Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model

Kinetic parameter identification in the dynamic metabolic model of Escherichia coli (E. coli ) has become important and is needed to obtain appropriate metabolite and enzyme data that are valid under in vivo conditions. The dynamic metabolic model under study represents five metabolic pathways with...

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Main Authors:	Azrag, M. A. K., Tuty Asmawaty, Abdul Kadir, Jaber, Aqeel S.
Format:	Article
Language:	English
Published:	IEEE 2018
Subjects:	QA Mathematics QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/23609/ http://umpir.ump.edu.my/id/eprint/23609/ http://umpir.ump.edu.my/id/eprint/23609/ http://umpir.ump.edu.my/id/eprint/23609/1/Segment%20Particle%20Swarm%20Optimization%20Adoption.pdf

id	ump-23609
recordtype	eprints
spelling	ump-236092019-01-03T07:33:44Z http://umpir.ump.edu.my/id/eprint/23609/ Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S. QA Mathematics QA75 Electronic computers. Computer science Kinetic parameter identification in the dynamic metabolic model of Escherichia coli (E. coli ) has become important and is needed to obtain appropriate metabolite and enzyme data that are valid under in vivo conditions. The dynamic metabolic model under study represents five metabolic pathways with more than 170 kinetic parameters at steady state with a 0.1 dilution rate. In this paper, identification is declared in two steps. The first step is to identify which kinetic parameters have a higher impact on the model response using local sensitivity analysis results upon increasing each kinetic parameter up to 2.0 by steps of 0.5, while the second step uses highly sensitive kinetic results to be identified and minimized the model simulation metabolite errors using real experimental data by adopting. However, this paper focuses on adopting segment particle swarm optimization (PSO) and PSO algorithms for large-scale kinetic parameters identification. Among the 170 kinetic parameters investigated, seven kinetic parameters were found to be the most effective kinetic parameters in the model response after finalizing the sensitivity. The seven sensitive kinetic parameters were used in both the algorithms to minimize the model response errors. The validation results proved the effectiveness of both the proposed methods, which identified the kinetics and minimized the model response errors perfectly. IEEE 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23609/1/Segment%20Particle%20Swarm%20Optimization%20Adoption.pdf Azrag, M. A. K. and Tuty Asmawaty, Abdul Kadir and Jaber, Aqeel S. (2018) Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model. IEEE Access, 6. pp. 78622-78639. ISSN 2169-3536 https://doi.org/10.1109/ACCESS.2018.2885118 https://doi.org/10.1109/ACCESS.2018.2885118
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics QA75 Electronic computers. Computer science
spellingShingle	QA Mathematics QA75 Electronic computers. Computer science Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S. Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model
description	Kinetic parameter identification in the dynamic metabolic model of Escherichia coli (E. coli ) has become important and is needed to obtain appropriate metabolite and enzyme data that are valid under in vivo conditions. The dynamic metabolic model under study represents five metabolic pathways with more than 170 kinetic parameters at steady state with a 0.1 dilution rate. In this paper, identification is declared in two steps. The first step is to identify which kinetic parameters have a higher impact on the model response using local sensitivity analysis results upon increasing each kinetic parameter up to 2.0 by steps of 0.5, while the second step uses highly sensitive kinetic results to be identified and minimized the model simulation metabolite errors using real experimental data by adopting. However, this paper focuses on adopting segment particle swarm optimization (PSO) and PSO algorithms for large-scale kinetic parameters identification. Among the 170 kinetic parameters investigated, seven kinetic parameters were found to be the most effective kinetic parameters in the model response after finalizing the sensitivity. The seven sensitive kinetic parameters were used in both the algorithms to minimize the model response errors. The validation results proved the effectiveness of both the proposed methods, which identified the kinetics and minimized the model response errors perfectly.
format	Article
author	Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S.
author_facet	Azrag, M. A. K. Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S.
author_sort	Azrag, M. A. K.
title	Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model
title_short	Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model
title_full	Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model
title_fullStr	Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model
title_full_unstemmed	Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model
title_sort	segment particle swarm optimization adoption for large-scale kinetic parameter identification of metabolic network model
publisher	IEEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23609/ http://umpir.ump.edu.my/id/eprint/23609/ http://umpir.ump.edu.my/id/eprint/23609/ http://umpir.ump.edu.my/id/eprint/23609/1/Segment%20Particle%20Swarm%20Optimization%20Adoption.pdf
first_indexed	2023-09-18T22:35:26Z
last_indexed	2023-09-18T22:35:26Z
_version_	1777416563555565568

Segment Particle Swarm Optimization Adoption for Large-Scale Kinetic Parameter Identification of Metabolic Network Model

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