File Entry: Gene Knockout Strategies Identification by Using a Hybrid of Bees Algorithm and Flux Balance Analysis for Optimizing Microbial Strains

 By optimizing microbial strains it is possible to improve product yield or improve growth characteristics. Microbial strains can be optimized through genetic engineering. It is proven that through genetic engineering it is able to obtain the desirable phenotypes. However, it is difficult to predict the effects of genetic modifications on the resulting phenotype due to the complexity of the networks. Optimization algorithms are implemented in previous works in order to identify the effects of gene knockout on the results. Sadly, the previous works face the problem of falling into local minima. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. Lists of knockout genes and the growth rate after the deletion for improving the production of succinic acid, glycerol and vanillin as targets are the results from the experiments. Genome-scale model of the yeast Saccharomyces cerevisiae is the model organism in this paper. By comparing with the previous methods, BAFBA shows better results. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems.