Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of micro...

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Autores principales:	Yee Wen Choon, Mohd Saberi Mohamad, Safaai Deris, Rosli Md Illias, Chuii Khim Chong, Lian En Chai, Sigeru Omatu, Juan Manuel Corchado
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0f94c681a263496098c88eabee36d99e
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spelling	oai:doaj.org-article:0f94c681a263496098c88eabee36d99e2021-11-25T06:07:51ZDifferential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.1932-620310.1371/journal.pone.0102744https://doaj.org/article/0f94c681a263496098c88eabee36d99e2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25047076/?tool=EBIhttps://doaj.org/toc/1932-6203Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.Yee Wen ChoonMohd Saberi MohamadSafaai DerisRosli Md IlliasChuii Khim ChongLian En ChaiSigeru OmatuJuan Manuel CorchadoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e102744 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yee Wen Choon Mohd Saberi Mohamad Safaai Deris Rosli Md Illias Chuii Khim Chong Lian En Chai Sigeru Omatu Juan Manuel Corchado Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
description	Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.
format	article
author	Yee Wen Choon Mohd Saberi Mohamad Safaai Deris Rosli Md Illias Chuii Khim Chong Lian En Chai Sigeru Omatu Juan Manuel Corchado
author_facet	Yee Wen Choon Mohd Saberi Mohamad Safaai Deris Rosli Md Illias Chuii Khim Chong Lian En Chai Sigeru Omatu Juan Manuel Corchado
author_sort	Yee Wen Choon
title	Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
title_short	Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
title_full	Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
title_fullStr	Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
title_full_unstemmed	Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
title_sort	differential bees flux balance analysis with optknock for in silico microbial strains optimization.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/0f94c681a263496098c88eabee36d99e
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_version_	1718414147617554432

Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.

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