A hybrid of ant colony optimization, genetic algorithm and flux balance analysis for optimization of succinic acid production in Escherichia coli

Jun Bin Tan, Yee Wen Choon, Kohbalan Moorthy, Hasyiya Karimah Adli, Muhammad Akmal Remli, Mohd Arfian Ismail, Zuwairie Ibrahim, Mohd Saberi Mohamad

Research output: Contribution to journalArticlepeer-review

Abstract

Succinic acid, also known as dicarboxylic acid, is one of the biochemical products chemically produced from Escherichia coli (E. coli) metabolism. However, by using conventional methods succinic acid cannot be produced sufficiently and it is costly. Hence, there is a lot of ongoing research on E. coli by using in silico methods. Researchers build computational models of E. coli to analyze and modify their metabolic network. This paper proposes a hybrid of ant colony optimization-genetic algorithm-flux balance analysis (ACOGAFBA) in enhancing the succinic acid production of E. coli by identifying genes to be knocked out. Ant colony optimization (ACO) is a swarm intelligent optimization that is inspired based on the natural foraging behavior of ant colony. Local search technique like genetic algorithm (GA) is applied to solve optimization and search problem by approximation. Flux balance analysis (FBA) is used for fitness calculation after gene knockout. FBA identifies a point (fitness) in flux space by using quadratic programming, which is closest to the wild type point. ACOGAFBA produced three sets of gene knockout lists. The dataset iJR904 is used in this paper. The results show that ACOGAFBA can identify the set of knockout genes to improve succinic acid production in E. coli.

Original languageEnglish
Article number2350040
JournalInternational Journal of Modeling, Simulation, and Scientific Computing
Volume14
Issue number4
DOIs
Publication statusPublished - Aug 1 2023

Keywords

  • ant colony optimization
  • artificial intelligence
  • bioinformatics
  • flux balance analysis
  • Gene knockout strategy
  • genetic algorithm
  • health data science
  • metabolic engineering

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications

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