A hybrid of bees algorithm and flux balance analysis (BAFBA) for the optimisation of microbial strains. (1st January 2014)

• Record Type: Journal Article
• Authors: Choon, Yee Wen; Mohamad, Mohd. Saberi; Deris, Safaai; Illias, Rosli Md.
• Is Part Of: International journal of data mining and bioinformatics. Volume 10:Number 2(2014)
• Page Start: 225
• Page End: 238
• Publication Date: 2014-01-01
• Subjects: metabolic engineering -- bees algorithm -- flux balance analysis -- gene knockout -- microbial strains -- optimisation -- data mining -- bioinformatics; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

The development of microbial production system has become popular in recent years as microbial hosts offer a number of unique advantages for both native and heterologous small-molecules. However, the main drawback is low yield or productivity of the desired products. Optimisation algorithms are implemented in previous works to identify the effects of gene knockout. Nevertheless, the previous works faced performance issue. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to improve the performance in predicting optimal sets of gene deletion for maximising the growth rate and production yield of certain metabolite. This paper involves two datasets which are E. coli and S. cerevisiae . The list of knockout genes, growth rate and production yield after the deletion are the results from the experiments. BAFBA presents better results compared to the other methods and the identified list may be useful in solving genetic engineering problems.