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Kinetic paramaters identification for large-scle metabolic model of escherichia coli / Mohammed Adam Kunna Azrag

By:

Mohammed Adam Kunna Azrag

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2015Description: xvi, 91 p. : ill. ; 30 cm. + 1 CD-ROMISBN:

THE0001068(Local)

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Dissertation note: Thesis (Master of Science (Compute)) -- Universiti Malaysia Pahang – 2015 Abstract: 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.

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Holdings ( 2 )
Title notes ( 4 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Thesis	UMPLIB PEKAN	FSKKP .A36 2015 r Thesis (Browse shelf(Opens below))	1	Not for loan		0000107864
Thesis	UMPLIB PEKAN	CD 9692 \| FSKKP .A36 2015 r Thesis (Browse shelf(Opens below))	1	Not for loan		0000107865

Faculty of Computer System and Software Engineering

Thesis (Master of Science (Compute)) -- Universiti Malaysia Pahang – 2015

Bibliography : p. 70-79

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.