Most food and beverage is containing artificial flavor compound. Creation of artificial flavors is not an easy step and it is hardly ever completely effective. In this paper, we introduce an in silico method in optimization of microbial strains of flavor compound synthesis. Previously, there are several algorithms such as Genetic Algorithm, Evolutionary Algorithm, Opt Knock tool and other related techniques are widely used to predict the yield of target compound by suggesting the gene knockouts. The used of these algorithms or tools is able to predict the yield of production instead of using trial and error method for gene deletions. Nowadays, without using in silico method, the direct experiment methods are not cost effective and time consumed. As we know, the cost of chemical is expensive and not all flavorist able to afford the cost. However, the main limitations of previous algorithms are it failed to optimize the prediction of the yield and suggesting unrealistic flux distribution. Therefore, this paper proposed a hybrid of continuous Bees algorithm and Flux Balance Analysis. The target compound in this research is vanillin and glutamate compound. The aim of study is to identify optimum gene knockouts. The results in this paper are the prediction of the yield and the growth rate values of the model. The predictive results showed that the improvement in term of yield which may help in food flavorings.
Artificial Intelligence and Bioinformatics Research Group Faculty of Computing, University Technology Malaysia Skoda, 81310 Johor Malaysia.