Choon Yee Wen, Mohamad Mohd Saberi, Deris Safaai, Chong Chuii Khim, Omatu Sigeru, Corchado Juan Manuel
Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan.
Biomed Res Int. 2015;2015:124537. doi: 10.1155/2015/124537. Epub 2015 Mar 22.
Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.
近年来，通过微生物菌株优化来过量生产所需表型一直是一个热门话题。基因敲除是一种基因工程技术，可改变微生物细胞的代谢以获得所需表型。人们已开发出优化算法来确定基因敲除的效果。然而，代谢网络的复杂性使得确定基因修饰对所需表型的影响这一过程具有挑战性。此外，细胞代谢中的大量反应常常导致在获得最佳基因敲除时出现组合问题。随着问题规模的增加，计算时间呈指数增长。这项工作报告了蜜蜂山通量平衡分析（BHFBA）的扩展，以确定最佳基因敲除，在维持生长速率的同时最大化所需表型的产量。该方法通过将OptKnock集成到BHFBA中自动验证结果来发挥作用。结果表明，BHFBA的扩展适用于预测基因敲除，可靠且适用。通过以大肠杆菌、枯草芽孢杆菌和热纤梭菌为模式生物进行的多项实验，BHFBA的扩展在计算时间、稳定性、生长速率和所需表型的产量方面表现出更好的性能。
