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

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...

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Autores principales:	Lea Seep, Zahra Razaghi-Moghadam, Zoran Nikoloski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2b772b3cdecf41ae9121f43d4a14b07e
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spelling	oai:doaj.org-article:2b772b3cdecf41ae9121f43d4a14b07e2021-12-02T18:27:47ZReaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis10.1038/s41598-021-87643-82045-2322https://doaj.org/article/2b772b3cdecf41ae9121f43d4a14b07e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87643-8https://doaj.org/toc/2045-2322Abstract 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 $$, {\Delta }_{f} G^{0}$$ , Δ 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 $${\Delta }_{f} G^{0}$$ Δ 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 $${\Delta }_{f} G^{0}$$ Δ 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 $${\Delta }_{f} G^{0}$$ Δ 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.Lea SeepZahra Razaghi-MoghadamZoran NikoloskiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lea Seep Zahra Razaghi-Moghadam Zoran Nikoloski Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
description	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 $$, {\Delta }_{f} G^{0}$$ , Δ 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 $${\Delta }_{f} G^{0}$$ Δ 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 $${\Delta }_{f} G^{0}$$ Δ 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 $${\Delta }_{f} G^{0}$$ Δ 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.
format	article
author	Lea Seep Zahra Razaghi-Moghadam Zoran Nikoloski
author_facet	Lea Seep Zahra Razaghi-Moghadam Zoran Nikoloski
author_sort	Lea Seep
title	Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
title_short	Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
title_full	Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
title_fullStr	Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
title_full_unstemmed	Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
title_sort	reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2b772b3cdecf41ae9121f43d4a14b07e
work_keys_str_mv	AT leaseep reactionlumpinginmetabolicnetworksforapplicationwiththermodynamicmetabolicfluxanalysis AT zahrarazaghimoghadam reactionlumpinginmetabolicnetworksforapplicationwiththermodynamicmetabolicfluxanalysis AT zorannikoloski reactionlumpinginmetabolicnetworksforapplicationwiththermodynamicmetabolicfluxanalysis
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Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis

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