Systematic Applications of Metabolomics in Metabolic Engineering
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis...
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| Format: | Online |
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MDPI
2012
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901235/ |
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pubmed-3901235 |
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pubmed-39012352014-05-27 Systematic Applications of Metabolomics in Metabolic Engineering Dromms, Robert A. Styczynski, Mark P. Review The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. MDPI 2012-12-14 /pmc/articles/PMC3901235/ /pubmed/24957776 http://dx.doi.org/10.3390/metabo2041090 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Dromms, Robert A. Styczynski, Mark P. |
| spellingShingle |
Dromms, Robert A. Styczynski, Mark P. Systematic Applications of Metabolomics in Metabolic Engineering |
| author_facet |
Dromms, Robert A. Styczynski, Mark P. |
| author_sort |
Dromms, Robert A. |
| title |
Systematic Applications of Metabolomics in Metabolic Engineering |
| title_short |
Systematic Applications of Metabolomics in Metabolic Engineering |
| title_full |
Systematic Applications of Metabolomics in Metabolic Engineering |
| title_fullStr |
Systematic Applications of Metabolomics in Metabolic Engineering |
| title_full_unstemmed |
Systematic Applications of Metabolomics in Metabolic Engineering |
| title_sort |
systematic applications of metabolomics in metabolic engineering |
| description |
The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering. |
| publisher |
MDPI |
| publishDate |
2012 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3901235/ |
| _version_ |
1612051015896399872 |