%0 Journal Article %1 David2011FFCA %A David, Laszlo %A Marashi, Sayed A. %A Larhlimi, Abdelhalim %A Mieth, Bettina %A Bockmayr, Alexander %D 2011 %J BMC Bioinformatics %K flux-analysis %N 1 %P 236+ %R 10.1186/1471-2105-12-236 %T FFCA: a feasibility-based method for flux coupling analysis of metabolic networks %U http://dx.doi.org/10.1186/1471-2105-12-236 %V 12 %X BACKGROUND: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.RESULTS: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.CONCLUSIONS: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 http://www.bioinformatics.org/ffca/ webcite.

@article{David2011FFCA, abstract = {{BACKGROUND}: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.RESULTS}: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.CONCLUSIONS}: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 http://www.bioinformatics.org/ffca/ webcite.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {David, Laszlo and Marashi, Sayed A. and Larhlimi, Abdelhalim and Mieth, Bettina and Bockmayr, Alexander}, biburl = {https://www.bibsonomy.org/bibtex/214f0b3b63c42de0c008067043a92246a/karthikraman}, citeulike-article-id = {9419445}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-12-236}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/21676263}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=21676263}, doi = {10.1186/1471-2105-12-236}, interhash = {7eb2cfc5a7e56403ec7de45b8db8daff}, intrahash = {14f0b3b63c42de0c008067043a92246a}, issn = {1471-2105}, journal = {BMC Bioinformatics}, keywords = {flux-analysis}, number = 1, pages = {236+}, pmid = {21676263}, posted-at = {2011-06-16 06:18:13}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {{FFCA}: a feasibility-based method for flux coupling analysis of metabolic networks}, url = {http://dx.doi.org/10.1186/1471-2105-12-236}, volume = 12, year = 2011 }