In silico gene knockout prediction using a hybrid of Bat algorithm and minimization of metabolic adjustment

Mei Yen Man, Mohd Saberi Mohamad, Yee Wen Choon, Mohd Arfian Ismail

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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).

Original languageEnglish
JournalJournal of integrative bioinformatics
Volume18
Issue number3
DOIs
Publication statusPublished - Aug 4 2021

Keywords

  • Escherichia coli
  • Bat algorithm
  • bioinformatics
  • gene knockout
  • lactate
  • minimization of metabolic adjustment
  • succinate

ASJC Scopus subject areas

  • General Medicine

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