Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli 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 m...
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers Inc.
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33504/ http://umpir.ump.edu.my/id/eprint/33504/1/Segment%20particle%20swarm%20optimization%20adoption%20for%20large-scale%20kinetic%20parameter%20l.pdf |
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| author | Azrag, Mohammed Adam Kunna Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S. |
| author_facet | Azrag, Mohammed Adam Kunna Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S. |
| author_sort | Azrag, Mohammed Adam Kunna |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:10:22Z |
| format | Article |
| id | ump-33504 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:10:22Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-335042022-04-15T06:56:25Z http://umpir.ump.edu.my/id/eprint/33504/ Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model Azrag, Mohammed Adam Kunna Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S. QA75 Electronic computers. Computer science QA76 Computer software QH Natural history TA Engineering (General). Civil engineering (General) 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. Institute of Electrical and Electronics Engineers Inc. 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33504/1/Segment%20particle%20swarm%20optimization%20adoption%20for%20large-scale%20kinetic%20parameter%20l.pdf Azrag, Mohammed Adam Kunna and Tuty Asmawaty, Abdul Kadir and Jaber, Aqeel S. (2018) Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model. IEEE Access, 6 (8565855). pp. 78622-78639. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2018.2885118 https://doi.org/10.1109/ACCESS.2018.2885118 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software QH Natural history TA Engineering (General). Civil engineering (General) Azrag, Mohammed Adam Kunna Tuty Asmawaty, Abdul Kadir Jaber, Aqeel S. Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| title | Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| title_full | Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| title_fullStr | Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| title_full_unstemmed | Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| title_short | Segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| title_sort | segment particle swarm optimization adoption for large-scale kinetic parameter identification of escherichia coli metabolic network model |
| topic | QA75 Electronic computers. Computer science QA76 Computer software QH Natural history TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/33504/ http://umpir.ump.edu.my/id/eprint/33504/ http://umpir.ump.edu.my/id/eprint/33504/ http://umpir.ump.edu.my/id/eprint/33504/1/Segment%20particle%20swarm%20optimization%20adoption%20for%20large-scale%20kinetic%20parameter%20l.pdf |