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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Online Access: | http://www.mdpi.com/2218-1989/2/4/1090 |
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doaj-1673918c03fc400c8ebe25ecd5bf92a62020-11-25T00:05:36ZengMDPI AGMetabolites2218-19892012-12-01241090112210.3390/metabo2041090Systematic Applications of Metabolomics in Metabolic EngineeringRobert A. DrommsMark P. StyczynskiThe 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.http://www.mdpi.com/2218-1989/2/4/1090metabolomicsmetabolic engineeringmass spectrometrymetabolic fluxmetabolic profilingprincipal components analysispartial least squares regressionflux balance analysisconstraint-based modelskinetic ODE models |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Robert A. Dromms Mark P. Styczynski |
spellingShingle |
Robert A. Dromms Mark P. Styczynski Systematic Applications of Metabolomics in Metabolic Engineering Metabolites metabolomics metabolic engineering mass spectrometry metabolic flux metabolic profiling principal components analysis partial least squares regression flux balance analysis constraint-based models kinetic ODE models |
author_facet |
Robert A. Dromms Mark P. Styczynski |
author_sort |
Robert A. Dromms |
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 |
publisher |
MDPI AG |
series |
Metabolites |
issn |
2218-1989 |
publishDate |
2012-12-01 |
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. |
topic |
metabolomics metabolic engineering mass spectrometry metabolic flux metabolic profiling principal components analysis partial least squares regression flux balance analysis constraint-based models kinetic ODE models |
url |
http://www.mdpi.com/2218-1989/2/4/1090 |
work_keys_str_mv |
AT robertadromms systematicapplicationsofmetabolomicsinmetabolicengineering AT markpstyczynski systematicapplicationsofmetabolomicsinmetabolicengineering |
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