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Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems

Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bacteria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico. The tools that could facilitate this process are...

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Main Authors: Muhammad Akmal, Remli, Mohd Saberi, Mohamad, Safaai, Deris, Azurah, A. Samah, Omatu, Sigeru, Corchado, Juan Manuel
Format: Article
Language:English
Published: Elsevier 2019
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/23650/1/Cooperative%20enhanced%20scatter%20search%20with%20opposition-based%20learning1.pdf
http://umpir.ump.edu.my/id/eprint/23650/
https://doi.org/10.1016/j.eswa.2018.09.020
https://doi.org/10.1016/j.eswa.2018.09.020
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spelling my.ump.umpir.236502019-01-08T02:41:53Z http://umpir.ump.edu.my/id/eprint/23650/ Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems Muhammad Akmal, Remli Mohd Saberi, Mohamad Safaai, Deris Azurah, A. Samah Omatu, Sigeru Corchado, Juan Manuel QA75 Electronic computers. Computer science Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bacteria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico. The tools that could facilitate this process are known as the kinetic models. Kinetic models of cellular metabolism are important in assisting researchers to understand the rational design of biological systems, predicting metabolites production, and improving bio-products development. However, the most challenging task in model development is parameter estimation, which is the process of identifying an unknown value of model parameters which provides the best fit between the model output and a set of experimental data. Due to the increased complexity and high dimensionality of the models, which are extremely nonlinear and contain large numbers of kinetic parameters, parameter estimation is known to be difficult and time-consuming. This study proposes a cooperative enhanced scatter search with opposition-based learning schemes (CeSSOL) for parameter estimation in large-scale biology models. The method was executed in parallel with the proposed cooperative mechanism in order to exchange information (kinetic parameters) between individual threads. Each thread consists of different parameters settings that enhance the systemic properties in obtaining the global minimum. The performance of the proposed method was assessed against two large-scale microorganisms models using mammalian and bacteria cells. The results revealed that the proposed method recorded faster computation time compared to other methods. The study has also demonstrated that the proposed method can be used to provide more accurate and faster estimation of kinetic models, indicating the potential benefits of utilizing this method for expert systems of industrial biotechnology. abcbcde Elsevier 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23650/1/Cooperative%20enhanced%20scatter%20search%20with%20opposition-based%20learning1.pdf Muhammad Akmal, Remli and Mohd Saberi, Mohamad and Safaai, Deris and Azurah, A. Samah and Omatu, Sigeru and Corchado, Juan Manuel (2019) Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems. Expert Systems with Applications, 116. pp. 131-146. ISSN 0957-4174 https://doi.org/10.1016/j.eswa.2018.09.020 https://doi.org/10.1016/j.eswa.2018.09.020
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Muhammad Akmal, Remli
Mohd Saberi, Mohamad
Safaai, Deris
Azurah, A. Samah
Omatu, Sigeru
Corchado, Juan Manuel
Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
description Industrial bioprocesses development nowadays is concerned with producing chemicals using yeast, bacteria and therapeutic proteins in mammalian cells. This involves the utilization of microorganism cells as factories and re-engineering them in silico. The tools that could facilitate this process are known as the kinetic models. Kinetic models of cellular metabolism are important in assisting researchers to understand the rational design of biological systems, predicting metabolites production, and improving bio-products development. However, the most challenging task in model development is parameter estimation, which is the process of identifying an unknown value of model parameters which provides the best fit between the model output and a set of experimental data. Due to the increased complexity and high dimensionality of the models, which are extremely nonlinear and contain large numbers of kinetic parameters, parameter estimation is known to be difficult and time-consuming. This study proposes a cooperative enhanced scatter search with opposition-based learning schemes (CeSSOL) for parameter estimation in large-scale biology models. The method was executed in parallel with the proposed cooperative mechanism in order to exchange information (kinetic parameters) between individual threads. Each thread consists of different parameters settings that enhance the systemic properties in obtaining the global minimum. The performance of the proposed method was assessed against two large-scale microorganisms models using mammalian and bacteria cells. The results revealed that the proposed method recorded faster computation time compared to other methods. The study has also demonstrated that the proposed method can be used to provide more accurate and faster estimation of kinetic models, indicating the potential benefits of utilizing this method for expert systems of industrial biotechnology. abcbcde
format Article
author Muhammad Akmal, Remli
Mohd Saberi, Mohamad
Safaai, Deris
Azurah, A. Samah
Omatu, Sigeru
Corchado, Juan Manuel
author_facet Muhammad Akmal, Remli
Mohd Saberi, Mohamad
Safaai, Deris
Azurah, A. Samah
Omatu, Sigeru
Corchado, Juan Manuel
author_sort Muhammad Akmal, Remli
title Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
title_short Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
title_full Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
title_fullStr Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
title_full_unstemmed Cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
title_sort cooperative enhanced scatter search with opposition-based learning schemes for parameter estimation in high dimensional kinetic models of biological systems
publisher Elsevier
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/23650/1/Cooperative%20enhanced%20scatter%20search%20with%20opposition-based%20learning1.pdf
http://umpir.ump.edu.my/id/eprint/23650/
https://doi.org/10.1016/j.eswa.2018.09.020
https://doi.org/10.1016/j.eswa.2018.09.020
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score 13.211869

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