ArticleBMC bioinformatics2011
FFCA: a feasibility-based method for flux coupling analysis of metabolic networks.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.International journal of molecular sciences · 2023Review
- Quantitative flux coupling analysis.Journal of mathematical biology · 2019Article
- Evolutionary design principles in metabolism.Proceedings. Biological sciences · 2019Review
- SteadyCom: Predicting microbial abundances while ensuring community stability.PLoS computational biology · 2017Article
- Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance.Scientific reports · 2014Article
- Studying the relationship between robustness against mutations in metabolic networks and lifestyle of organisms.TheScientificWorldJournal · 2013Article
- F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks.BMC bioinformatics · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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/.
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Registered trials
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