A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen

In silico metabolic engineering has shown many successful results in genome - scale model reconstruction and modification of metabolic network by implementing reaction deletion strategies to improve microbial strain such as production yield and growth rate. While improving the...

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Main Authors: Azhar, Amira Husna, Tan Ah Chik @ Mohamad, Mohd Saberi, Deris, Safaai, Napis, Suhaimi, Sinnott, Richard
Format: Article
Language:English
Published: Universiti Teknologi Malaysia 2016
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Online Access:http://eprints.utm.my/id/eprint/68423/1/AmiraHusnaAzhar2016_AHybridofAntColony.pdf
http://eprints.utm.my/id/eprint/68423/
https://pure.utm.my/en/publications/a-hybrid-of-ant-colony-optimization-and-flux-variability-analysis
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spelling my.utm.684232017-11-14T06:23:13Z http://eprints.utm.my/id/eprint/68423/ A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen Azhar, Amira Husna Tan Ah Chik @ Mohamad, Mohd Saberi Deris, Safaai Napis, Suhaimi Sinnott, Richard QA75 Electronic computers. Computer science In silico metabolic engineering has shown many successful results in genome - scale model reconstruction and modification of metabolic network by implementing reaction deletion strategies to improve microbial strain such as production yield and growth rate. While improving the metabolites production, optimization algorithm has been implemented gradually in previous studies to identify the near - optimal sets of reaction knockout to obtain the best results. However, previous works implemented other algorithms that differ than this study which faced with several issues such as premature convergence and able to only produce low production yield because of ineffective algorithm and existence of complex metabolic data. The lack of effective genome models is because of the presence thousands of reactions in the metabolic network caused complex and high dimensional data size that contains competing pathway of non - desirable product. Indeed, the suitable population size and knockout number for this new algorithm have been tested previously. This study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux variability analysis (ACOFVA) to predict near - optimal sets of reactions knockout in an effort to improve the growth rates and the production rate of L - phenylalanine and biohydrogen in Saccharomyces cerevisiae and cyanobacteria Synechocystis sp PCC6803 respectively. Universiti Teknologi Malaysia 2016-01-07 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/68423/1/AmiraHusnaAzhar2016_AHybridofAntColony.pdf Azhar, Amira Husna and Tan Ah Chik @ Mohamad, Mohd Saberi and Deris, Safaai and Napis, Suhaimi and Sinnott, Richard (2016) A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen. International Journal of Advances in Soft Computing and Its Applications, 8 (2). pp. 161-180. ISSN 2074-8523 https://pure.utm.my/en/publications/a-hybrid-of-ant-colony-optimization-and-flux-variability-analysis
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/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Azhar, Amira Husna
Tan Ah Chik @ Mohamad, Mohd Saberi
Deris, Safaai
Napis, Suhaimi
Sinnott, Richard
A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
description In silico metabolic engineering has shown many successful results in genome - scale model reconstruction and modification of metabolic network by implementing reaction deletion strategies to improve microbial strain such as production yield and growth rate. While improving the metabolites production, optimization algorithm has been implemented gradually in previous studies to identify the near - optimal sets of reaction knockout to obtain the best results. However, previous works implemented other algorithms that differ than this study which faced with several issues such as premature convergence and able to only produce low production yield because of ineffective algorithm and existence of complex metabolic data. The lack of effective genome models is because of the presence thousands of reactions in the metabolic network caused complex and high dimensional data size that contains competing pathway of non - desirable product. Indeed, the suitable population size and knockout number for this new algorithm have been tested previously. This study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux variability analysis (ACOFVA) to predict near - optimal sets of reactions knockout in an effort to improve the growth rates and the production rate of L - phenylalanine and biohydrogen in Saccharomyces cerevisiae and cyanobacteria Synechocystis sp PCC6803 respectively.
format Article
author Azhar, Amira Husna
Tan Ah Chik @ Mohamad, Mohd Saberi
Deris, Safaai
Napis, Suhaimi
Sinnott, Richard
author_facet Azhar, Amira Husna
Tan Ah Chik @ Mohamad, Mohd Saberi
Deris, Safaai
Napis, Suhaimi
Sinnott, Richard
author_sort Azhar, Amira Husna
title A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
title_short A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
title_full A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
title_fullStr A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
title_full_unstemmed A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
title_sort hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen
publisher Universiti Teknologi Malaysia
publishDate 2016
url http://eprints.utm.my/id/eprint/68423/1/AmiraHusnaAzhar2016_AHybridofAntColony.pdf
http://eprints.utm.my/id/eprint/68423/
https://pure.utm.my/en/publications/a-hybrid-of-ant-colony-optimization-and-flux-variability-analysis
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score 13.211869