Abstract
Genome-scale metabolic models are mathematical formulations widely used to describe the relationship between cells’ genotype and phenotype. Over the years, several attempts have been made to expand these formulations with macromolecular expression. Recently, GECKO models proposed the inclusion of enzyme mass constraints to improve phenotype predictions of a yeast genome-scale metabolic model. Taking a step forward, ETFL formulation includes the gene expression machinery, enabling models to compute the entire metabolic and gene expression proteome in a growing cell. These formulations may lead to more biologically accurate predictions and improve the design of new strains. The present work explores the utilization of such models for the optimization of succinate production in Escherichia coli, taken here as a case study to show the potential of using different modeling approaches in strain design applications. All the optimizations were conducted using MEWpy, a recently proposed Metabolic Engineering Framework.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement number 814408).
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Pereira, V., Rocha, M. (2022). Combinatorial Optimization of Succinate Production in Escherichia coli. In: Rocha, M., Fdez-Riverola, F., Mohamad, M.S., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021). PACBB 2021. Lecture Notes in Networks and Systems, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-86258-9_16
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