DOI : https://doi.org/10.18517/ijaseit.7.4-2.3398

A Hybrid of Integer Differential Bees and Flux Balance Analysis for Improving Succinate and Lactate Production

Mohd Fahmi Arieef (1), Yee Wen Choon (2), Mohd Saberi Mohamad (3), Safaai Deris (4), Suhaimi Napis (5), Shahreen Kasim (6)
(1) Artificial Intelligence and Bioinformatics Research Group, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
(2) Artificial Intelligence and Bioinformatics Research Group, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia
(3) Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Karung Berkunci 01, 16300, Bachok, Kelantan, Malaysia.
(4) Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Karung Berkunci 01, 16300, Bachok, Kelantan, Malaysia
(5) Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
(6) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia.
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How to cite (IJASEIT) :
Arieef, Mohd Fahmi, et al. “A Hybrid of Integer Differential Bees and Flux Balance Analysis for Improving Succinate and Lactate Production”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4-2, Sept. 2017, pp. 1615-20, doi:10.18517/ijaseit.7.4-2.3398.
Citation Format :
The production of succinate and lactate from E.coli become a demand in pharmaceutical industries. To increase the yield of the production, gene knockout technique was implemented in various hybrid optimization algorithms. In recent years, several hybrid optimization have been introduced to optimize succinate and lactate production. However, the previous works were ineffective to produce the highest production due to the size and complexity of metabolic networks and the dynamic interaction between the components. Therefore, the main purpose of this study is to overcome the limitation of the existing algorithms which hybridizing Integer Differential Bees and Flux Balance Analysis (IDBFBA). The experimental results show a better performance in terms of growth rate and production yield of desired phenotypes compared to the method used in previous works.
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