Kinetic paramaters identification for large-scle metabolic model of escherichia coli
One of the biggest challenging in metabolic engineering is to design an accurate model of large-scale of metabolic network in metabolic engineering field; which require an appropriate sensitivity analysis and optimization techniques. This research focusing on identifying the optimize values of large...
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| Format: | Thesis |
| Language: | English |
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2015
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| Online Access: | http://umpir.ump.edu.my/id/eprint/12718/1/Kinetic%20paramaters%20identification%20for%20large-scle%20metabolic%20model%20of%20escherichia%20coli.pdf |
| _version_ | 1825822854682247168 |
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| author | Mohammed Adam, Kunna Azrag |
| author_facet | Mohammed Adam, Kunna Azrag |
| author_sort | Mohammed Adam, Kunna Azrag |
| collection | UMP |
| description | One of the biggest challenging in metabolic engineering is to design an accurate model of large-scale of metabolic network in metabolic engineering field; which require an appropriate sensitivity analysis and optimization techniques. This research focusing on identifying the optimize values of large-scale kinetic parameters of E. coli model. The model under study consist of five metabolic pathways which are Glycolysis, Pentose Phosphate, TCA cycle, Gluconegenesis and Glycoxylate; which contain 194 kinetic parameters to be optimize. This model also includes PTS system in addition to Acetate formation, 23 metabolites, 28 enzymatic reactions and 10 co-factors. The experimental data were run in 0.1 and 0.2 dilution rates at continuous culture on steady-state condition. The One-At-A-Time Sensitivity Measure and Particle Swarm Optimization (PSO) techniques was applied to the model under study in order to identify the optimum values of the kinetics. The result stated from the One-At-A-Time Sensitivity Measure shows that there are 7 kinetics affecting highly in the model response under 0.1 dilution rate, while in 0.2 there are 8 kinetics affecting highly in the model response also. The result stated from PSO shows that, this technique can minimize the errors of our simulation result by % as compare to (Ishii et al., 2007) and % as compare to (Hoque et al., 2005). Based on the results found by the techniques, these tichniques can be applied to correct the model response through large-scale kinetic parameters. |
| first_indexed | 2024-03-06T12:02:44Z |
| format | Thesis |
| id | UMPir12718 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:02:44Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | UMPir127182023-03-15T03:18:22Z http://umpir.ump.edu.my/id/eprint/12718/ Kinetic paramaters identification for large-scle metabolic model of escherichia coli Mohammed Adam, Kunna Azrag QA75 Electronic computers. Computer science One of the biggest challenging in metabolic engineering is to design an accurate model of large-scale of metabolic network in metabolic engineering field; which require an appropriate sensitivity analysis and optimization techniques. This research focusing on identifying the optimize values of large-scale kinetic parameters of E. coli model. The model under study consist of five metabolic pathways which are Glycolysis, Pentose Phosphate, TCA cycle, Gluconegenesis and Glycoxylate; which contain 194 kinetic parameters to be optimize. This model also includes PTS system in addition to Acetate formation, 23 metabolites, 28 enzymatic reactions and 10 co-factors. The experimental data were run in 0.1 and 0.2 dilution rates at continuous culture on steady-state condition. The One-At-A-Time Sensitivity Measure and Particle Swarm Optimization (PSO) techniques was applied to the model under study in order to identify the optimum values of the kinetics. The result stated from the One-At-A-Time Sensitivity Measure shows that there are 7 kinetics affecting highly in the model response under 0.1 dilution rate, while in 0.2 there are 8 kinetics affecting highly in the model response also. The result stated from PSO shows that, this technique can minimize the errors of our simulation result by % as compare to (Ishii et al., 2007) and % as compare to (Hoque et al., 2005). Based on the results found by the techniques, these tichniques can be applied to correct the model response through large-scale kinetic parameters. 2015 Thesis NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/12718/1/Kinetic%20paramaters%20identification%20for%20large-scle%20metabolic%20model%20of%20escherichia%20coli.pdf Mohammed Adam, Kunna Azrag (2015) Kinetic paramaters identification for large-scle metabolic model of escherichia coli. Masters thesis, Universiti Malaysia Pahang (Contributors, Thesis advisor: Tuty Asmawaty, Abdul Kadir). |
| spellingShingle | QA75 Electronic computers. Computer science Mohammed Adam, Kunna Azrag Kinetic paramaters identification for large-scle metabolic model of escherichia coli |
| title | Kinetic paramaters identification for large-scle metabolic model of escherichia coli |
| title_full | Kinetic paramaters identification for large-scle metabolic model of escherichia coli |
| title_fullStr | Kinetic paramaters identification for large-scle metabolic model of escherichia coli |
| title_full_unstemmed | Kinetic paramaters identification for large-scle metabolic model of escherichia coli |
| title_short | Kinetic paramaters identification for large-scle metabolic model of escherichia coli |
| title_sort | kinetic paramaters identification for large scle metabolic model of escherichia coli |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/12718/1/Kinetic%20paramaters%20identification%20for%20large-scle%20metabolic%20model%20of%20escherichia%20coli.pdf |
| work_keys_str_mv | AT mohammedadamkunnaazrag kineticparamatersidentificationforlargesclemetabolicmodelofescherichiacoli |