Man, Mei Yen and Mohd Saberi, Mohamad and Choon, Yee Wen and Mohd Arfian, Ismail (2021) In silico gene knockout prediction using a hybrid of Bat algorithm and minimization of metabolic adjustment. Journal of integrative bioinformatics, 18 (3). pp. 1-14. ISSN 1613-4516. (Published)
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Abstract
Microorganisms commonly produce many high-demand industrial products like fuels, food, vitamins, and other chemicals. Microbial strains are the strains of microorganisms, which can be optimized to improve their technological properties through metabolic engineering. Metabolic engineering is the process of overcoming cellular regulation in order to achieve a desired product or to generate a new product that the host cells do not usually need to produce. The prediction of genetic manipulations such as gene knockout is part of metabolic engineering. Gene knockout can be used to optimize the microbial strains, such as to maximize the production rate of chemicals of interest. Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in Escherichia coli (E. coli).
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Escherichia coli ; Bat algorithm; Bioinformatics; Gene knockout; Lactate; Minimization of metabolic adjustment; Succinate |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 16 Jun 2022 04:10 |
| Last Modified: | 16 Jun 2022 04:10 |
| URI: | http://umpir.ump.edu.my/id/eprint/33133 |
| Download Statistic: | View Download Statistics |
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