Abstract
Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.
Highlights
The microbial production of various useful compounds has been actively studied
An Flux balance analysis (FBA) solution that maximizes or minimizes the objective function is searched, using a stoichiometric matrix composed of stoichiometric coefficients of reactions that constitute the metabolic model [6]
In the FBA-based method, a simulation can be performed relatively using a large-scale genome-scale metabolic model (GSM), and the analyses have provided various metabolic engineering strategies for the improved production of useful compounds
Summary
The microbial production of various useful compounds has been actively studied. With the recent development of synthetic biotechnology, the production of useful compounds that microorganisms do not naturally produce can be made possible by introducing biosynthetic pathways (synthetic metabolic pathways), designed by combining heterologous and modified (mutant) enzymes. Since dFBA is applied to systems involving cell growth, such as batch and fed-batch cultures, the theoretical maximum production concentrations and yields can be estimated under conditions closer to the production process than FBA By comparing these maximum production concentrations and yields with the experimental values, it was possible to estimate the differences between the simulated maximum values and the experimental results, and to evaluate production performance in the target compound production of the strain, which could provide useful information as to whether there is room for improvement in the production of the target compound. We applied dFBA to estimate the difference between the simulated maximum production concentration of the target compound and the experimental value under the same constraints, such as substrate consumption and cell growth, and to evaluate production performance in the experimental strain.
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