Artificial bee colony and dynamic flux balance analysis for microbial production

Abstract

The ethanol and lactate productions of Escherichia coli (E. coli) can be optimized using metabolic engineering, which implements gene knockout techniques. The gene knockout technique is utilized inside optimization algorithms to alter the metabolism of E. coli. Nowadays, several hybrid optimization algorithms have been introduced to optimize the ethanol and lactate productions. However, the existing algorithms were ineffective to produce the highest production due to the huge and complex metabolic networks. Therefore, the main goal of this study is to propose a hybrid of Artificial Bee Colony and Dynamic Flux Balance Analysis (ABCDFBA) to overcome the limitation of existing algorithms. Artificial Bee Colony algorithm has advantages such as high flexibility and fast convergence. Dynamic Flux Balance Analysis algorithm can predict metabolite concentration and the dynamic of diauxic growth. Experimental results show that the ABCDFBA has performed better results in terms of Biomass-Product Coupled Yield (BPCY) of ethanol, which was 1.9505 milli-gram (gram.glucose.hour)-1 and lactate was 6.6037 milli-gram (gram.glucose.hour)-1 in E. coli performance compared to existing algorithms.