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

Author

Listed:
  • Tim Maiwald
  • Helge Hass
  • Bernhard Steiert
  • Joep Vanlier
  • Raphael Engesser
  • Andreas Raue
  • Friederike Kipkeew
  • Hans H Bock
  • Daniel Kaschek
  • Clemens Kreutz
  • Jens Timmer

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.

Suggested Citation

  • Tim Maiwald & Helge Hass & Bernhard Steiert & Joep Vanlier & Raphael Engesser & Andreas Raue & Friederike Kipkeew & Hans H Bock & Daniel Kaschek & Clemens Kreutz & Jens Timmer, 2016. "Driving the Model to Its Limit: Profile Likelihood Based Model Reduction," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-18, September.
  • Handle: RePEc:plo:pone00:0162366
    DOI: 10.1371/journal.pone.0162366
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    Cited by:

    1. Philip J. Schmidt & Monica B. Emelko & Mary E. Thompson, 2020. "Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit," Risk Analysis, John Wiley & Sons, vol. 40(2), pages 352-369, February.
    2. Sanjana Gupta & Robin E C Lee & James R Faeder, 2020. "Parallel Tempering with Lasso for model reduction in systems biology," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-22, March.
    3. Shoya Iwanami & Kosaku Kitagawa & Hirofumi Ohashi & Yusuke Asai & Kaho Shionoya & Wakana Saso & Kazane Nishioka & Hisashi Inaba & Shinji Nakaoka & Takaji Wakita & Odo Diekmann & Shingo Iwami & Koichi , 2020. "Should a viral genome stay in the host cell or leave? A quantitative dynamics study of how hepatitis C virus deals with this dilemma," PLOS Biology, Public Library of Science, vol. 18(7), pages 1-17, July.
    4. Mario Castro & Rob J de Boer, 2020. "Testing structural identifiability by a simple scaling method," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-15, November.

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