A kernel-based method to determine optimal sampling times for the simultaneous estimation of the parameters of rival mathematical models.

Full Abstract

When several models are proposed for one and the same process, experimental design techniques are available to design optimal discriminatory experiments. However, because the experimental design techniques are model-based, it is important that the required model predictions are not too uncertain. This uncertainty is determined by the quality of the already available data, since low-quality data will result in poorly estimated parameters, which on their turn result in uncertain model predictions. Therefore, model discrimination may become more efficient and effective if this uncertainty is reduced first. This can be achieved by performing dedicated experiments, designed to increase the accuracy of the parameter estimates. However, performing such an additional experiment for each rival model may undermine the overall goal of optimal experimental design, which is to minimize the experimental effort. In this article, a kernel-based method is presented to determine optimal sampling times to simultaneously estimate the parameters of rival models in a single experiment. The method is applied in a case study where nine rival models are defined to describe the kinetics of an enzymatic reaction (glucokinase). The results clearly show that the presented method performs well, and that a compromise experiment is found which is sufficiently informative to improve the overall accuracy of the parameters of all rival models, thus allowing subsequent design of an optimal discriminatory experiment. Copyright 2009 Wiley Periodicals, Inc.

Author information

Author/s: Donckels, Brecht M R (BM); De Pauw, Dirk J W (DJ); Vanrolleghem, Peter A (PA); De Baets, Bernard (B);

Affiliation: BIOMATH, Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, B-9000 Ghent, Belgium. brecht.donckels(-atsign-)biomath.ugent.be

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Journal of computational chemistry (J Comput Chem), published in United States. (Language: eng)

Reference: 2009-Oct; vol 30 (issue 13) : pp 2064-77

Dates: Created 2009/08/03; Completed 2009/10/09;

PMID: 19165773, status: MEDLINE (last retrieval date: 10/9/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article
(including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.