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Title: Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 massmore » balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. Finally, we also give examples of new applications made possible by elucidating the atomic structure of conserved moieties.« less
 [1] ;  [1]
  1. Univ. of Luxembourg, Esch-sur-Alzette (Luxembourg). Luxembourg Centre for Systems Biomedicine
Publication Date:
Grant/Contract Number:
Published Article
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 12; Journal Issue: 11; Journal ID: ISSN 1553-7358
Public Library of Science
Research Org:
Univ. of Luxembourg, Esch-sur-Alzette (Luxembourg)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); Luxembourg National Research Fund (FNR) (Luxembourg)
Country of Publication:
United States
59 BASIC BIOLOGICAL SCIENCES; metabolic networks; metabolites; algorithms; carbon; graph theory; vector spaces; metabolic labeling; oxygen
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1360088