Cover Image

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set...

Full description

Saved in:
Bibliographic Details
Main Authors: Ismail, A. M., Mohamad, M. S., Abdul Majid, H., Abas, K. H., Deris, S., Zaki, N., Mohd. Hashim, S. Z., Ibrahim, Z., Remli, M. A.
Format: Article
Published: Elsevier Ireland Ltd 2017
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/75447/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030723135&doi=10.1016%2fj.biosystems.2017.09.013&partnerID=40&md5=454a49d90a2a3ca1a45d10ccc43fce7f
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.75447
record_format eprints
spelling my.utm.754472018-03-22T11:11:07Z http://eprints.utm.my/id/eprint/75447/ An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways Ismail, A. M. Mohamad, M. S. Abdul Majid, H. Abas, K. H. Deris, S. Zaki, N. Mohd. Hashim, S. Z. Ibrahim, Z. Remli, M. A. QA Mathematics Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions. Elsevier Ireland Ltd 2017 Article PeerReviewed Ismail, A. M. and Mohamad, M. S. and Abdul Majid, H. and Abas, K. H. and Deris, S. and Zaki, N. and Mohd. Hashim, S. Z. and Ibrahim, Z. and Remli, M. A. (2017) An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways. BioSystems, 162 . pp. 81-89. ISSN 0303-2647 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030723135&doi=10.1016%2fj.biosystems.2017.09.013&partnerID=40&md5=454a49d90a2a3ca1a45d10ccc43fce7f
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 QA Mathematics
spellingShingle QA Mathematics
Ismail, A. M.
Mohamad, M. S.
Abdul Majid, H.
Abas, K. H.
Deris, S.
Zaki, N.
Mohd. Hashim, S. Z.
Ibrahim, Z.
Remli, M. A.
An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
description Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
format Article
author Ismail, A. M.
Mohamad, M. S.
Abdul Majid, H.
Abas, K. H.
Deris, S.
Zaki, N.
Mohd. Hashim, S. Z.
Ibrahim, Z.
Remli, M. A.
author_facet Ismail, A. M.
Mohamad, M. S.
Abdul Majid, H.
Abas, K. H.
Deris, S.
Zaki, N.
Mohd. Hashim, S. Z.
Ibrahim, Z.
Remli, M. A.
author_sort Ismail, A. M.
title An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
title_short An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
title_full An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
title_fullStr An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
title_full_unstemmed An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
title_sort improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
publisher Elsevier Ireland Ltd
publishDate 2017
url http://eprints.utm.my/id/eprint/75447/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030723135&doi=10.1016%2fj.biosystems.2017.09.013&partnerID=40&md5=454a49d90a2a3ca1a45d10ccc43fce7f
_version_ 1643657067008884736
score 13.211869

Similar Items