TY - JOUR
T1 - A hybrid of ant colony optimization, genetic algorithm and flux balance analysis for optimization of succinic acid production in Escherichia coli
AU - Tan, Jun Bin
AU - Choon, Yee Wen
AU - Moorthy, Kohbalan
AU - Adli, Hasyiya Karimah
AU - Remli, Muhammad Akmal
AU - Ismail, Mohd Arfian
AU - Ibrahim, Zuwairie
AU - Mohamad, Mohd Saberi
N1 - Publisher Copyright:
© 2023 World Scientific Publishing Company.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - 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.
AB - 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.
KW - ant colony optimization
KW - artificial intelligence
KW - bioinformatics
KW - flux balance analysis
KW - Gene knockout strategy
KW - genetic algorithm
KW - health data science
KW - metabolic engineering
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U2 - 10.1142/S179396232350040X
DO - 10.1142/S179396232350040X
M3 - Article
AN - SCOPUS:85150803158
SN - 1793-9623
VL - 14
JO - International Journal of Modeling, Simulation, and Scientific Computing
JF - International Journal of Modeling, Simulation, and Scientific Computing
IS - 4
M1 - 2350040
ER -