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An analytic approximation of the feasible space of metabolic networks

Author

Listed:
  • Alfredo Braunstein

    (DISAT, Politecnico di Torino
    Human Genetics Foundation–Torino
    Collegio Carlo Alberto)

  • Anna Paola Muntoni

    (DISAT, Politecnico di Torino)

  • Andrea Pagnani

    (DISAT, Politecnico di Torino
    Human Genetics Foundation–Torino
    Istituto Nazionale di Fisica Nucleare (INFN) Via Pietro Giuria)

Abstract

Assuming a steady-state condition within a cell, metabolic fluxes satisfy an underdetermined linear system of stoichiometric equations. Characterizing the space of fluxes that satisfy such equations along with given bounds (and possibly additional relevant constraints) is considered of utmost importance for the understanding of cellular metabolism. Extreme values for each individual flux can be computed with linear programming (as flux balance analysis), and their marginal distributions can be approximately computed with Monte Carlo sampling. Here we present an approximate analytic method for the latter task based on expectation propagation equations that does not involve sampling and can achieve much better predictions than other existing analytic methods. The method is iterative, and its computation time is dominated by one matrix inversion per iteration. With respect to sampling, we show through extensive simulation that it has some advantages including computation time, and the ability to efficiently fix empirically estimated distributions of fluxes.

Suggested Citation

  • Alfredo Braunstein & Anna Paola Muntoni & Andrea Pagnani, 2017. "An analytic approximation of the feasible space of metabolic networks," Nature Communications, Nature, vol. 8(1), pages 1-9, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14915
    DOI: 10.1038/ncomms14915
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    Cited by:

    1. Chaitra Sarathy & Marian Breuer & Martina Kutmon & Michiel E Adriaens & Chris T Evelo & Ilja C W Arts, 2021. "Comparison of metabolic states using genome-scale metabolic models," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-25, November.
    2. Hazan, Aurélien, 2019. "A maximum entropy network reconstruction of macroeconomic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 1-17.

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