An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems

Background: Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary...

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Main Authors: Remli, Muhammad Akmal, Mohamad, Mohd Saberi, Deris, Safaai, Sinnott, Richard, Napis, Suhaimi
Format: Article
Language:English
Published: Bentham Science Publishers 2019
Online Access:http://psasir.upm.edu.my/id/eprint/80601/1/KINETIC.pdf
http://psasir.upm.edu.my/id/eprint/80601/
https://www.eurekaselect.com/171196
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Institution: Universiti Putra Malaysia
Language: English
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spelling my.upm.eprints.806012020-11-05T18:46:58Z http://psasir.upm.edu.my/id/eprint/80601/ An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems Remli, Muhammad Akmal Mohamad, Mohd Saberi Deris, Safaai Sinnott, Richard Napis, Suhaimi Background: Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal values of kinetic parameters which may culminate in the best fit of model prediction to experimental data. Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. The improved algorithm is based on hybridization of quasi opposition-based learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a large-scale metabolic model of Chinese Hamster Ovary (CHO) cells. Results: The experimental result shows that the proposed algorithm performs better than other algorithms in terms of convergence speed and the minimum value of the objective function (loglikelihood). The estimated parameters from the experiment produce a better model by means of obtaining a reasonable good fit of model prediction to the experimental data. Conclusion: The kinetic parameters’ value obtained from our work was able to result in a reasonable best fit of model prediction to the experimental data, which contributes to a better understanding and produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building. Bentham Science Publishers 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80601/1/KINETIC.pdf Remli, Muhammad Akmal and Mohamad, Mohd Saberi and Deris, Safaai and Sinnott, Richard and Napis, Suhaimi (2019) An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems. Current Proteomics, 16 (5). pp. 427-438. ISSN 1570-1646; ESSN: 1875-6247 https://www.eurekaselect.com/171196 10.2174/1570164616666190401203128
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Background: Mathematical models play a central role in facilitating researchers to better understand and comprehensively analyze various processes in biochemical systems. Their usage is beneficial in metabolic engineering as they help predict and improve desired products. However, one of the primary challenges in model building is parameter estimation. It is the process to find nearoptimal values of kinetic parameters which may culminate in the best fit of model prediction to experimental data. Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. The improved algorithm is based on hybridization of quasi opposition-based learning in enhanced scatter search (QOBLESS) method. The algorithm is tested using a large-scale metabolic model of Chinese Hamster Ovary (CHO) cells. Results: The experimental result shows that the proposed algorithm performs better than other algorithms in terms of convergence speed and the minimum value of the objective function (loglikelihood). The estimated parameters from the experiment produce a better model by means of obtaining a reasonable good fit of model prediction to the experimental data. Conclusion: The kinetic parameters’ value obtained from our work was able to result in a reasonable best fit of model prediction to the experimental data, which contributes to a better understanding and produced more accurate model. Based on the results, the QOBLESS method can be used as an efficient parameter estimation method in large-scale kinetic model building.
format Article
author Remli, Muhammad Akmal
Mohamad, Mohd Saberi
Deris, Safaai
Sinnott, Richard
Napis, Suhaimi
spellingShingle Remli, Muhammad Akmal
Mohamad, Mohd Saberi
Deris, Safaai
Sinnott, Richard
Napis, Suhaimi
An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
author_facet Remli, Muhammad Akmal
Mohamad, Mohd Saberi
Deris, Safaai
Sinnott, Richard
Napis, Suhaimi
author_sort Remli, Muhammad Akmal
title An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
title_short An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
title_full An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
title_fullStr An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
title_full_unstemmed An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
title_sort improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
publisher Bentham Science Publishers
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/80601/1/KINETIC.pdf
http://psasir.upm.edu.my/id/eprint/80601/
https://www.eurekaselect.com/171196
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