Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.
Comput Biol Med. 2014 Jun;49:74-82. doi: 10.1016/j.compbiomed.2014.03.011. Epub 2014 Apr 5.
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.
本文提出了一种基因敲除策略的研究，旨在确定候选基因，以提高大肠杆菌中琥珀酸的产量。琥珀酸广泛用作许多化学物质的前体，例如抗生素、治疗性蛋白质和食品的生产。然而，使用传统方法进行化学合成琥珀酸通常导致产量远低于其理论最大值。因此，通常采用基于计算机的基因敲除策略来删除大肠杆菌中的基因以克服此问题。本文提出了一种蚁群优化（ACO）和代谢调整最小化（MoMA）的混合方法，以确定基因敲除策略，从而提高大肠杆菌中琥珀酸的产量。结果，该混合算法生成了大肠杆菌基因敲除后琥珀酸产量和生长速率的敲除基因列表。将混合算法的结果与先前的 OptKnock 和 MOMAKnock 方法进行比较，发现混合算法在产率方面优于 OptKnock 和 MOMAKnock。混合算法产生的结果信息可用于湿实验室实验，以增加大肠杆菌中琥珀酸的产量。
