Parameter estimation of microbial models using hybrid optimization methods

Development of biological models is essential as it represents and predicts complex processes within microbial cells. These models are formed by mathematical formulations that depend heavily on a set of parameters whose accuracy is often influenced by noisy and incomplete experimental data. This stu...

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Main Author:	Abdullah, Afnizanfaizal
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/35875/1/AfnizanfaizalAbdullahPFC2013.pdf
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id	my-utm-ep.35875
record_format	uketd_dc
spelling	my-utm-ep.358752017-08-02T03:57:26Z Parameter estimation of microbial models using hybrid optimization methods 2013-06 Abdullah, Afnizanfaizal QA Mathematics Development of biological models is essential as it represents and predicts complex processes within microbial cells. These models are formed by mathematical formulations that depend heavily on a set of parameters whose accuracy is often influenced by noisy and incomplete experimental data. This study is aimed to design and develop new optimization methods that can effectively estimate these parameters by iteratively fitting the model outputs to the experimental data. To achieve this goal, two new hybrid optimization methods based on the Firefly Algorithm (FA) method are proposed. Firstly, a method using evolutionary operations from Differential Evolution (DE) method was developed to improve the estimation accuracy of the parameters. Then, a second method using Chemical Reaction Optimization (CRO) method was proposed to surmount the convergence speed problem during parameter estimation. The effectiveness of the proposed methods was evaluated using synthetic transcriptional oscillator and extracellular protease production models. Computational experiments showed that these methods were able to estimate plausible parameters which produced model outputs that closely fitted in the experimental data. Statistical validation confirmed that these methods are competent at estimating the identifiable parameters. These findings are crucial to ensure that the estimated parameters can generate predictive and sensitive model outputs. In conclusion, this study has presented new hybrid optimization methods, capable of estimating the model parameters effectively whilst taking into account noisy and incomplete experimental data. 2013-06 Thesis http://eprints.utm.my/id/eprint/35875/ http://eprints.utm.my/id/eprint/35875/1/AfnizanfaizalAbdullahPFC2013.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:70732?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Abdullah, Afnizanfaizal Parameter estimation of microbial models using hybrid optimization methods
description	Development of biological models is essential as it represents and predicts complex processes within microbial cells. These models are formed by mathematical formulations that depend heavily on a set of parameters whose accuracy is often influenced by noisy and incomplete experimental data. This study is aimed to design and develop new optimization methods that can effectively estimate these parameters by iteratively fitting the model outputs to the experimental data. To achieve this goal, two new hybrid optimization methods based on the Firefly Algorithm (FA) method are proposed. Firstly, a method using evolutionary operations from Differential Evolution (DE) method was developed to improve the estimation accuracy of the parameters. Then, a second method using Chemical Reaction Optimization (CRO) method was proposed to surmount the convergence speed problem during parameter estimation. The effectiveness of the proposed methods was evaluated using synthetic transcriptional oscillator and extracellular protease production models. Computational experiments showed that these methods were able to estimate plausible parameters which produced model outputs that closely fitted in the experimental data. Statistical validation confirmed that these methods are competent at estimating the identifiable parameters. These findings are crucial to ensure that the estimated parameters can generate predictive and sensitive model outputs. In conclusion, this study has presented new hybrid optimization methods, capable of estimating the model parameters effectively whilst taking into account noisy and incomplete experimental data.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abdullah, Afnizanfaizal
author_facet	Abdullah, Afnizanfaizal
author_sort	Abdullah, Afnizanfaizal
title	Parameter estimation of microbial models using hybrid optimization methods
title_short	Parameter estimation of microbial models using hybrid optimization methods
title_full	Parameter estimation of microbial models using hybrid optimization methods
title_fullStr	Parameter estimation of microbial models using hybrid optimization methods
title_full_unstemmed	Parameter estimation of microbial models using hybrid optimization methods
title_sort	parameter estimation of microbial models using hybrid optimization methods
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2013
url	http://eprints.utm.my/id/eprint/35875/1/AfnizanfaizalAbdullahPFC2013.pdf
_version_	1747816377134088192

Parameter estimation of microbial models using hybrid optimization methods

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