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Highlight on software that predicts overexpression strategies for overproduction of metabolites Wenfa Ng 01 January 2020.pdf (22.07 kB)

Highlight on software that predicts overexpression strategies for overproduction of metabolites

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posted on 2020-01-01, 02:40 authored by Wenfa NgWenfa Ng
By tuning the expression of target genes through a combination of overexpression and downregulation, desired molecules of interest or metabolites could be overproduced in selected microbial hosts. However, the challenge lies in identifying the genes and pathways whose expression level needs to be modulated to help achieve a desired overproduction of a specific metabolite or molecule of interest. One approach is through trial and error combinatorial experimentation where individual genes and pathways are modulated to understand their effect on the production of a target molecule. Although this approach could be aided by expert knowledge gained from metabolic engineering, it remains tedious and inefficient and might not arrive at a global optimum for the gene expression system. Mathematical modelling approaches for understanding metabolism at the whole cell level such as metabolic flux analysis (MFA) could provide steady state readouts of metabolic flux into different pathways, but large system of equations render the approach only solvable by computer. While MFA could help the metabolic engineer understand how modulation of expression level of a gene could impact on production of a target metabolite, the approach still relies on trial and error computer simulation to arrive at a possible approach for overexpressing particular sets of genes. Hence, a method is needed to predict the overexpression strategies (i.e., genes and pathways whose modulation is necessary) for overproducing a particular metabolite. Writing in Metabolic Engineering Communications, Wang and coworkers developed UP Finder, a MATLAB based COBRA extension that could identify overexpression strategies for targeted overproduction of metabolites. Using genome-scale metabolic model as input, specification of the target metabolite would lead to the software yielding a list of genes useful for gene expression modulation for overproducing a product. Further, the software ranks the list of genes in order of preference for overproduction of a particular molecule of interest. Concurrence of the set of genes identified for overexpression in lycopene and fatty acyl-ACP overproduction in Escherichia coli and Synechocystis sp. PCC6803 with those reported in the literature validated the utility of UP Finder. Collectively, software capable of identifying gene overexpression strategies in overproduction of specific metabolite fills an important gap in metabolic engineering research. Currently designed for steady state cellular analysis and metabolic optimization, future extensions of the software to analysing the dynamic state of cells would open up avenues for understanding the interconnection between gene expression dynamics and cellular physiology.

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