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Identifying All Moiety Conservation Laws in Genome-Scale Metabolic Networks

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  • Andrea De Martino
  • Daniele De Martino
  • Roberto Mulet
  • Andrea Pagnani

Abstract

The stoichiometry of a metabolic network gives rise to a set of conservation laws for the aggregate level of specific pools of metabolites, which, on one hand, pose dynamical constraints that cross-link the variations of metabolite concentrations and, on the other, provide key insight into a cell's metabolic production capabilities. When the conserved quantity identifies with a chemical moiety, extracting all such conservation laws from the stoichiometry amounts to finding all non-negative integer solutions of a linear system, a programming problem known to be NP-hard. We present an efficient strategy to compute the complete set of integer conservation laws of a genome-scale stoichiometric matrix, also providing a certificate for correctness and maximality of the solution. Our method is deployed for the analysis of moiety conservation relationships in two large-scale reconstructions of the metabolism of the bacterium E. coli, in six tissue-specific human metabolic networks, and, finally, in the human reactome as a whole, revealing that bacterial metabolism could be evolutionarily designed to cover broader production spectra than human metabolism. Convergence to the full set of moiety conservation laws in each case is achieved in extremely reduced computing times. In addition, we uncover a scaling relation that links the size of the independent pool basis to the number of metabolites, for which we present an analytical explanation.

Suggested Citation

  • Andrea De Martino & Daniele De Martino & Roberto Mulet & Andrea Pagnani, 2014. "Identifying All Moiety Conservation Laws in Genome-Scale Metabolic Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-11, July.
    • Handle: RePEc:plo:pone00:0100750
    DOI: 10.1371/journal.pone.0100750
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    References listed on IDEAS

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    1. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    2. Andreas Wagner & David Fell, 2000. "The Small World Inside Large Metabolic Networks," Working Papers 00-07-041, Santa Fe Institute.
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    Cited by:

    1. Aur'elien Hazan, 2017. "Stock-flow consistent macroeconomic model with nonuniform distributional constraint," Papers 1708.00645, arXiv.org.
    2. Hulda S Haraldsdóttir & Ronan M T Fleming, 2016. "Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-30, November.

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