@article{DavidMarashiLarhlimietal.2011,
  author    = {David, Laszlo and Marashi, Sayed-Amir and Larhlimi, Abdelhalim and Mieth, Bettina and Bockmayr, Alexander},
  title     = {FFCA a feasibility-based method for flux coupling analysis of metabolic networks},
  series = {BMC bioinformatics},
  volume    = {12},
  journal   = {BMC bioinformatics},
  number    = {12},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1471-2105},
  doi       = {10.1186/1471-2105-12-236},
  pages     = {7},
  year      = {2011},
  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/.},
  language  = {en}
}
@article{LarhlimiDavidSelbigetal.2012,
  author    = {Larhlimi, Abdelhalim and David, Laszlo and Selbig, Joachim and Bockmayr, Alexander},
  title     = {F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks},
  series = {BMC bioinformatics},
  volume    = {13},
  journal   = {BMC bioinformatics},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1471-2105},
  doi       = {10.1186/10.1186/1471-2105-13-57},
  pages     = {9},
  year      = {2012},
  abstract  = {Background: Flux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions. Results: We present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner. Conclusions: We propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.},
  language  = {en}
}