Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains

Metabolic engineering is highly demanded currently for the production of various useful compounds such as succinate and lactate that are very useful in food, pharmaceutical, fossil fuels, and energy industries. Gene or reaction deletion known as knockout is one of the strategies used in in silico me...

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Main Author:	Khairil Anuar, Mohammad Fahmi Arieef
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/81609/1/MohammadFahmiArieefMFC2018.pdf
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id	my-utm-ep.81609
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spelling	my-utm-ep.816092019-09-10T01:49:57Z Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains 2018 Khairil Anuar, Mohammad Fahmi Arieef QA75 Electronic computers. Computer science Metabolic engineering is highly demanded currently for the production of various useful compounds such as succinate and lactate that are very useful in food, pharmaceutical, fossil fuels, and energy industries. Gene or reaction deletion known as knockout is one of the strategies used in in silico metabolic engineering to change the metabolism of the chosen microbial cells to obtain the desired phenotypes. However, the size and complexity of the metabolic network are a challenge in determining the near-optimal set of genes to be knocked out in the metabolism due to the presence of competing pathway that interrupts the high production of desired metabolite, leading to low production rate and growth rate of the required microorganisms. In addition, the inefficiency of existing algorithms in reconstructing high growth rate and production rate becomes one of the issues to be solved. Therefore, this research proposes Dynamic Flux Variability Analysis (DFVA) algorithm to identify the best knockout reaction combination to improve the production of desired metabolites in microorganisms. Based on the experimental results, DFVA shows an improvement of growth rate of succinate and lactate by 12.06% and 47.16% respectively in E. coli and by 4.62% and 47.98% respectively in S. Cerevisae. Suggested reactions to be knocked out to improve the production of succinate and lactate have been identified and validated through the biological database. 2018 Thesis http://eprints.utm.my/id/eprint/81609/ http://eprints.utm.my/id/eprint/81609/1/MohammadFahmiArieefMFC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:119432 masters Universiti Teknologi Malaysia Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Khairil Anuar, Mohammad Fahmi Arieef Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
description	Metabolic engineering is highly demanded currently for the production of various useful compounds such as succinate and lactate that are very useful in food, pharmaceutical, fossil fuels, and energy industries. Gene or reaction deletion known as knockout is one of the strategies used in in silico metabolic engineering to change the metabolism of the chosen microbial cells to obtain the desired phenotypes. However, the size and complexity of the metabolic network are a challenge in determining the near-optimal set of genes to be knocked out in the metabolism due to the presence of competing pathway that interrupts the high production of desired metabolite, leading to low production rate and growth rate of the required microorganisms. In addition, the inefficiency of existing algorithms in reconstructing high growth rate and production rate becomes one of the issues to be solved. Therefore, this research proposes Dynamic Flux Variability Analysis (DFVA) algorithm to identify the best knockout reaction combination to improve the production of desired metabolites in microorganisms. Based on the experimental results, DFVA shows an improvement of growth rate of succinate and lactate by 12.06% and 47.16% respectively in E. coli and by 4.62% and 47.98% respectively in S. Cerevisae. Suggested reactions to be knocked out to improve the production of succinate and lactate have been identified and validated through the biological database.
format	Thesis
qualification_level	Master's degree
author	Khairil Anuar, Mohammad Fahmi Arieef
author_facet	Khairil Anuar, Mohammad Fahmi Arieef
author_sort	Khairil Anuar, Mohammad Fahmi Arieef
title	Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
title_short	Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
title_full	Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
title_fullStr	Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
title_full_unstemmed	Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
title_sort	enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains
granting_institution	Universiti Teknologi Malaysia
granting_department	Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/81609/1/MohammadFahmiArieefMFC2018.pdf
_version_	1747818370314534912

Enhanced dynamic flux variability analysis for improving growth and production rate in microbial strains

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