Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case

Paulo A. N. Dias, Marco Seabra dos Reis, Pedro Martins, Armindo Salvador

Abstract

The detection of strong statistical bias in metabolic networks is of much interest for highlighting potential selective preferences. However, previous approaches to this problem have relied on ambiguous representations of the coupling among chemical reactions or in physically unrealizable null models, which raise interpretation problems. Here we present an approach that avoids these problems. It relies in a bipartite-graph representation of chemical reactions, and it prompts a near-comprehensive examination of statistical bias in the relative frequencies of topologically related metabolic structures within a predefined scope. It also lends naturally to a comprehensive visualization of such statistical relationships. The approach was applied to the metabolic network of Saccharomyces cerevisiae, where it highlighted a preference for sparse local structures and flagged strong context-dependences of the reversibility of reactions and of the presence/absence of some types of reactions.

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Paper Citation


in Harvard Style

A. N. Dias P., Seabra dos Reis M., Martins P. and Salvador A. (2015). Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015) ISBN 978-989-758-070-3, pages 207-212. DOI: 10.5220/0005258102070212


in Bibtex Style

@conference{bioinformatics15,
author={Paulo A. N. Dias and Marco Seabra dos Reis and Pedro Martins and Armindo Salvador},
title={Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)},
year={2015},
pages={207-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005258102070212},
isbn={978-989-758-070-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2015)
TI - Identifying Strong Statistical Bias in the Local Structure of Metabolic Networks - The Metabolic Network of Saccharomyces Cerevisiae as a Test Case
SN - 978-989-758-070-3
AU - A. N. Dias P.
AU - Seabra dos Reis M.
AU - Martins P.
AU - Salvador A.
PY - 2015
SP - 207
EP - 212
DO - 10.5220/0005258102070212