An improved swarm optimization for parameter estimation and biological model selection.

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incor...

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Autores principales:	Afnizanfaizal Abdullah, Safaai Deris, Mohd Saberi Mohamad, Sohail Anwar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/de9f0fbcefc84d808b9b0493e0153a3f
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spelling	oai:doaj.org-article:de9f0fbcefc84d808b9b0493e0153a3f2021-11-18T07:49:44ZAn improved swarm optimization for parameter estimation and biological model selection.1932-620310.1371/journal.pone.0061258https://doaj.org/article/de9f0fbcefc84d808b9b0493e0153a3f2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23593445/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.Afnizanfaizal AbdullahSafaai DerisMohd Saberi MohamadSohail AnwarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e61258 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Afnizanfaizal Abdullah Safaai Deris Mohd Saberi Mohamad Sohail Anwar An improved swarm optimization for parameter estimation and biological model selection.
description	One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
format	article
author	Afnizanfaizal Abdullah Safaai Deris Mohd Saberi Mohamad Sohail Anwar
author_facet	Afnizanfaizal Abdullah Safaai Deris Mohd Saberi Mohamad Sohail Anwar
author_sort	Afnizanfaizal Abdullah
title	An improved swarm optimization for parameter estimation and biological model selection.
title_short	An improved swarm optimization for parameter estimation and biological model selection.
title_full	An improved swarm optimization for parameter estimation and biological model selection.
title_fullStr	An improved swarm optimization for parameter estimation and biological model selection.
title_full_unstemmed	An improved swarm optimization for parameter estimation and biological model selection.
title_sort	improved swarm optimization for parameter estimation and biological model selection.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/de9f0fbcefc84d808b9b0493e0153a3f
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An improved swarm optimization for parameter estimation and biological model selection.

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