A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli

Mee K. Lee; Mohd Saberi Mohamad; Yee Wen Choon; Kauthar Mohd Daud; Nurul Athirah Nasarudin; Mohd Arfian Ismail; Zuwairie Ibrahim; Suhaimi Napis; Richard O. Sinnott

doi:10.1007/978-3-030-23873-5_5

A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli

Mee K. Lee, Mohd Saberi Mohamad, Yee Wen Choon, Kauthar Mohd Daud, Nurul Athirah Nasarudin, Mohd Arfian Ismail, Zuwairie Ibrahim, Suhaimi Napis, Richard O. Sinnott

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Practical Applications of Computational Biology and Bioinformatics, 13th International Conference, PACBB 2019
Editors	Florentino Fdez-Riverola, Miguel Rocha, Mohd Saberi Mohamad, Nazar Zaki, José A. Castellanos-Garzón
Publisher	Springer Verlag
Pages	36-44
Number of pages	9
ISBN (Print)	9783030238728
DOIs	https://doi.org/10.1007/978-3-030-23873-5_5
Publication status	Published - 2020
Externally published	Yes
Event	13th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2019 - Ávila, Spain Duration: Jun 26 2019 → Jun 28 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1005
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	13th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2019
Country/Territory	Spain
City	Ávila
Period	6/26/19 → 6/28/19

Keywords

Artificial intelligence
Bioinformatics
Metabolic engineering
Minimization of metabolic adjustment
Particle swarm optimization

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-23873-5_5

Cite this

Lee, M. K., Mohamad, M. S., Choon, Y. W., Mohd Daud, K., Nasarudin, N. A., Ismail, M. A., Ibrahim, Z., Napis, S., & Sinnott, R. O. (2020). A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli. In F. Fdez-Riverola, M. Rocha, M. S. Mohamad, N. Zaki, & J. A. Castellanos-Garzón (Eds.), Practical Applications of Computational Biology and Bioinformatics, 13th International Conference, PACBB 2019 (pp. 36-44). (Advances in Intelligent Systems and Computing; Vol. 1005). Springer Verlag. https://doi.org/10.1007/978-3-030-23873-5_5

A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli. / Lee, Mee K.; Mohamad, Mohd Saberi; Choon, Yee Wen et al.
Practical Applications of Computational Biology and Bioinformatics, 13th International Conference, PACBB 2019. ed. / Florentino Fdez-Riverola; Miguel Rocha; Mohd Saberi Mohamad; Nazar Zaki; José A. Castellanos-Garzón. Springer Verlag, 2020. p. 36-44 (Advances in Intelligent Systems and Computing; Vol. 1005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, MK, Mohamad, MS, Choon, YW, Mohd Daud, K, Nasarudin, NA, Ismail, MA, Ibrahim, Z, Napis, S & Sinnott, RO 2020, A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli. in F Fdez-Riverola, M Rocha, MS Mohamad, N Zaki & JA Castellanos-Garzón (eds), Practical Applications of Computational Biology and Bioinformatics, 13th International Conference, PACBB 2019. Advances in Intelligent Systems and Computing, vol. 1005, Springer Verlag, pp. 36-44, 13th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2019, Ávila, Spain, 6/26/19. https://doi.org/10.1007/978-3-030-23873-5_5

Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA et al. A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli. In Fdez-Riverola F, Rocha M, Mohamad MS, Zaki N, Castellanos-Garzón JA, editors, Practical Applications of Computational Biology and Bioinformatics, 13th International Conference, PACBB 2019. Springer Verlag. 2020. p. 36-44. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23873-5_5

Lee, Mee K. ; Mohamad, Mohd Saberi ; Choon, Yee Wen et al. / A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli. Practical Applications of Computational Biology and Bioinformatics, 13th International Conference, PACBB 2019. editor / Florentino Fdez-Riverola ; Miguel Rocha ; Mohd Saberi Mohamad ; Nazar Zaki ; José A. Castellanos-Garzón. Springer Verlag, 2020. pp. 36-44 (Advances in Intelligent Systems and Computing).

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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.",

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AU - Mohd Daud, Kauthar

AU - Nasarudin, Nurul Athirah

AU - Ismail, Mohd Arfian

AU - Ibrahim, Zuwairie

AU - Napis, Suhaimi

AU - Sinnott, Richard O.

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N2 - 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.

AB - 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.

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A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli

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