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optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space of Genome-Scale Metabolic Networks

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  • Wout Megchelenbrink
  • Martijn Huynen
  • Elena Marchiori

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. Availability: optGpSampler for Matlab and Python is available for non-commercial use at: http://cs.ru.nl/~wmegchel/optGpSampler/.

Suggested Citation

  • Wout Megchelenbrink & Martijn Huynen & Elena Marchiori, 2014. "optGpSampler: An Improved Tool for Uniformly Sampling the Solution-Space of Genome-Scale Metabolic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.
    • Handle: RePEc:plo:pone00:0086587
    DOI: 10.1371/journal.pone.0086587
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    References listed on IDEAS

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    1. David E. Kaufman & Robert L. Smith, 1998. "Direction Choice for Accelerated Convergence in Hit-and-Run Sampling," Operations Research, INFORMS, vol. 46(1), pages 84-95, February.
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    Cited by:

    1. Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2024. "Randomized Control in Performance Analysis and Empirical Asset Pricing," Papers 2403.00009, arXiv.org.
    2. Shirin Fallahi & Hans J Skaug & Guttorm Alendal, 2020. "A comparison of Monte Carlo sampling methods for metabolic network models," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-24, July.
    3. Asli Sahin & Daniel R. Weilandt & Vassily Hatzimanikatis, 2023. "Optimal enzyme utilization suggests that concentrations and thermodynamics determine binding mechanisms and enzyme saturations," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Marcelo Rivas-Astroza & Raúl Conejeros, 2020. "Metabolic flux configuration determination using information entropy," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-19, December.

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