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An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm

Genetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico method...

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Main Authors: Siow, Shen Yee, Mohamad, Mohd. Saberi, Choon, Yee Wen, Remli, Muhammad Akmal, Abdul Majid, Hairudin
Format: Conference or Workshop Item
Published: 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/100936/
http://dx.doi.org/10.1007/978-3-030-85990-9_47
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spelling my.utm.1009362023-05-27T07:38:57Z http://eprints.utm.my/id/eprint/100936/ An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm Siow, Shen Yee Mohamad, Mohd. Saberi Choon, Yee Wen Remli, Muhammad Akmal Abdul Majid, Hairudin QA75 Electronic computers. Computer science Genetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico methods have been applied to predict potential metabolic network response and optimise production. Previous methods working on relational modelling framework, such as OptKnock and OptGene, however, failed at handling its multivariable and multimodal functions optimization algorithms. This paper proposes hybridising bacterial foraging optimizationg algorithm (BFO) and dynamic flux balance analysis (DFBA) to overcome problems in OptKnock and OptGene with a nature-inspired algorithm and also to couple kinematic variables in the model to predict production of succinate in E.coli model. In-silico results showed that by knocking out genes identifed by BFODFBA, production rate of succinate is better as when compared to OptKnock and OptGene. 2022 Conference or Workshop Item PeerReviewed Siow, Shen Yee and Mohamad, Mohd. Saberi and Choon, Yee Wen and Remli, Muhammad Akmal and Abdul Majid, Hairudin (2022) An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm. In: International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2021, 25 June 2021 - 26 June 2021, Al Buraimi, Oman. http://dx.doi.org/10.1007/978-3-030-85990-9_47
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
Siow, Shen Yee
Mohamad, Mohd. Saberi
Choon, Yee Wen
Remli, Muhammad Akmal
Abdul Majid, Hairudin
An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
description Genetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico methods have been applied to predict potential metabolic network response and optimise production. Previous methods working on relational modelling framework, such as OptKnock and OptGene, however, failed at handling its multivariable and multimodal functions optimization algorithms. This paper proposes hybridising bacterial foraging optimizationg algorithm (BFO) and dynamic flux balance analysis (DFBA) to overcome problems in OptKnock and OptGene with a nature-inspired algorithm and also to couple kinematic variables in the model to predict production of succinate in E.coli model. In-silico results showed that by knocking out genes identifed by BFODFBA, production rate of succinate is better as when compared to OptKnock and OptGene.
format Conference or Workshop Item
author Siow, Shen Yee
Mohamad, Mohd. Saberi
Choon, Yee Wen
Remli, Muhammad Akmal
Abdul Majid, Hairudin
author_facet Siow, Shen Yee
Mohamad, Mohd. Saberi
Choon, Yee Wen
Remli, Muhammad Akmal
Abdul Majid, Hairudin
author_sort Siow, Shen Yee
title An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
title_short An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
title_full An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
title_fullStr An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
title_full_unstemmed An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
title_sort enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
publishDate 2022
url http://eprints.utm.my/id/eprint/100936/
http://dx.doi.org/10.1007/978-3-030-85990-9_47
_version_ 1768006587264270336
score 13.211869

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