International Workshop on Hybrid Systems Biology

Hybrid Systems Biology pp 20-34 | Cite as

Comparative Statistical Analysis of Qualitative Parametrization Sets

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9271)


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.


Qualitative modelling Statistical inference Big data Parameter identification Data mining 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Freie Universität BerlinBerlinGermany

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