A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models. (1st November 2018) - Arif, Muhammad Azharuddin; Mohamad, Mohd Saberi; Abd Latif, Muhammad Shafie; Deris, Safaai; Remli, Muhammad Akmal; Mohd Daud, Kauthar; Ibrahim, Zuwairie; Omatu, Sigeru; Corchado, Juan Manuel
Abstract: Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time. Highlights: A hybrid method to optimize metabolites production in genome-scale models is proposed. The method searched for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. The results are better than other methods in terms of metabolites production and computation time.