Profile likelihood in systems biology

Authors

  • Clemens Kreutz,

    Corresponding author
    1. Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Germany
    • Physics Department, University of Freiburg, Germany
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  • Andreas Raue,

    1. Physics Department, University of Freiburg, Germany
    2. Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Neuherberg, Germany
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  • Daniel Kaschek,

    1. Physics Department, University of Freiburg, Germany
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  • Jens Timmer

    1. Physics Department, University of Freiburg, Germany
    2. Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Germany
    3. Freiburg Center for Biosystems Analysis, University of Freiburg, Germany
    4. BIOSS Centre for Biological Signalling Studies, University of Freiburg, Germany
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Correspondence

C. Kreutz, Physics Department, University of Freiburg, Hermann-Herder Str. 3, 79104 Freiburg, Germany

Fax: +49 761 203 5754

Tel: +49 761 203 8533

E-mail: ckreutz@fdm.uni-freiburg.de

Abstract

Inferring knowledge about biological processes by a mathematical description is a major characteristic of Systems Biology. To understand and predict system's behavior the available experimental information is translated into a mathematical model. Since the availability of experimental data is often limited and measurements contain noise, it is essential to appropriately translate experimental uncertainty to model parameters as well as to model predictions. This is especially important in Systems Biology because typically large and complex models are applied and therefore the limited experimental knowledge might yield weakly specified model components. Likelihood profiles have been recently suggested and applied in the Systems Biology for assessing parameter and prediction uncertainty. In this article, the profile likelihood concept is reviewed and the potential of the approach is demonstrated for a model of the erythropoietin (EPO) receptor.

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