Abstract
The elementary concept of metabolic engineering is to manipulate those potential genes to augment the yields of metabolite production by restructuring and deregulating the metabolic networks. However, the yields of biochemical products are far below their theoretical maximums, although many conventional methods that have been introduced. These conventional methods had encountered problems in getting stuck at a local minimum. This paper proposes a hybrid of the Bat Algorithm (BAT) and the Minimization of Metabolic Adjustment (MOMA) to predict an ideal set of solutions in order to improve the production of succinate and lactate. The dataset utilized in this paper was the Escherichia coli core model, which is the subset of the iAF1260 E. coli metabolic network. The experimental results include the production rate, growth rate, and a list of knockout genes. From the comparative analysis, BATMOMA has better performance in terms of production rate compared to previous works, proving that it has the potential for resolving genetic engineering problems.
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We would like to thank the Skim Geran Penyelidikan Fundamental (FRGS-MRSA) (no grant: R/FRGS/A0800/01655A/003/2020/00720) for their support in order to make this research a success.
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Man, M.Y., Mohamad, M.S., Choon, Y.W., Ismail, M.A. (2021). A Hybrid of Bat Algorithm and Minimization of Metabolic Adjustment for Succinate and Lactate Production. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_17
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