IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0162366.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162366
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0162366&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0162366?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0162366. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.