## 6 Metabolic Flux Analysis
### \({}^{13}\)C-Metabolic Flux Analysis  
\({}^{13}\)C-MFA manages the intricate metabolic networks involving reversible, parallel, and cyclic biochemical reactions. It is being used widely for understanding the metabolic traits of OptKnock, dependent knockout strains [83], confirming the activity for different pathways [144; 145] and for identifying the metabolic blockages [146]. This approach uses \({}^{13}\)C to label metabolites and thereby provides the essential information about the metabolism of the labelled source in any metabolic pathway [147; 148; 149]. This approach of labelling metabolites can even determine the distribution of flux across the different metabolic reactions. These experiments are performed under steady-state conditions and for continuous cultures only. In the experimental setup, only the medium is supplemented with a \({}^{13}\)C labelled substrate at a particular position. Generally, for understanding the central carbon metabolism, a mix of [1-13C] and [U-13C] glucose is employed. As the proteins with \({}^{13}\)C-labelled amino acids are used for estimating the flux in a particular biochemical reaction, the cellular metabolism needs to be carefully monitored till the protein acquires the \({}^{13}\)C label. Usually \({}^{13}\)C MFA is a four step process: (a) discerning the required analytical pathway and labelling the particular substrate, (b) performing a \({}^{13}\)C-labelled experiment under steady-state circumstances and further label the pattern measurement of proteinswith labelled amino acids using GC-MS/NMR [150], (c) formulating a stoichiometric model and, depending on the previously resolved network, preparing intracellular metabolite iso-topomer balance equations, and (d) determining the label patterns of proteins with labelled amino acids and computing them from the predicted fluxes for optimizing the distribution of flux.  
In MFA, to analyze the isotope labelling pattern, the iso-topomer approach is mostly preferred [151]. In recent times, scientists have developed an open-source library, JBEI Quantitative Metabolic Modeling, to guide the design and execution of experiments related to metabolic modelling as well as flux analysis. It permits the users to determine the cellular fluxes by converting metabolomics information to isotope-labelled information from a \({}^{13}\)C-labelled experiment. This data is very helpful to develop different genetic engineering approaches for metabolically engineering the cell [152]. Depending on various factors like metabolism and pace of growth., _E. coli_ for prokaryotic studies and _S. cerevisiae_ for eukaryotic studies are most preferred, and extensively engineered model organisms are preferred for the production of different biofuels. In an _E. coli_ strain that has high fatty acid yields, \({}^{13}\)C MFA was performed to analyze its metabolism, where the gene fadE was knocked out, and 'tesA as well as fadR genes were highly upregulated. When the obtained results were compared with the wild-type strain of _E. coli_, it was observed that the metabolic flux was rerouted to the biosynthesis of the fatty acid pathway from the actual acetate pathway. These results recommended that the increase in the fatty acid precursor molecules, NADPH and acetyl-CoA, considerably diverted the fluxes towards the required pathway [153]. The fatty alcohol content and production were enhanced by employing innovative methods that involve the estimation of protein levels as well as metabolic flux quantitatively, engineering at the enzyme level, cofactor balancing, etc.; adapting these approaches yielded good amounts of fatty alcohols (1.2 g/L) in a shake flask as well as in fed batch (6.0 g/L) studies [11].