Abstract
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/.
Highlights
Flux coupling analysis (FCA) is a useful method for finding dependencies between fluxes of a metabolic network at steady-state
We introduce a new approach for flux coupling analysis, feasibility-based” flux coupling analysis method (FFCA), which is based on feasibility testing
Taking into account the previously mentioned strategies for improving flux coupling analysis, we propose the following procedure for FFCA: 1. i, j Î Irev: In this case, we check the feasibility of two systems of linear inequalities: vi = 1, vj = 0, Sv = 0, vr ≥ 0, for all r ∈ Irr, (P1)
Summary
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. Constraint-based analysis of metabolic networks has become increasingly important for describing and predicting the behavior of living organisms [1,2]. Flux coupling analysis (FCA) [6] is a useful method for finding dependencies between fluxes of a metabolic network at steady-state. If a non-zero flux through a reaction in steady-state implies a non-zero flux through another reaction, the first reaction is said to be coupled to the second reaction. Several studies have used FCA for exploring various biological questions such as network evolution [7,8,9], gene essentiality [7], analysis of experimentally measured fluxes [10,11] or
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
