Using an improved differential evolution algorithm for parameter estimation to simulate glycolysis pathway

Chuii Khim Chong; Mohd Saberi Mohamad; Safaai Deris; Shahir Shamsir; Afnizanfaizal Abdullah; Yee Wen Choon; Lian En Chai; Sigeru Omatu

doi:10.1007/978-3-642-28765-7_85

Using an improved differential evolution algorithm for parameter estimation to simulate glycolysis pathway

Chuii Khim Chong, Mohd Saberi Mohamad, Safaai Deris, Shahir Shamsir, Afnizanfaizal Abdullah, Yee Wen Choon, Lian En Chai, Sigeru Omatu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper presents an improved Differential Evolution algorithm (IDE). It is aimed at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Nonetheless, due to the noisy data and difficulty of the system in estimating myriad of parameters, many computation algorithms face obstacles and require longer computational time to estimate the relevant parameters. The IDE proposed in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than a Genetic Algorithm (GA) and DE. The results of IDE from this experiment show estimated optimal kinetic parameters values, shorter computation time and better accuracy of simulated results compared to the other estimation algorithms.

Original language	English
Title of host publication	Distributed Computing and Artificial Intelligence - 9th International Conference
Pages	709-716
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-28765-7_85
Publication status	Published - 2012
Externally published	Yes
Event	9th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2012 - Salamanca, Spain Duration: Mar 28 2012 → Mar 30 2012

Publication series

Name	Advances in Intelligent and Soft Computing
Volume	151 AISC
ISSN (Print)	1867-5662

Conference

Conference	9th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2012
Country/Territory	Spain
City	Salamanca
Period	3/28/12 → 3/30/12

Keywords

Differential Evolution Algorithm
Kalman Filter
Parameter Estimation
Simulation

ASJC Scopus subject areas

General Computer Science

Access to Document

10.1007/978-3-642-28765-7_85

Cite this

Chong, C. K., Mohamad, M. S., Deris, S., Shamsir, S., Abdullah, A., Choon, Y. W., Chai, L. E., & Omatu, S. (2012). Using an improved differential evolution algorithm for parameter estimation to simulate glycolysis pathway. In Distributed Computing and Artificial Intelligence - 9th International Conference (pp. 709-716). (Advances in Intelligent and Soft Computing; Vol. 151 AISC). https://doi.org/10.1007/978-3-642-28765-7_85

Using an improved differential evolution algorithm for parameter estimation to simulate glycolysis pathway. / Chong, Chuii Khim; Mohamad, Mohd Saberi; Deris, Safaai et al.
Distributed Computing and Artificial Intelligence - 9th International Conference. 2012. p. 709-716 (Advances in Intelligent and Soft Computing; Vol. 151 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chong, CK, Mohamad, MS, Deris, S, Shamsir, S, Abdullah, A, Choon, YW, Chai, LE & Omatu, S 2012, Using an improved differential evolution algorithm for parameter estimation to simulate glycolysis pathway. in Distributed Computing and Artificial Intelligence - 9th International Conference. Advances in Intelligent and Soft Computing, vol. 151 AISC, pp. 709-716, 9th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2012, Salamanca, Spain, 3/28/12. https://doi.org/10.1007/978-3-642-28765-7_85

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abstract = "This paper presents an improved Differential Evolution algorithm (IDE). It is aimed at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Nonetheless, due to the noisy data and difficulty of the system in estimating myriad of parameters, many computation algorithms face obstacles and require longer computational time to estimate the relevant parameters. The IDE proposed in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than a Genetic Algorithm (GA) and DE. The results of IDE from this experiment show estimated optimal kinetic parameters values, shorter computation time and better accuracy of simulated results compared to the other estimation algorithms.",

keywords = "Differential Evolution Algorithm, Kalman Filter, Parameter Estimation, Simulation",

author = "Chong, {Chuii Khim} and Mohamad, {Mohd Saberi} and Safaai Deris and Shahir Shamsir and Afnizanfaizal Abdullah and Choon, {Yee Wen} and Chai, {Lian En} and Sigeru Omatu",

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AU - Mohamad, Mohd Saberi

AU - Deris, Safaai

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AU - Abdullah, Afnizanfaizal

AU - Choon, Yee Wen

AU - Chai, Lian En

AU - Omatu, Sigeru

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AB - This paper presents an improved Differential Evolution algorithm (IDE). It is aimed at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Nonetheless, due to the noisy data and difficulty of the system in estimating myriad of parameters, many computation algorithms face obstacles and require longer computational time to estimate the relevant parameters. The IDE proposed in this paper is a hybrid of a Differential Evolution algorithm (DE) and a Kalman Filter (KF). The outcome of IDE is proven to be superior than a Genetic Algorithm (GA) and DE. The results of IDE from this experiment show estimated optimal kinetic parameters values, shorter computation time and better accuracy of simulated results compared to the other estimation algorithms.

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Using an improved differential evolution algorithm for parameter estimation to simulate glycolysis pathway

Abstract

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Keywords

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