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Concept
Metabolic flux analysis
Résumé
Metabolic flux analysis (MFA) is an experimental fluxomics technique used to examine production and consumption rates of metabolites in a biological system. At an intracellular level, it allows for the quantification of metabolic fluxes, thereby elucidating the central metabolism of the cell. Various methods of MFA, including isotopically stationary metabolic flux analysis, isotopically non-stationary metabolic flux analysis, and thermodynamics-based metabolic flux analysis, can be coupled with stoichiometric models of metabolism and mass spectrometry methods with isotopic mass resolution to elucidate the transfer of moieties containing isotopic tracers from one metabolite into another and derive information about the metabolic network. Metabolic flux analysis (MFA) has many applications such as determining the limits on the ability of a biological system to produce a biochemical such as ethanol, predicting the response to gene knockout, and guiding the identification of bottleneck enzymes in metabolic networks for metabolic engineering efforts.
Metabolic flux analysis may use 13C-labeled isotope tracers for isotopic labeling experiments. Nuclear magnetic resonance (NMR) techniques and mass spectrometry may then be used to measure metabolite labeling patterns to provide information for determination of pathway fluxes. Because MFA typically requires rigorous flux calculation of complex metabolic networks, publicly available software tools have been developed to automate MFA and reduce its computational burden.
Although using a stoichiometric balance and constraints of the metabolites comprising the metabolic network can elucidate fluxes, this approach has limitations including difficulty in stimulating fluxes through parallel, cyclic, and reversible pathways. Moreover, there is limited insight on how metabolites interconvert in a metabolic network without the use of isotope tracers. Thus, the use of isotopes has become the dominant technique for MFA.
Isotope labeling experiments are optimal for gathering experimental data necessary for MFA.
Source officielle
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