Affiliation: Artificial Intelligence & Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia.
Most food and beverages contain artificial flavor compounds. 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, several algorithms exist such as Genetic Algorithm, Evolutionary Algorithm, Opt Knock tool and other related techniques which are widely used to predict the yield of target compound by suggesting the gene knockouts. The use of these algorithms or tools to 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 consuming. As we know, the cost of chemical is expensive and not all flavorists are able to afford the cost. However, the main limitations of previous algorithms are that they 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 terms of yield may help in food flavorings.