ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. B...

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Autores principales: Sudharshan Ravi, Rudiyanto Gunawan
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Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/ae273aeebba34f3db087b274601b0e01
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spelling oai:doaj.org-article:ae273aeebba34f3db087b274601b0e012021-12-02T19:57:57ZΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.1553-734X1553-735810.1371/journal.pcbi.1009589https://doaj.org/article/ae273aeebba34f3db087b274601b0e012021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009589https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.Sudharshan RaviRudiyanto GunawanPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11, p e1009589 (2021)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
Sudharshan Ravi
Rudiyanto Gunawan
ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
description Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.
format article
author Sudharshan Ravi
Rudiyanto Gunawan
author_facet Sudharshan Ravi
Rudiyanto Gunawan
author_sort Sudharshan Ravi
title ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
title_short ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
title_full ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
title_fullStr ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
title_full_unstemmed ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
title_sort δfba-predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/ae273aeebba34f3db087b274601b0e01
work_keys_str_mv AT sudharshanravi dfbapredictingmetabolicfluxalterationsusinggenomescalemetabolicmodelsanddifferentialtranscriptomicdata
AT rudiyantogunawan dfbapredictingmetabolicfluxalterationsusinggenomescalemetabolicmodelsanddifferentialtranscriptomicdata
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