Discrimination of Semi-Quantitative Models by Experiment Selection: Method and Application in Population Biology


Ivayla Vatcheva 1 , Olivier Bernard 2 , Hidde de Jong 3 , Jean-Luc Gouzé 2, and Nicolaas J. I. Mars 1

1 Department of Computer Science, University of Twente, P. O. Box 217, 7500 AE Enschede, the Netherlands

2 INRIA Sophia Antipolis, 2004 Route des Lucioles, BP 93, 06902 Sophia Antipolis, France

3 INRIA Rhône-Alpes, 655, avenue de l’Europe, 38330 Montbonnot Saint Martin, France

Email: {ivayla, mars }@cs.utwente.nl, {Olivier.Bernard,Jean-Luc.Gouze }@sophia.inria.fr, Hidde.de-Jong@inrialpes.fr


Abstract: Modeling an experimental system often results in a number of alternative models that are justified equally well by the experimental data. In order to discriminate between these models, additional experiments are needed. We present a method for the discrimination of models in the form of semi-quantitative differential equations. The method is a generalization of previous work in model discrimination. It is based on an entropy criterion for the selection of the most informative experiment which can handle cases where the models predict multiple qualitative behaviors. The applicability of the method is demonstrated on a real-life example, the discrimination of a set of competing models of the growth of phytoplankton in a bioreactor.


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