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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Main Author:	Mohammed Adam, Kunna Azrag
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/12718/1/Kinetic%20paramaters%20identification%20for%20large-scle%20metabolic%20model%20of%20escherichia%20coli.pdf
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spelling	my-ump-ir.127182023-03-15T03:18:22Z Kinetic paramaters identification for large-scle metabolic model of escherichia coli 2015 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 http://umpir.ump.edu.my/id/eprint/12718/ http://umpir.ump.edu.my/id/eprint/12718/1/Kinetic%20paramaters%20identification%20for%20large-scle%20metabolic%20model%20of%20escherichia%20coli.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Computer System and Software Engineering 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 Kinetic paramaters identification for large-scle metabolic model of escherichia coli
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.
format	Thesis
qualification_level	Master's degree
author	Mohammed Adam, Kunna Azrag
author_facet	Mohammed Adam, Kunna Azrag
author_sort	Mohammed Adam, Kunna Azrag
title	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_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_sort	kinetic paramaters identification for large-scle metabolic model of escherichia coli
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computer System and Software Engineering
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/12718/1/Kinetic%20paramaters%20identification%20for%20large-scle%20metabolic%20model%20of%20escherichia%20coli.pdf
_version_	1783731969935278080

Kinetic paramaters identification for large-scle metabolic model of escherichia coli

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