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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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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. |
format | Online Article Text |
id | pubmed-5192451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT martinezveronicas quantificationofmicrobialphenotypes AT kromerjenso quantificationofmicrobialphenotypes |