A hybrid of ant colony optimization and minimization of metabolic adjustment to improve the production of succinic acid in Escherichia coli

Shiue Kee Chong, Mohd Saberi Mohamad, Abdul Hakim Mohamed Salleh, Yee Wen Choon, Chuii Khim Chong, Safaai Deris

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

This paper presents a study on gene knockout strategies to identify candidate genes to be knocked out for improving the production of succinic acid in Escherichia coli. Succinic acid is widely used as a precursor for many chemicals, for example production of antibiotics, therapeutic proteins and food. However, the chemical syntheses of succinic acid using the traditional methods usually result in the production that is far below their theoretical maximums. In silico gene knockout strategies are commonly implemented to delete the gene in E. coli to overcome this problem. In this paper, a hybrid of Ant Colony Optimization (ACO) and Minimization of Metabolic Adjustment (MoMA) is proposed to identify gene knockout strategies to improve the production of succinic acid in E. coli. As a result, the hybrid algorithm generated a list of knockout genes, succinic acid production rate and growth rate for E. coli after gene knockout. The results of the hybrid algorithm were compared with the previous methods, OptKnock and MOMAKnock. It was found that the hybrid algorithm performed better than OptKnock and MOMAKnock in terms of the production rate. The information from the results produced from the hybrid algorithm can be used in wet laboratory experiments to increase the production of succinic acid in E. coli.

Original languageEnglish
Pages (from-to)74-82
Number of pages9
JournalComputers in Biology and Medicine
Volume49
Issue number1
DOIs
Publication statusPublished - Jun 1 2014
Externally publishedYes

Keywords

  • Ant colony optimization
  • Escherichia coli
  • Gene knockout strategies
  • Minimization of metabolic adjustment
  • Succinic acid

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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