Metabolic engineering
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Metabolic engineering is the practice of optimizing
The ultimate goal of metabolic engineering is to be able to use these organisms to produce valuable substances on an industrial scale in a cost-effective manner. Current examples include producing
Since cells use these metabolic networks for their survival, changes can have drastic effects on the cells' viability. Therefore, trade-offs in metabolic engineering arise between the cells ability to produce the desired substance and its natural survival needs. Therefore, instead of directly deleting and/or overexpressing the genes that encode for metabolic enzymes, the current focus is to target the regulatory networks in a cell to efficiently engineer the metabolism.[5]
History and applications
![](http://upload.wikimedia.org/wikipedia/commons/thumb/7/7c/Epithelial-cells.jpg/135px-Epithelial-cells.jpg)
In the past, to increase the productivity of a desired metabolite, a microorganism was genetically modified by chemically induced mutation, and the mutant strain that overexpressed the desired metabolite was then chosen.[6] However, one of the main problems with this technique was that the metabolic pathway for the production of that metabolite was not analyzed, and as a result, the constraints to production and relevant pathway enzymes to be modified were unknown.[6]
In 1990s, a new technique called metabolic engineering emerged. This technique analyzes the metabolic pathway of a
At the industrial scale, metabolic engineering is becoming more convenient and cost-effective. According to the
Metabolic engineering continues to evolve in efficiency and processes aided by breakthroughs in the field of
Metabolic flux analysis
An analysis of metabolic flux can be found at Flux balance analysis
Setting up a metabolic pathway for analysis
The first step in the process is to identify a desired goal to achieve through the improvement or modification of an organism's metabolism. Reference books and online databases are used to research reactions and metabolic pathways that are able to produce this product or result. These databases contain copious genomic and chemical information including pathways for metabolism and other cellular processes. Using this research, an organism is chosen that will be used to create the desired product or result. Considerations that are taken into account when making this decision are how close the organism's metabolic pathway is to the desired pathway, the maintenance costs associated with the organism, and how easy it is to modify the pathway of the organism. Escherichia coli (E. coli) is widely used in metabolic engineering to synthesize a wide variety of products such as amino acids because it is relatively easy to maintain and modify.[14] If the organism does not contain the complete pathway for the desired product or result, then genes that produce the missing enzymes must be incorporated into the organism.
Analyzing a metabolic pathway
The completed metabolic pathway is modeled mathematically to find the theoretical yield of the product or the reaction fluxes in the cell. A flux is the rate at which a given reaction in the network occurs. Simple metabolic pathway analysis can be done by hand, but most require the use of software to perform the computations.[15] These programs use complex linear algebra algorithms to solve these models. To solve a network using the equation for determined systems shown below, one must input the necessary information about the relevant reactions and their fluxes. Information about the reaction (such as the reactants and stoichiometry) are contained in the matrices Gx and Gm. Matrices Vm and Vx contain the fluxes of the relevant reactions. When solved, the equation yields the values of all the unknown fluxes (contained in Vx).
Determining the optimal genetic manipulations
After solving for the fluxes of reactions in the network, it is necessary to determine which reactions may be altered in order to maximize the yield of the desired product. To determine what specific genetic manipulations to perform, it is necessary to use computational algorithms, such as OptGene or OptFlux.[16] They provide recommendations for which genes should be overexpressed, knocked out, or introduced in a cell to allow increased production of the desired product. For example, if a given reaction has particularly low flux and is limiting the amount of product, the software may recommend that the enzyme catalyzing this reaction should be overexpressed in the cell to increase the reaction flux. The necessary genetic manipulations can be performed using standard molecular biology techniques. Genes may be overexpressed or knocked out from an organism, depending on their effect on the pathway and the ultimate goal.[17]
Experimental measurements
In order to create a solvable model, it is often necessary to have certain fluxes already known or experimentally measured. In addition, in order to verify the effect of genetic manipulations on the metabolic network (to ensure they align with the model), it is necessary to experimentally measure the fluxes in the network. To measure reaction fluxes, carbon flux measurements are made using
See also
- Bacterial transformation
- Bioreactor
- Genetic engineering
- Synthetic biological circuit
- Synthetic biology
References
- ISSN 0717-3458
- PMID 32224264.)
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- ^ Kulkarni R, 2016. Metabolic Engineering: Biological Art of Producing Useful Chemicals. Resonance, 21 (3), 233-237.
- ^ Vemuri, G.M, Aristidou, A.A, (2005) Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks, Microbial Mol Biology Review vol. 69: 197-216
- ^ a b Voit,Eberhard.,Torres,Nestor V.(2002)." Pathways Analysis and Optimization in Metabolic Engineering." Cambridge:University Press,p.ix-x
- ^ Stephanopoulos, G. N., Aristidou, A. A., Nielsen, J. (1998). " Metabolic Engineering: Principles and Methodologies ". San Diego: Academic Press
- ^ Dellomonaco, Clementina.(2011). Engineered Reversal of the beta oxidation cycle for the Synthesis of Fuels and Chemicals. Nature 476,355-359
- ^ Patnaik, R. and Liao, J. (1994). "Engineering of Escherichia coli central metabolism for aromatic metabolite production with near theoretical yield". Appl. Environ. Microbiol. 60(11):3903-3908
- ^ Keasling D.,Jay(2010). Advanced Biofuel production in microbes. Biotechnol.J.,5,147-162
- S2CID 17214504.
- PMID 17693564.
- PMID 23656228.
- ^ University of California - Los Angeles (2008, December 18). "Genetic Modification Turns E. Coli Bacteria Into High Density Biofuel". ScienceDaily. Retrieved December 7, 2011, from https://www.sciencedaily.com/releases/2008/12/081218151652.htm
- ^ Schellenberger, J., Que, R., Fleming, R., et al. (2011). "Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0". Nature Protocols. 6(9):1290-1307
- ^ Rocha, I., Maia, P., Evangelista, P., et al. (2010). "OptFlux: an open-source software platform for in silico metabolic engineering". BMC Sys Biol. 45(4)
- ^ Work, T.S., Hinton, R., Work, E., Dobrota, M., Chard, T. (1980). "Laboratory Techniques in Biochemistry and Molecular Biology". v.8
- ^ Wiechert, W. and de Graaf, A.A. (2000). "Bidirectional Reaction Steps in Metabolic Networks: Modeling and Simulation of Carbon Isotope Labeling Experiments". Biotechnol. Bioeng. 55(1):101-117
External links
Biotechnology Industry Organization(BIO) website: