TY - GEN
T1 - An Enhancement of Succinate Production Using a Hybrid of Bacterial Foraging Optimization Algorithm
AU - Siow, Shen Yee
AU - Mohamad, Mohd Saberi
AU - Choon, Yee Wen
AU - Remli, Muhammad Akmal
AU - Majid, Hairudin Abdul
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Bacterial foraging algorithm
KW - Dynamic flux balance analysis
KW - Escherichia coli
KW - Gene knockout
KW - Metabolic engineering
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U2 - 10.1007/978-3-030-85990-9_47
DO - 10.1007/978-3-030-85990-9_47
M3 - Conference contribution
AN - SCOPUS:85121801245
SN - 9783030859893
T3 - Lecture Notes in Networks and Systems
SP - 591
EP - 601
BT - Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021
A2 - Al-Emran, Mostafa
A2 - Al-Sharafi, Mohammed A.
A2 - Al-Kabi, Mohammed N.
A2 - Shaalan, Khaled
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2021
Y2 - 25 June 2021 through 26 June 2021
ER -