Integrated analysis of the gene neighbouring impact on bacterial metabolic networks
Integrated analysis of the gene neighbouring impact on bacterial metabolic networks
- Author(s): P. Bordron ; D. Eveillard ; I. Rusu
- DOI: 10.1049/iet-syb.2010.0070
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- Author(s): P. Bordron 1 ; D. Eveillard 1 ; I. Rusu 1
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View affiliations
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Affiliations:
1: Computational Biology Group (ComBi) – LINA, CNRS UMR 6241, Université de Nantes, Nantes, France
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Affiliations:
1: Computational Biology Group (ComBi) – LINA, CNRS UMR 6241, Université de Nantes, Nantes, France
- Source:
Volume 5, Issue 4,
July 2011,
p.
261 – 268
DOI: 10.1049/iet-syb.2010.0070 , Print ISSN 1751-8849, Online ISSN 1751-8857
Different levels of abstraction are needed to represent a living system. Unfortunately information of different nature is not superposable in an obvious way, but requires a dedicated framework. Because biological abstractions, i.e., genomic or metabolic information, can be easily respresented as graphs, it is intuitive to integrate them into a unique graph, in which one can perform graph analysis for investigating a given biological assumption. This study follows such a philosophy and completes a genome and metabolome combination. In a such integrated framework and as illustration, we applied a graph analysis that automatically investigates impacts of the gene adjacency to predict functional relationships between genes and reactions. Our approach, called SIPPER, creates a weighted graph, in which the weights rely on the given relationship between genes, and computes (alternative) chains of reactions catalysed by genes. This method, as a generalisation of methods already published, can be easily adapted to several biological assumptions, properties or measures. This paper evaluates SIPPER on Escherichia coli. We automatically extract subgraphs, called k-SIPs, and quantify their interest in both genomic and metabolic contexts by showing functional compounds like operons or functional modules. [Includes supplementary material]
Inspec keywords: bioinformatics; genomics; microorganisms; genetics; living systems; graph theory
Other keywords:
Subjects: Biology and medical computing; Combinatorial mathematics; Genomic techniques; Physics of subcellular structures; Algebra, set theory, and graph theory
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