Optimising the production of succinate and lactate in Escherichia coli usingahybrid of artificial bee colony algorithm and minimisation of metabolic adjustment

Phooi Wah Tang, Yee Wen Choon, Mohd Saberi Mohamad, Safaai Deris, Suhaimi Napis

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Metabolic engineering is a research field that focuses on the design of models for metabolism, and uses computational procedures to suggest genetic manipulation. It aims to improve the yield of particular chemical or biochemical products. Several traditional metabolic engineering methods are commonly used to increase the production of a desired target, but the products are always far below their theoretical maximums. Using numeral optimisation algorithms to identify gene knockouts may stall at a local minimum in a multivariable function. This paper proposes a hybrid of the artificial bee colony (ABC) algorithm and the minimisation of metabolic adjustment (MOMA) to predict an optimal set of solutions in order to optimise the production rate of succinate and lactate. The dataset used in this work was from the iJO1366 Escherichia coli metabolic network. The experimental results include the production rate, growth rate and a list of knockout genes. From the comparative analysis, ABCMOMA produced better results compared to previous works, showing potential for solving genetic engineering problems.

Original languageEnglish
Pages (from-to)363-368
Number of pages6
JournalJournal of Bioscience and Bioengineering
Volume119
Issue number3
DOIs
Publication statusPublished - Mar 1 2015
Externally publishedYes

Keywords

  • Artificial bee colony
  • Escherichia coli
  • Gene knockout
  • Metabolic engineering
  • Minimisation of metabolic adjustment

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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