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Quantification of Microbial Phenotypes

Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and...

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Autores principales: Martínez, Verónica S., Krömer, Jens O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5192451/
https://www.ncbi.nlm.nih.gov/pubmed/27941694
http://dx.doi.org/10.3390/metabo6040045
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author Martínez, Verónica S.
Krömer, Jens O.
author_facet Martínez, Verónica S.
Krömer, Jens O.
author_sort Martínez, Verónica S.
collection PubMed
description Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by (13)C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis.
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spelling pubmed-51924512017-01-03 Quantification of Microbial Phenotypes Martínez, Verónica S. Krömer, Jens O. Metabolites Review Metabolite profiling technologies have improved to generate close to quantitative metabolomics data, which can be employed to quantitatively describe the metabolic phenotype of an organism. Here, we review the current technologies available for quantitative metabolomics, present their advantages and drawbacks, and the current challenges to generate fully quantitative metabolomics data. Metabolomics data can be integrated into metabolic networks using thermodynamic principles to constrain the directionality of reactions. Here we explain how to estimate Gibbs energy under physiological conditions, including examples of the estimations, and the different methods for thermodynamics-based network analysis. The fundamentals of the methods and how to perform the analyses are described. Finally, an example applying quantitative metabolomics to a yeast model by (13)C fluxomics and thermodynamics-based network analysis is presented. The example shows that (1) these two methods are complementary to each other; and (2) there is a need to take into account Gibbs energy errors. Better estimations of metabolic phenotypes will be obtained when further constraints are included in the analysis. MDPI 2016-12-09 /pmc/articles/PMC5192451/ /pubmed/27941694 http://dx.doi.org/10.3390/metabo6040045 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Martínez, Verónica S.
Krömer, Jens O.
Quantification of Microbial Phenotypes
title Quantification of Microbial Phenotypes
title_full Quantification of Microbial Phenotypes
title_fullStr Quantification of Microbial Phenotypes
title_full_unstemmed Quantification of Microbial Phenotypes
title_short Quantification of Microbial Phenotypes
title_sort quantification of microbial phenotypes
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5192451/
https://www.ncbi.nlm.nih.gov/pubmed/27941694
http://dx.doi.org/10.3390/metabo6040045
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