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Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad result...

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Main Authors: Chong, Chuiikhim, Mohamad, Mohd. Saberi, Deris, Safaai, Shamsir, Mohd. Shahir, Chai, Lian En, Choon, Yeewen
格式: Article
出版: World Scientific Publishing Company 2014
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QA75 Electronic computers. Computer science
在線閱讀:http://eprints.utm.my/id/eprint/63185/
http://dx.doi.org/10.1142/S0218339014500065
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spelling my.utm.631852017-06-15T02:28:16Z http://eprints.utm.my/id/eprint/63185/ Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways Chong, Chuiikhim Mohamad, Mohd. Saberi Deris, Safaai Shamsir, Mohd. Shahir Chai, Lian En Choon, Yeewen QA75 Electronic computers. Computer science When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder-Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm. World Scientific Publishing Company 2014 Article PeerReviewed Chong, Chuiikhim and Mohamad, Mohd. Saberi and Deris, Safaai and Shamsir, Mohd. Shahir and Chai, Lian En and Choon, Yeewen (2014) Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways. Journal of Biological Systems, 22 (1). pp. 101-121. ISSN 0218-3390 http://dx.doi.org/10.1142/S0218339014500065 DOI :10.1142/S0218339014500065
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Chong, Chuiikhim
Mohamad, Mohd. Saberi
Deris, Safaai
Shamsir, Mohd. Shahir
Chai, Lian En
Choon, Yeewen
Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
description When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder-Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.
format Article
author Chong, Chuiikhim
Mohamad, Mohd. Saberi
Deris, Safaai
Shamsir, Mohd. Shahir
Chai, Lian En
Choon, Yeewen
author_facet Chong, Chuiikhim
Mohamad, Mohd. Saberi
Deris, Safaai
Shamsir, Mohd. Shahir
Chai, Lian En
Choon, Yeewen
author_sort Chong, Chuiikhim
title Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
title_short Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
title_full Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
title_fullStr Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
title_full_unstemmed Using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
title_sort using an improved bee memory differential evolution algorithm for parameter estimation to simulate biochemical pathways
publisher World Scientific Publishing Company
publishDate 2014
url http://eprints.utm.my/id/eprint/63185/
http://dx.doi.org/10.1142/S0218339014500065
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score 13.251813

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