article number72
<mark>Journal publication date</mark>16/05/2011
<mark>Journal</mark>BMC Systems Biology
Volume5
Publication StatusPublished
<mark>Original language</mark>English

Abstract

The quantification of experimentally-induced alterations in biological pathways remains a major challenge in systems biology. One example of this is the quantitative characterization of alterations in defined, established metabolic pathways from complex metabolomic data. At present, the disruption of a given metabolic pathway is inferred from metabolomic data by observing an alteration in the level of one or more individual metabolites present within that pathway. Not only is this approach open to subjectivity, as metabolites participate in multiple pathways, but it also ignores useful information available through the pairwise correlations between metabolites. This extra information may be incorporated using a higher-level approach that looks for alterations between a pair of correlation networks. In this way experimentally-induced alterations in metabolic pathways can be quantitatively defined by characterizing group differences in metabolite clustering. Taking this approach increases the objectivity of interpreting alterations in metabolic pathways from metabolomic data.

Bibliographic note

© 2011 Xiao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.