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  Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis

Seep, L., Razaghi-Moghadam, Z., & Nikoloski, Z. (2021). Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Scientific Reports, 11: 8544. doi:10.1038/s41598-021-87643-8.

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Seep, Lea1, Author
Razaghi-Moghadam, Z.2, Author              
Nikoloski, Z.2, Author              
Affiliations:
1external, ou_persistent22              
2Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753310              

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 Abstract: Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation $$, {\Delta }_{f} G^{0}$$, of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown $${\Delta }_{f} G^{0}$$. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown $${\Delta }_{f} G^{0}$$whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown $${\Delta }_{f} G^{0}$$are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41598-021-87643-8
Other: Seep2021
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Title: Scientific Reports
  Abbreviation : Sci. Rep.
Source Genre: Journal
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Publ. Info: London, UK : Nature Publishing Group
Pages: 8544 Volume / Issue: 11 Sequence Number: 8544 Start / End Page: - Identifier: ISSN: 2045-2322
CoNE: https://pure.mpg.de/cone/journals/resource/2045-2322