A hybrid of ant colony optimization and minimization of metabolic adjustment to improve the production of succinic acid in Escherichia coli.
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.
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: skchong4@live.utm.my.
Shiue Kee
SK
Chong
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: saberi@utm.my.
Mohd Saberi
MS
Mohamad
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: abdhakim.utm@gmail.com.
Abdul Hakim
AH
Mohamed Salleh
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: ywchoon2@live.utm.my.
Yee Wen
YW
Choon
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: ckchong2@live.utm.my.
Chuii Khim
CK
Chong
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: safaai@utm.my.
Safaai
S
Deris
D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism* / Q000502:physiology; D055786:Gene Knockout Techniques; D008954:Models, Biological; D019802:Succinic Acid / Q000032:analysis / Q000378:metabolism*
D019802:Succinic Acid
Ant colony optimization; Escherichia coli; Gene knockout strategies; Minimization of metabolic adjustment; Succinic acid