LAPSE:2020.1238
Published Article
LAPSE:2020.1238
An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli
December 22, 2020
Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.
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Keywords
kinetic model, kinetic parameters estimation, metabolic engineering, PSO algorithm, Se-PSO algorithm
Subject
Suggested Citation
Kunna MA, Kadir TAA, Remli MA, Ali NM, Moorthy K, Muhammad N. An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli. (2020). LAPSE:2020.1238
Author Affiliations
Kunna MA: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia
Kadir TAA: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia [ORCID]
Remli MA: Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, Kota Bharu 16100, Kelantan, Malaysia
Ali NM: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia
Moorthy K: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia [ORCID]
Muhammad N: Centre for Mathematical Sciences, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia
Kadir TAA: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia [ORCID]
Remli MA: Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, Kota Bharu 16100, Kelantan, Malaysia
Ali NM: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia
Moorthy K: Faculty of Computing, College of Computing and Applied Science, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia [ORCID]
Muhammad N: Centre for Mathematical Sciences, Universiti Malaysia Pahang, Kuantan, Pahang 26300, Malaysia
Journal Name
Processes
Volume
8
Issue
8
Article Number
E963
Year
2020
Publication Date
2020-08-10
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr8080963, Publication Type: Journal Article
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Published Article
LAPSE:2020.1238
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doi:10.3390/pr8080963
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Version History
[v1] (Original Submission)
Dec 22, 2020
Verified by curator on
Dec 22, 2020
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v1
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https://psecommunity.org/LAPSE:2020.1238
Original Submitter
Calvin Tsay
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