title: FFCA: a feasibility-based method for flux coupling analysis of metabolic networks.
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/.
authors.affiliation: DFG-Research Center Matheon, Berlin, Germany.
authors.forename: Laszlo
authors.initials: L
authors.lastname: David
authors.forename: Sayed-Amir
authors.initials: SA
authors.lastname: Marashi
authors.forename: Abdelhalim
authors.initials: A
authors.lastname: Larhlimi
authors.forename: Bettina
authors.initials: B
authors.lastname: Mieth
authors.forename: Alexander
authors.initials: A
authors.lastname: Bockmayr
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000378:metabolism; D016480:Helicobacter pylori / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D012984:Software