Comparison of metabolic states using genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting...

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Autores principales:	Chaitra Sarathy, Marian Breuer, Martina Kutmon, Michiel E Adriaens, Chris T Evelo, Ilja C W Arts
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a7c451aa0d954083a97ba7020341a4e8
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spelling	oai:doaj.org-article:a7c451aa0d954083a97ba7020341a4e82021-12-02T19:57:59ZComparison of metabolic states using genome-scale metabolic models.1553-734X1553-735810.1371/journal.pcbi.1009522https://doaj.org/article/a7c451aa0d954083a97ba7020341a4e82021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009522https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.Chaitra SarathyMarian BreuerMartina KutmonMichiel E AdriaensChris T EveloIlja C W ArtsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 11, p e1009522 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Chaitra Sarathy Marian Breuer Martina Kutmon Michiel E Adriaens Chris T Evelo Ilja C W Arts Comparison of metabolic states using genome-scale metabolic models.
description	Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.
format	article
author	Chaitra Sarathy Marian Breuer Martina Kutmon Michiel E Adriaens Chris T Evelo Ilja C W Arts
author_facet	Chaitra Sarathy Marian Breuer Martina Kutmon Michiel E Adriaens Chris T Evelo Ilja C W Arts
author_sort	Chaitra Sarathy
title	Comparison of metabolic states using genome-scale metabolic models.
title_short	Comparison of metabolic states using genome-scale metabolic models.
title_full	Comparison of metabolic states using genome-scale metabolic models.
title_fullStr	Comparison of metabolic states using genome-scale metabolic models.
title_full_unstemmed	Comparison of metabolic states using genome-scale metabolic models.
title_sort	comparison of metabolic states using genome-scale metabolic models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a7c451aa0d954083a97ba7020341a4e8
work_keys_str_mv	AT chaitrasarathy comparisonofmetabolicstatesusinggenomescalemetabolicmodels AT marianbreuer comparisonofmetabolicstatesusinggenomescalemetabolicmodels AT martinakutmon comparisonofmetabolicstatesusinggenomescalemetabolicmodels AT michieleadriaens comparisonofmetabolicstatesusinggenomescalemetabolicmodels AT christevelo comparisonofmetabolicstatesusinggenomescalemetabolicmodels AT iljacwarts comparisonofmetabolicstatesusinggenomescalemetabolicmodels
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Comparison of metabolic states using genome-scale metabolic models.

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