Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment

Microbial strains can be optimized using metabolic engineering which implements gene knockout techniques. These techniques manipulate potential genes to increase the yield of metabolites through restructuring metabolic networks. Nowadays, several hybrid optimization algorithms have been proposed to...

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Main Author:	Mazlan, Noor Ameera
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/86093/1/NoorAmeeraMazlanMFC2017.pdf
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spelling	my-utm-ep.860932020-08-30T08:56:05Z Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment 2017 Mazlan, Noor Ameera QA75 Electronic computers. Computer science Microbial strains can be optimized using metabolic engineering which implements gene knockout techniques. These techniques manipulate potential genes to increase the yield of metabolites through restructuring metabolic networks. Nowadays, several hybrid optimization algorithms have been proposed to optimize the microbial strains. However, the existing algorithms were unable to obtain optimal strains because the nonessential genes are hardly to be diagnosed and need to be removed due to high complexity of metabolic network. Therefore, the main goal of this study is to overcome the limitation of the existing algorithms by proposing a hybrid of Differential Evolution and Minimization of Metabolic Adjustments (DEMOMA). Differential Evolution (DE) is known as population-based stochastic search algorithm with few tuneable parameter control. Minimization of Metabolic Adjustment (MOMA) is one of the constraint based algorithms which act to simulate the cellular metabolism after perturbation (gene knockout) occurred to the metabolic model. The strength of MOMA is the ability to simulate the strains that have undergone mutation precisely compared to Flux Balance Analysis. The data set used for the production of fumaric acid is S. cerevisiae whereas data set for lycopene production is Y. lipolytica metabolic networks model. Experimental results show that the DEMOMA was able to improve the growth rate for the fumaric acid production rate while for the lycopene production, Biomass Product Coupled Yield (BPCY) and production rate were both able to be optimized. 2017 Thesis http://eprints.utm.my/id/eprint/86093/ http://eprints.utm.my/id/eprint/86093/1/NoorAmeeraMazlanMFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:132584 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Mazlan, Noor Ameera Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
description	Microbial strains can be optimized using metabolic engineering which implements gene knockout techniques. These techniques manipulate potential genes to increase the yield of metabolites through restructuring metabolic networks. Nowadays, several hybrid optimization algorithms have been proposed to optimize the microbial strains. However, the existing algorithms were unable to obtain optimal strains because the nonessential genes are hardly to be diagnosed and need to be removed due to high complexity of metabolic network. Therefore, the main goal of this study is to overcome the limitation of the existing algorithms by proposing a hybrid of Differential Evolution and Minimization of Metabolic Adjustments (DEMOMA). Differential Evolution (DE) is known as population-based stochastic search algorithm with few tuneable parameter control. Minimization of Metabolic Adjustment (MOMA) is one of the constraint based algorithms which act to simulate the cellular metabolism after perturbation (gene knockout) occurred to the metabolic model. The strength of MOMA is the ability to simulate the strains that have undergone mutation precisely compared to Flux Balance Analysis. The data set used for the production of fumaric acid is S. cerevisiae whereas data set for lycopene production is Y. lipolytica metabolic networks model. Experimental results show that the DEMOMA was able to improve the growth rate for the fumaric acid production rate while for the lycopene production, Biomass Product Coupled Yield (BPCY) and production rate were both able to be optimized.
format	Thesis
qualification_level	Master's degree
author	Mazlan, Noor Ameera
author_facet	Mazlan, Noor Ameera
author_sort	Mazlan, Noor Ameera
title	Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
title_short	Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
title_full	Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
title_fullStr	Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
title_full_unstemmed	Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
title_sort	hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2017
url	http://eprints.utm.my/id/eprint/86093/1/NoorAmeeraMazlanMFC2017.pdf
_version_	1747818494506827776

Hybrid approach for metabolites production using differential evolution and minimization of metabolic adjustment

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