Tan, Jun Bin and Choon, Yee Wen and Moorthy, Kohbalan and Hasyiya Karimah, Adli and Muhammad Akmal, Remli and Mohd Arfian, Ismail and Zuwairie, Ibrahim and Mohd Saberi, Mohamad (2023) A hybrid of ant colony optimization, genetic algorithm and flux balance analysis for optimization of succinic acid production in Escherichia coli. International Journal of Modeling, Simulation, and Scientific Computing, 14 (4). pp. 1-19. ISSN 1793-9623. (Published)
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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 iiJR904 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Gene knockout strategy; Flux balance analysis; Ant colony optimization; Genetic algorithm; health data science; Metabolic engineering; Bioinformatics; artificial intelligence |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QH Natural history > QH426 Genetics |
| Faculty/Division: | Faculty of Computing Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Dr. Kohbalan Moorthy |
| Date Deposited: | 10 Jan 2025 08:34 |
| Last Modified: | 10 Jan 2025 08:34 |
| URI: | http://umpir.ump.edu.my/id/eprint/43509 |
| Download Statistic: | View Download Statistics |
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