Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso
The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enha...
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my.ump.umpir.382232023-09-05T03:49:49Z http://umpir.ump.edu.my/id/eprint/38223/ Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso Mohammed Adam Kunna, Azrag Jasni Mohamad, Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Jaber, Aqeel Sakhy Abdlrhman, Hybat Salih Mohamed Ahmed, Yasmeen Hafiz Zaki Husain, Mohamed Saad Bala QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight ((Formula presented.)) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed. MDPI 2023-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38223/1/Estimation%20of%20small-scale%20kinetic%20parameters%20of%20escherichia%20coli%20%28E.%20coli%29%20model%20by%20enhanced%20segment.pdf Mohammed Adam Kunna, Azrag and Jasni Mohamad, Zain and Tuty Asmawaty, Abdul Kadir and Marina, Yusoff and Jaber, Aqeel Sakhy and Abdlrhman, Hybat Salih Mohamed and Ahmed, Yasmeen Hafiz Zaki and Husain, Mohamed Saad Bala (2023) Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso. Processes, 11 (126). pp. 1-25. ISSN 2227-9717. (Published) https://doi.org/10.3390/pr11010126 https://doi.org/10.3390/pr11010126 |
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QD Chemistry T Technology (General) TA Engineering (General). Civil engineering (General) TP Chemical technology Mohammed Adam Kunna, Azrag Jasni Mohamad, Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Jaber, Aqeel Sakhy Abdlrhman, Hybat Salih Mohamed Ahmed, Yasmeen Hafiz Zaki Husain, Mohamed Saad Bala Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
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The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight ((Formula presented.)) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed. |
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Article |
author |
Mohammed Adam Kunna, Azrag Jasni Mohamad, Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Jaber, Aqeel Sakhy Abdlrhman, Hybat Salih Mohamed Ahmed, Yasmeen Hafiz Zaki Husain, Mohamed Saad Bala |
author_facet |
Mohammed Adam Kunna, Azrag Jasni Mohamad, Zain Tuty Asmawaty, Abdul Kadir Marina, Yusoff Jaber, Aqeel Sakhy Abdlrhman, Hybat Salih Mohamed Ahmed, Yasmeen Hafiz Zaki Husain, Mohamed Saad Bala |
author_sort |
Mohammed Adam Kunna, Azrag |
title |
Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
title_short |
Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
title_full |
Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
title_fullStr |
Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
title_full_unstemmed |
Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
title_sort |
estimation of small-scale kinetic parameters of escherichia coli (e. coli) model by enhanced segment particle swarm optimization algorithm ese-pso |
publisher |
MDPI |
publishDate |
2023 |
url |
http://umpir.ump.edu.my/id/eprint/38223/1/Estimation%20of%20small-scale%20kinetic%20parameters%20of%20escherichia%20coli%20%28E.%20coli%29%20model%20by%20enhanced%20segment.pdf http://umpir.ump.edu.my/id/eprint/38223/ https://doi.org/10.3390/pr11010126 https://doi.org/10.3390/pr11010126 |
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1776247229716103168 |
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13.188404 |