title: optGpSampler: an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks.
abstract: Constraint-based models of metabolic networks are typically underdetermined, because they contain more reactions than metabolites. Therefore the solutions to this system do not consist of unique flux rates for each reaction, but rather a space of possible flux rates. By uniformly sampling this space, an estimated probability distribution for each reaction's flux in the network can be obtained. However, sampling a high dimensional network is time-consuming. Furthermore, the constraints imposed on the network give rise to an irregularly shaped solution space. Therefore more tailored, efficient sampling methods are needed. We propose an efficient sampling algorithm (called optGpSampler), which implements the Artificial Centering Hit-and-Run algorithm in a different manner than the sampling algorithm implemented in the COBRA Toolbox for metabolic network analysis, here called gpSampler. Results of extensive experiments on different genome-scale metabolic networks show that optGpSampler is up to 40 times faster than gpSampler. Application of existing convergence diagnostics on small network reconstructions indicate that optGpSampler converges roughly ten times faster than gpSampler towards similar sampling distributions. For networks of higher dimension (i.e. containing more than 500 reactions), we observed significantly better convergence of optGpSampler and a large deviation between the samples generated by the two algorithms.
  
  
  BACKGROUND
  optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.
authors.affiliation: Institute for Computing and Information Sciences (ICIS), Radboud University Nijmegen, Nijmegen, The Netherlands ; Centre for Molecular and Biomolecular Informatics (CMBI), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands ; Centre for Systems Biology and Bioenergetics (CSBB), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
authors.forename: Wout
authors.initials: W
authors.lastname: Megchelenbrink
authors.affiliation: Centre for Molecular and Biomolecular Informatics (CMBI), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands ; Centre for Systems Biology and Bioenergetics (CSBB), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
authors.forename: Martijn
authors.initials: M
authors.lastname: Huynen
authors.affiliation: Institute for Computing and Information Sciences (ICIS), Radboud University Nijmegen, Nijmegen, The Netherlands ; Centre for Systems Biology and Bioenergetics (CSBB), Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
authors.forename: Elena
authors.initials: E
authors.lastname: Marchiori
mesh_terms: D000465:Algorithms; D048013:Acetivibrio thermocellus / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics; D016678:Genome / Q000235:genetics*; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics