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PLoS One. 2016 Sep 2;11(9):e0162366. doi: 10.1371/journal.pone.0162366. eCollection 2016.

Driving the Model to Its Limit: Profile Likelihood Based Model Reduction.

Author information

1
Institute of Physics, University of Freiburg, Freiburg im Breisgau, Germany.
2
Merrimack Pharmaceuticals, Boston, MA, United States of America.
3
Department of Gastroenterology, Hepatology and Infectiology, University Hospital Duesseldorf, Duesseldorf, Germany.
4
Center for Biosystems Analysis (ZBSA), University of Freiburg, Freiburg im Breisgau, Germany.
5
BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg im Breisgau, Germany.

Abstract

In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to describe the data whereas a model which is too large tends to overfit measurement errors and does not provide precise predictions. Typically, the model is modified and tuned to fit the data, which often results in an oversized model. To restore the balance between model complexity and available measurements, either new data has to be gathered or the model has to be reduced. In this manuscript, we present a data-based method for reducing non-linear models. The profile likelihood is utilised to assess parameter identifiability and designate likely candidates for reduction. Parameter dependencies are analysed along profiles, providing context-dependent suggestions for the type of reduction. We discriminate four distinct scenarios, each associated with a specific model reduction strategy. Iterating the presented procedure eventually results in an identifiable model, which is capable of generating precise and testable predictions. Source code for all toy examples is provided within the freely available, open-source modelling environment Data2Dynamics based on MATLAB available at http://www.data2dynamics.org/, as well as the R packages dMod/cOde available at https://github.com/dkaschek/. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.

PMID:
27588423
PMCID:
PMC5010240
DOI:
10.1371/journal.pone.0162366
[Indexed for MEDLINE]
Free PMC Article

Conflict of interest statement

The authors have declared that no competing interests exist. Author AR received support in the form of salaries from Merrimack Pharmaceuticals. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.

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