Journal Article

An optimal experimental design approach to model discrimination in dynamic biochemical systems

Dominik Skanda and Dirk Lebiedz

in Bioinformatics

Volume 26, issue 7, pages 939-945
Published in print April 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq074
An optimal experimental design approach to model discrimination in dynamic biochemical systems

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Motivation: Finding suitable models of dynamic biochemical systems is an important task in systems biology approaches to the biosciences. On the one hand, a correct model helps to understand the underlying mechanisms and on the other hand, one can use the model to predict the behavior of a biological system under various circumstances. Typically, before the correct model of a biochemical system is found, different hypothetical models might be reasonable and consistent with previous knowledge and available data. The main goal now is to find the best suited model out of different hypotheses. The process of falsifying inappropriate candidate models is called model discrimination.

Results: We have developed a new computational tool to compute optimal experiments for biochemical kinetic systems with underlying ordinary differential equation (ODE) models for the purpose of model discrimination. We were inspired by the demands of biological experimentalists which perform one run measurement where perturbations to the system are possible. We provide a criterion which calculates the number and location of time points of optimal measurements as well as optimal initial conditions and optimal perturbations to the system.

Availability: The model discrimination algorithm described here is implemented in C++ in the package ModelDiscriminationToolkit. The source code can be downloaded from http://omnibus.unifreiburg.de/~ds500/_software.html

Contact: dirk.lebiedz@biologie.uni-freiburg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  3763 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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