title: Random sampling of elementary flux modes in large-scale metabolic networks.
abstract: BACKGROUND
  The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set.
  
  
  RESULTS
  Our algorithm is an adaptation of the canonical basis approach, where we add an additional filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the number of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and observed that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for analysis and optimization of metabolic networks.
  
  
  BACKGROUND
  Source code for a cross-platform implementation in Python is freely available at http://code.google.com/p/emsampler.
  
  
  BACKGROUND
  dmachado@deb.uminho.pt
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: IBB-Institute for Biotechnology and Bioengineering/Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. dmachado@deb.uminho.pt
authors.forename: Daniel
authors.initials: D
authors.lastname: Machado
authors.forename: Zita
authors.initials: Z
authors.lastname: Soons
authors.forename: Kiran Raosaheb
authors.initials: KR
authors.lastname: Patil
authors.forename: Eugénio C
authors.initials: EC
authors.lastname: Ferreira
authors.forename: Isabel
authors.initials: I
authors.lastname: Rocha
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D019802:Succinic Acid / Q000378:metabolism
chemical_list: D019802:Succinic Acid