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International Workshop on Hybrid Systems Biology

HSB 2015: Hybrid Systems Biology pp 20–34Cite as

Comparative Statistical Analysis of Qualitative Parametrization Sets

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9271))

Abstract

The problem of model parametrization is a core issue for all varieties of mathematical modelling in biology. This problem becomes more tractable when qualitative modelling is used, since the range of parameter values is finite and consequently it is possible to enumerate and evaluate all possible parametrizations of a model. If such an approach is undertaken, one usually obtains a vast set of parametrizations that are scored for various properties, e.g. fitness. The usual next step is to take the best scoring parametrization. However, as noted in recent works [1, 4], there is knowledge to be gained from examining sets of parametrizations based on their scoring. In this article we extend this line of thought and introduce a comprehensive workflow for comparing such sets and obtaining knowledge from the comparison.

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Author information

Authors and Affiliations

  1. Freie Universität Berlin, Berlin, Germany

    Adam Streck, Kirsten Thobe & Heike Siebert

Authors
  1. Adam Streck
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  2. Kirsten Thobe
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  3. Heike Siebert
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Corresponding author

Correspondence to Adam Streck .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, United Kingdom

    Alessandro Abate

  2. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    David Šafránek

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Streck, A., Thobe, K., Siebert, H. (2015). Comparative Statistical Analysis of Qualitative Parametrization Sets. In: Abate, A., Šafránek, D. (eds) Hybrid Systems Biology. HSB 2015. Lecture Notes in Computer Science(), vol 9271. Springer, Cham. https://doi.org/10.1007/978-3-319-26916-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-26916-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26915-3

  • Online ISBN: 978-3-319-26916-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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