Lee, Mee K. and Mohd Saberi, Mohamad and Choon, Yee Wen and Kauthar, Mohd Daud and Nurul Athirah, Nasarudin and Mohd Arfian, Ismail and Zuwairie, Ibrahim and Suhaimi, Napis and Sinnott, Richard O. (2020) A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli. In: 13th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2019 , 26 - 28 June 2019 , Ávila, Spain. pp. 36-44., 1005. ISSN 2194-5357 ISBN 9783030238728
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Abstract
Ethanol is a chemical-colourless compound that widely used in pharmaceutical, medicines, food products, and industrial applications. As the demand for ethanol is rising recently, attention has been given on metabolic engineering of Escherichia coli (E.coli) to enhance its production through alteration of its genetic content. This research mainly aimed to optimize ethanol production in E.coli using a gene knockout strategy. Several gene knockout strategies like OptKnock and OptGene have been proposed previously. However, most of them suffer from premature convergence. Hence, a hybrid of Particle Swarm Optimization (PSO) and Minimization of Metabolic Adjustment (MOMA) algorithm is proposed to identify the list of gene knockouts in maximizing the ethanol production and growth rate of E.coli. Experiment results show that the hybrid method is comparable with two state-of-the-art methods in term of growth rate and production.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Particle swarm optimization; Minimization of metabolic adjustment; Metabolic engineering; Bioinformatics; Artificial intelligence |
| Subjects: | Q Science > Q Science (General) Q Science > QD Chemistry Q Science > QH Natural history T Technology > TP Chemical technology |
| Faculty/Division: | Faculty of Computer System And Software Engineering Faculty of Mechanical & Manufacturing Engineering |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 13 Dec 2019 06:49 |
| Last Modified: | 13 Dec 2019 06:49 |
| URI: | http://umpir.ump.edu.my/id/eprint/25633 |
| Download Statistic: | View Download Statistics |
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