A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli

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 alterati...

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Main Authors: Lee, Mee K., Mohd Saberi, Mohamad, Choon, Yee Wen, Kauthar, Mohd Daud, Nurul Athirah, Nasarudin, Mohd Arfian, Ismail, Zuwairie, Ibrahim, Suhaimi, Napis, Sinnott, Richard O.
Format: Conference or Workshop Item
Language:English
Published: Springer Verlag 2020
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/25633/1/A%20hybrid%20of%20particle%20swarm%20optimization%20and%20minimization%20.pdf
http://umpir.ump.edu.my/id/eprint/25633/
https://doi.org/10.1007/978-3-030-23873-5_5
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spelling my.ump.umpir.256332019-12-13T06:49:31Z http://umpir.ump.edu.my/id/eprint/25633/ A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli Lee, Mee K. Mohd Saberi, Mohamad Choon, Yee Wen Kauthar, Mohd Daud Nurul Athirah, Nasarudin Mohd Arfian, Ismail Zuwairie, Ibrahim Suhaimi, Napis Sinnott, Richard O. Q Science (General) QD Chemistry QH Natural history TP Chemical technology 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. Springer Verlag 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25633/1/A%20hybrid%20of%20particle%20swarm%20optimization%20and%20minimization%20.pdf 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 https://doi.org/10.1007/978-3-030-23873-5_5
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QD Chemistry
QH Natural history
TP Chemical technology
spellingShingle Q Science (General)
QD Chemistry
QH Natural history
TP Chemical technology
Lee, Mee K.
Mohd Saberi, Mohamad
Choon, Yee Wen
Kauthar, Mohd Daud
Nurul Athirah, Nasarudin
Mohd Arfian, Ismail
Zuwairie, Ibrahim
Suhaimi, Napis
Sinnott, Richard O.
A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
description 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.
format Conference or Workshop Item
author Lee, Mee K.
Mohd Saberi, Mohamad
Choon, Yee Wen
Kauthar, Mohd Daud
Nurul Athirah, Nasarudin
Mohd Arfian, Ismail
Zuwairie, Ibrahim
Suhaimi, Napis
Sinnott, Richard O.
author_facet Lee, Mee K.
Mohd Saberi, Mohamad
Choon, Yee Wen
Kauthar, Mohd Daud
Nurul Athirah, Nasarudin
Mohd Arfian, Ismail
Zuwairie, Ibrahim
Suhaimi, Napis
Sinnott, Richard O.
author_sort Lee, Mee K.
title A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
title_short A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
title_full A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
title_fullStr A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
title_full_unstemmed A hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
title_sort hybrid of particle swarm optimization and minimization of metabolic adjustment for ethanol production of escherichia coli
publisher Springer Verlag
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/25633/1/A%20hybrid%20of%20particle%20swarm%20optimization%20and%20minimization%20.pdf
http://umpir.ump.edu.my/id/eprint/25633/
https://doi.org/10.1007/978-3-030-23873-5_5
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score 13.154949