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

Abstract

<p>Abstract</p> <p>Background</p> <p>Flux 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.</p> <p>Results</p> <p>We 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.</p> <p>Conclusions</p> <p>We 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 <url>http://www.bioinformatics.org/ffca/</url>.</p