Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization
This paper compares different initialization methods and investigates their performance and effects on estimating kinetic parameters’ value in models of biological systems. Estimating parameters values is difficult and time-consuming process due to their highly nonlinear and huge number of kinetic p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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The Science and Information (SAI) Organization Limited
2022
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45597/ |
| _version_ | 1848827461724274688 |
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| author | Muhammad Akmal, Remli Nor Syahidatul Nadiah, Ismail Noor Azida, Sahabudin Nor Bakiah, Abd Warif |
| author_facet | Muhammad Akmal, Remli Nor Syahidatul Nadiah, Ismail Noor Azida, Sahabudin Nor Bakiah, Abd Warif |
| author_sort | Muhammad Akmal, Remli |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper compares different initialization methods and investigates their performance and effects on estimating kinetic parameters’ value in models of biological systems. Estimating parameters values is difficult and time-consuming process due to their highly nonlinear and huge number of kinetic parameters involved. Global optimization method based on an enhanced scatter search (ESS) algorithm is a suitable choice to address this issue. However, despite its resounding success, the performance of ESS may decrease in solving high dimension problem. In this work, several choices of initialization methods are compared and experimental results indicated that the algorithm is sensitive to the initial value of kinetic parameters. Statistical results revealed that uniformly distributed random number generator (RNG) and controlled randomization (CR) that being used in ESS may lead to poor algorithm performance. In addition, the different initialization methods also influenced model accuracy. Our proposed methodology shows that initialization based on opposition-based learning scheme have shown 10% better accuracy in term of cost function. |
| first_indexed | 2025-11-15T04:01:05Z |
| format | Article |
| id | ump-45597 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:01:05Z |
| publishDate | 2022 |
| publisher | The Science and Information (SAI) Organization Limited |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-455972025-09-08T09:13:50Z https://umpir.ump.edu.my/id/eprint/45597/ Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization Muhammad Akmal, Remli Nor Syahidatul Nadiah, Ismail Noor Azida, Sahabudin Nor Bakiah, Abd Warif QA75 Electronic computers. Computer science TP Chemical technology This paper compares different initialization methods and investigates their performance and effects on estimating kinetic parameters’ value in models of biological systems. Estimating parameters values is difficult and time-consuming process due to their highly nonlinear and huge number of kinetic parameters involved. Global optimization method based on an enhanced scatter search (ESS) algorithm is a suitable choice to address this issue. However, despite its resounding success, the performance of ESS may decrease in solving high dimension problem. In this work, several choices of initialization methods are compared and experimental results indicated that the algorithm is sensitive to the initial value of kinetic parameters. Statistical results revealed that uniformly distributed random number generator (RNG) and controlled randomization (CR) that being used in ESS may lead to poor algorithm performance. In addition, the different initialization methods also influenced model accuracy. Our proposed methodology shows that initialization based on opposition-based learning scheme have shown 10% better accuracy in term of cost function. The Science and Information (SAI) Organization Limited 2022 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/45597/1/Parameter%20estimation%20in%20computational%20systems%20biology%20models.pdf Muhammad Akmal, Remli and Nor Syahidatul Nadiah, Ismail and Noor Azida, Sahabudin and Nor Bakiah, Abd Warif (2022) Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization. International Journal of Advanced Computer Science and Applications (IJACSA), 13 (8). pp. 473-478. ISSN 2158-107X ; 2156-5570(Online). (Published) https://doi.org/10.14569/IJACSA.2022.0130854 https://doi.org/10.14569/IJACSA.2022.0130854 https://doi.org/10.14569/IJACSA.2022.0130854 |
| spellingShingle | QA75 Electronic computers. Computer science TP Chemical technology Muhammad Akmal, Remli Nor Syahidatul Nadiah, Ismail Noor Azida, Sahabudin Nor Bakiah, Abd Warif Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| title | Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| title_full | Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| title_fullStr | Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| title_full_unstemmed | Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| title_short | Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| title_sort | parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization |
| topic | QA75 Electronic computers. Computer science TP Chemical technology |
| url | https://umpir.ump.edu.my/id/eprint/45597/ https://umpir.ump.edu.my/id/eprint/45597/ https://umpir.ump.edu.my/id/eprint/45597/ |