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Gene knockout identification using an extension of bees hill flux balance analysis

Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to iden...

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Main Authors: Yee, Wen Choon, Mohamad, Mohd. Saberi, Deris, Safaai, Chuii, Khim Chong, Omatu, Sigeru, Manuel, Corchado Juan
Format: Article
Published: Hindawi Publishing 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/55470/
http://dx.doi.org/10.1155/2015/124537
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spelling my.utm.554702017-08-08T08:23:37Z http://eprints.utm.my/id/eprint/55470/ Gene knockout identification using an extension of bees hill flux balance analysis Yee, Wen Choon Mohamad, Mohd. Saberi Deris, Safaai Chuii, Khim Chong Omatu, Sigeru Manuel, Corchado Juan QA75 Electronic computers. Computer science Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes Hindawi Publishing 2015 Article PeerReviewed Yee, Wen Choon and Mohamad, Mohd. Saberi and Deris, Safaai and Chuii, Khim Chong and Omatu, Sigeru and Manuel, Corchado Juan (2015) Gene knockout identification using an extension of bees hill flux balance analysis. BioMed Research International, 2015 . ISSN 2314-6133 http://dx.doi.org/10.1155/2015/124537 DOI:10.1155/2015/124537
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Yee, Wen Choon
Mohamad, Mohd. Saberi
Deris, Safaai
Chuii, Khim Chong
Omatu, Sigeru
Manuel, Corchado Juan
Gene knockout identification using an extension of bees hill flux balance analysis
description Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes
format Article
author Yee, Wen Choon
Mohamad, Mohd. Saberi
Deris, Safaai
Chuii, Khim Chong
Omatu, Sigeru
Manuel, Corchado Juan
author_facet Yee, Wen Choon
Mohamad, Mohd. Saberi
Deris, Safaai
Chuii, Khim Chong
Omatu, Sigeru
Manuel, Corchado Juan
author_sort Yee, Wen Choon
title Gene knockout identification using an extension of bees hill flux balance analysis
title_short Gene knockout identification using an extension of bees hill flux balance analysis
title_full Gene knockout identification using an extension of bees hill flux balance analysis
title_fullStr Gene knockout identification using an extension of bees hill flux balance analysis
title_full_unstemmed Gene knockout identification using an extension of bees hill flux balance analysis
title_sort gene knockout identification using an extension of bees hill flux balance analysis
publisher Hindawi Publishing
publishDate 2015
url http://eprints.utm.my/id/eprint/55470/
http://dx.doi.org/10.1155/2015/124537
_version_ 1643653806612807680
score 13.222552

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