FFCA: a feasibility-based method for flux coupling analysis of metabolic networks

BMC Bioinformatics

Methodology Article

Alexander Bockmayr¹  Laszlo David²  Abdelhalim Larhlimi³  Sayed-Amir Marashi⁴  Bettina Mieth⁵ 

[1] DFG-Research Center Matheon, Berlin, Germany;FB Mathematik und Informatik, Freie Universität Berlin, Arnimallee 6, D-14195, Berlin, Germany;DFG-Research Center Matheon, Berlin, Germany;FB Mathematik und Informatik, Freie Universität Berlin, Arnimallee 6, D-14195, Berlin, Germany;Berlin Mathematical School (BMS), Berlin, Germany;Department of Bioinformatics, Institute for Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, D-14476, Potsdam, Germany;FB Mathematik und Informatik, Freie Universität Berlin, Arnimallee 6, D-14195, Berlin, Germany;International Max Planck Research School for Computational Biology and Scientific Computing (IMPRS-CBSC), Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, D-14195, Berlin, Germany;FB Mathematik und Informatik, Freie Universität Berlin, Arnimallee 6, D-14195, Berlin, Germany;School of Mathematics, University of Southampton, Highfield, SO17 1BJ, Southampton, UK;

关键词: Metabolic Network;    Reversible Reaction;    Linear Inequality;    Irreversible Reaction;    Reversibility Type;   

DOI  :  10.1186/1471-2105-12-236

 received in 2011-02-10, accepted in 2011-06-15,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundFlux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. FCA classifies reactions into subsets (called coupled reaction sets) in which activity of one reaction implies activity of another reaction. Several approaches for FCA have been proposed in the literature.ResultsWe introduce a new FCA algorithm, FFCA (Feasibility-based Flux Coupling Analysis), which is based on checking the feasibility of a system of linear inequalities. We show on a set of benchmarks that for genome-scale networks FFCA is faster than other existing FCA methods.ConclusionsWe present FFCA as a new method for flux coupling analysis and prove it to be faster than existing approaches. A corresponding software tool is freely available for non-commercial use at http://www.bioinformatics.org/ffca/.

【 授权许可】

Unknown   
© David et al; licensee BioMed Central Ltd. 2011. This article is published under license to 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.