Experiment selection for the discrimination of semi-quantitative models of dynamical systems

  • Authors:

  • Ivayla Vatcheva;Hidde de Jong;Olivier Bernard;Nicolaas J. I. Mars

  • Affiliations:

  • German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;Institut National de Recherche en Informatique et en Automatique (INRIA), Unité de recherche Rhône-Alpes, 655 avenue de l'Europe, Montbonnot, 38334 Saint Ismier Cedex, France;Institut National de Recherche en Informatique et en Automatique (INRIA), Unité de recherche Sophia Antipolis, 2004 route des Lucioles, BP 93, 06902 Sophia Antipolis, France;Materials Science Centre, Department of Mathematics and Natural Sciences, Rijksuniversiteit Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2006

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Abstract

Modeling an experimental system often results in a number of alternative models that are all justified by the available experimental data. To discriminate among these models, additional experiments are needed. Existing methods for the selection of discriminatory experiments in statistics and in artificial intelligence are often based on an entropy criterion, the so-called information increment. A limitation of these methods is that they are not well-adapted to discriminating models of dynamical systems under conditions of limited measurability. Moreover, there are no generic procedures for computing the information increment of an experiment when the models are qualitative or semi-quantitative. This has motivated the development of a method for the selection of experiments to discriminate among semi-quantitative models of dynamical systems. The method has been implemented on top of existing implementations of the qualitative and semi-quantitative simulation techniques QSIM, Q2, and Q3. The applicability of the method to real-world problems is illustrated by means of an example in population biology: the discrimination of four competing models of the growth of phytoplankton in a bioreactor. The models have traditionally been considered equivalent for all practical purposes. Using our model discrimination approach and experimental data we show, however, that two of them are superior for describing phytoplankton growth under a wide range of experimental conditions.