Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO)
Due to complex nature of metabolic pathways, E. coli metabolic model kinetic parameters are difficult to detect experimentally. Thus, obtaining accurate kinetic data for all reactions in an E. coli metabolic model is a technically-challenging process. So, Garra Rufa-inspired Optimization (GRO) Algor...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/44238/ |
| _version_ | 1848827318802317312 |
|---|---|
| author | Jasni, Mohamad Zain Azrag, Mohammed Adam Kunna Saiful Farik, Mat Yatin Aldehim, Ghadah Zuhaira, Muhammad Zain Shaiba, Hadil Alturki, Nazik Sapiah, Sakri Azlinah, Mohamed Jaber, Aqeel S. |
| author_facet | Jasni, Mohamad Zain Azrag, Mohammed Adam Kunna Saiful Farik, Mat Yatin Aldehim, Ghadah Zuhaira, Muhammad Zain Shaiba, Hadil Alturki, Nazik Sapiah, Sakri Azlinah, Mohamed Jaber, Aqeel S. |
| author_sort | Jasni, Mohamad Zain |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Due to complex nature of metabolic pathways, E. coli metabolic model kinetic parameters are difficult to detect experimentally. Thus, obtaining accurate kinetic data for all reactions in an E. coli metabolic model is a technically-challenging process. So, Garra Rufa-inspired Optimization (GRO) Algorithm is applied to the primary metabolic network of E. coli as a model to estimate small-scale kinetic parameters and increase the kinetic accuracy. Also, the Differential Algebraic Equations (DAE) is used to represent the glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate, and acetate production pathways of Escherichia coli in the metabolic network. Based on the behavior of the Garra Rufa fish, a route is modelled in which particles are sorted into groups and each group is guided by the best value. In addition, the fitness of the group leaders determines whether or not these particles are able to switch groups. In this study, experimental data was used to estimate seven kinetic parameters. However, the numerical results of The Relative Error (RE) and the Mean Error (ME) reveal that the observed and anticipated data are in line with the results. As a result of this new method, it was discovered that small-scale and even whole-cell dynamic models can be estimated accurately. |
| first_indexed | 2025-11-15T03:58:49Z |
| format | Article |
| id | ump-44238 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:49Z |
| publishDate | 2024 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-442382025-08-06T00:53:58Z https://umpir.ump.edu.my/id/eprint/44238/ Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) Jasni, Mohamad Zain Azrag, Mohammed Adam Kunna Saiful Farik, Mat Yatin Aldehim, Ghadah Zuhaira, Muhammad Zain Shaiba, Hadil Alturki, Nazik Sapiah, Sakri Azlinah, Mohamed Jaber, Aqeel S. QA75 Electronic computers. Computer science QR Microbiology Due to complex nature of metabolic pathways, E. coli metabolic model kinetic parameters are difficult to detect experimentally. Thus, obtaining accurate kinetic data for all reactions in an E. coli metabolic model is a technically-challenging process. So, Garra Rufa-inspired Optimization (GRO) Algorithm is applied to the primary metabolic network of E. coli as a model to estimate small-scale kinetic parameters and increase the kinetic accuracy. Also, the Differential Algebraic Equations (DAE) is used to represent the glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate, and acetate production pathways of Escherichia coli in the metabolic network. Based on the behavior of the Garra Rufa fish, a route is modelled in which particles are sorted into groups and each group is guided by the best value. In addition, the fitness of the group leaders determines whether or not these particles are able to switch groups. In this study, experimental data was used to estimate seven kinetic parameters. However, the numerical results of The Relative Error (RE) and the Mean Error (ME) reveal that the observed and anticipated data are in line with the results. As a result of this new method, it was discovered that small-scale and even whole-cell dynamic models can be estimated accurately. IEEE 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 https://umpir.ump.edu.my/id/eprint/44238/1/Kinetic%20parameters%20estimation%20of%20the%20escherichia%20Coli.pdf Jasni, Mohamad Zain and Azrag, Mohammed Adam Kunna and Saiful Farik, Mat Yatin and Aldehim, Ghadah and Zuhaira, Muhammad Zain and Shaiba, Hadil and Alturki, Nazik and Sapiah, Sakri and Azlinah, Mohamed and Jaber, Aqeel S. (2024) Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO). IEEE Access, 12. pp. 165889 -165902. ISSN 2169-3536. (Published) https://doi.org/10.1109/ACCESS.2024.3422450 https://doi.org/10.1109/ACCESS.2024.3422450 https://doi.org/10.1109/ACCESS.2024.3422450 |
| spellingShingle | QA75 Electronic computers. Computer science QR Microbiology Jasni, Mohamad Zain Azrag, Mohammed Adam Kunna Saiful Farik, Mat Yatin Aldehim, Ghadah Zuhaira, Muhammad Zain Shaiba, Hadil Alturki, Nazik Sapiah, Sakri Azlinah, Mohamed Jaber, Aqeel S. Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) |
| title | Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) |
| title_full | Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) |
| title_fullStr | Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) |
| title_full_unstemmed | Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) |
| title_short | Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) |
| title_sort | kinetic parameters estimation of the escherichia coli (e. coli) model by garra rufa-inspired optimization algorithm (gro) |
| topic | QA75 Electronic computers. Computer science QR Microbiology |
| url | https://umpir.ump.edu.my/id/eprint/44238/ https://umpir.ump.edu.my/id/eprint/44238/ https://umpir.ump.edu.my/id/eprint/44238/ |