Please use this identifier to cite or link to this item:
http://hdl.handle.net/123456789/3150
DC Field | Value | Language |
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dc.contributor.author | Siow, Shen Yee | en_US |
dc.contributor.author | Mohamad, Mohd Saberi | en_US |
dc.contributor.author | Choon, Yee Wen | en_US |
dc.contributor.author | Remli, Muhammad Akmal | en_US |
dc.contributor.author | Majid, Hairudin Abdul | en_US |
dc.date.accessioned | 2022-07-17T09:23:54Z | - |
dc.date.available | 2022-07-17T09:23:54Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/3150 | - |
dc.description | Scopus | en_US |
dc.description.abstract | 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.relation.ispartof | Lecture Notes in Networks and Systems | en_US |
dc.subject | Bacterial foraging algorithm | en_US |
dc.subject | Dynamic flux balance analysis | en_US |
dc.subject | Escherichia coli | en_US |
dc.subject | Gene knockout | en_US |
dc.subject | Metabolic engineering | en_US |
dc.title | An Enhancement of Succinate Production Using a Hybrid of Bacterial Foraging Optimization Algorithm | en_US |
dc.type | National | en_US |
dc.relation.conference | International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2021 | en_US |
dc.identifier.doi | 23673370 | - |
dc.identifier.doi | 978-303085989-3 | - |
dc.identifier.doi | 10.1007/978-3-030-85990-9_47 | - |
dc.description.page | 591 - 601 | en_US |
dc.volume | 322 | en_US |
dc.date.seminarstartdate | 2021-06-25 | - |
dc.date.seminarenddate | 2021-06-26 | - |
dc.description.placeofseminar | Al Buraimi | en_US |
dc.description.type | Proceeding Papers | en_US |
item.languageiso639-1 | en | - |
item.openairetype | National | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
Appears in Collections: | Faculty of Data Science and Computing - Proceedings |
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