Combination of Genome-Scale Models and Bioreactor Dynamics to Optimize the Production of Commodity Chemicals.
Torres-Acosta, Mario A
Oliver, Stephen G
Front Mol Biosci
Frontiers Media SA
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Lázaro, J., Jansen, G., Yang, Y., Torres-Acosta, M. A., Lye, G., Oliver, S. G., & Júlvez, J. (2022). Combination of Genome-Scale Models and Bioreactor Dynamics to Optimize the Production of Commodity Chemicals.. Front Mol Biosci https://doi.org/10.3389/fmolb.2022.855735
The current production of a number of commodity chemicals relies on the exploitation of fossil fuels and hence has an irreversible impact on the environment. Biotechnological processes offer an attractive alternative by enabling the manufacturing of chemicals by genetically modified microorganisms. However, this alternative approach poses some important technical challenges that must be tackled to make it competitive. On the one hand, the design of biotechnological processes is based on trial-and-error approaches, which are not only costly in terms of time and money, but also result in suboptimal designs. On the other hand, the manufacturing of chemicals by biological processes is almost exclusively carried out by batch or fed-batch cultures. Given that batch cultures are expensive and not easy to scale, technical means must be developed to make continuous cultures feasible and efficient. In order to address these challenges, we have developed a mathematical model able to integrate in a single model both the genome-scale metabolic model for the organism synthesizing the chemical of interest and the dynamics of the bioreactor in which the organism is cultured. Such a model is based on the use of Flexible Nets, a modeling formalism for dynamical systems. The integration of a microscopic (organism) and a macroscopic (bioreactor) model in a single net provides an overall view of the whole system and opens the door to global optimizations. As a case study, the production of citramalate with respect to the substrate consumed by E. coli is modeled, simulated and optimized in order to find the maximum productivity in a steady-state continuous culture. The predicted computational results were consistent with the wet lab experiments.
Biological Sciences Research Council (UK) grant no. BB/N02348X/1, as part of the IBiotech Program, and by the Industrial Biotechnology Catalyst (Innovate UK, BBSRC, EPSRC) to support the translation, development and commercialisation of innovative Industrial Biotechnology processes.
Biotechnology and Biological Sciences Research Council (BB/N02348X/1)
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External DOI: https://doi.org/10.3389/fmolb.2022.855735
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334789
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/