Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model

The development of a large-scale metabolic model of Escherichia coli (E. coli) is very crucial to identify the potential solution of industrially viable productions. However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathwa...

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Main Author:	Mohammed Adam, Kunna Azrag
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/39571/1/ir.Enhanced%20segment%20particle%20swarm%20optimization%20for%20large-scale%20kinetic%20parameter%20estimation.pdf
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id	my-ump-ir.39571
record_format	uketd_dc
spelling	my-ump-ir.395712023-12-11T02:11:56Z Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model 2021-07 Mohammed Adam, Kunna Azrag QA75 Electronic computers. Computer science The development of a large-scale metabolic model of Escherichia coli (E. coli) is very crucial to identify the potential solution of industrially viable productions. However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. Thus, this research aimed to estimate large-scale kinetic parameters of the main metabolic pathway of the E. coli model. In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. Initially, PSO algorithm was adapted to find the globally optimal result based on unorganized particle movement in the search space toward the optimal solution. This development then introduces the Se-PSO algorithm in which the particles are segmented to find a local optimal solution at the beginning and later sought by the PSO algorithm. Additionally, the study proposed an Enhance Se-PSO algorithm to improve the linear value of inertia weight 2021-07 Thesis http://umpir.ump.edu.my/id/eprint/39571/ http://umpir.ump.edu.my/id/eprint/39571/1/ir.Enhanced%20segment%20particle%20swarm%20optimization%20for%20large-scale%20kinetic%20parameter%20estimation.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computing Tuty Asmawaty, Abdul Kadir
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Tuty Asmawaty, Abdul Kadir
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mohammed Adam, Kunna Azrag Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
description	The development of a large-scale metabolic model of Escherichia coli (E. coli) is very crucial to identify the potential solution of industrially viable productions. However, the large-scale kinetic parameters estimation using optimization algorithms is still not applied to the main metabolic pathway of the E. coli model, and they’re a lack of accuracy result been reported for current parameters estimation using this approach. Thus, this research aimed to estimate large-scale kinetic parameters of the main metabolic pathway of the E. coli model. In this regard, a Local Sensitivity Analysis, Segment Particle Swarm Optimization (Se-PSO) algorithm, and the Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm was adapted and proposed to estimate the parameters. Initially, PSO algorithm was adapted to find the globally optimal result based on unorganized particle movement in the search space toward the optimal solution. This development then introduces the Se-PSO algorithm in which the particles are segmented to find a local optimal solution at the beginning and later sought by the PSO algorithm. Additionally, the study proposed an Enhance Se-PSO algorithm to improve the linear value of inertia weight
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohammed Adam, Kunna Azrag
author_facet	Mohammed Adam, Kunna Azrag
author_sort	Mohammed Adam, Kunna Azrag
title	Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
title_short	Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
title_full	Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
title_fullStr	Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
title_full_unstemmed	Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
title_sort	enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computing
publishDate	2021
url	http://umpir.ump.edu.my/id/eprint/39571/1/ir.Enhanced%20segment%20particle%20swarm%20optimization%20for%20large-scale%20kinetic%20parameter%20estimation.pdf
_version_	1794020291559555072

Enhanced segment particle swarm optimization for large-scale kinetic parameter estimation of escherichia coli network model

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