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Delineating parameter unidentifiabilities in complex models.

Dhruva V Raman1, James Anderson1, Antonis Papachristodoulou1

  • 1Department of Engineering Science, University of Oxford, 17 Parks Road, OX1 3PJ Oxford, United Kingdom.

Physical Review. E
|April 19, 2017
PubMed
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Scientists developed a new method to detect parameter unidentifiabilities in complex mathematical models. This approach enables model simplification and identifies which parameters cannot be reliably estimated from data.

Area of Science:

  • Computational Science
  • Systems Biology
  • Mathematical Modeling

Background:

  • Complex mathematical models often contain parameter relationships leading to unidentifiability.
  • Structural and practical unidentifiabilities hinder reliable parameter estimation from data.
  • These issues imply potential for underlying model simplification.

Purpose of the Study:

  • To introduce a scalable method for detecting unidentifiabilities and their defining functional relations in generic models.
  • To enable model simplification and identify parameters not estimable from data.
  • To uncover features like redundant mechanisms and fast subsystems.

Main Methods:

  • Developed a scalable algorithm based on 'multiscale sloppiness', a quantification of regional parametric sensitivity.

Related Experiment Videos

  • Demonstrated duality between multiscale sloppiness and confidence region geometry for non-negligible measurement uncertainty.
  • Established theoretical links between multiscale sloppiness and the likelihood-ratio test.

    • Main Results:

    • The algorithm detects unidentifiabilities, functional relations, and applicable parameter regimes.
    • Multiscale sloppiness provides a tractable alternative to traditional local sensitivity analysis for parameter estimation reliability.
    • Applied the method to a large-scale model of tumor necrosis factor (NF)-κB signaling, identifying unidentifiabilities.

    Conclusions:

    • The novel 'multiscale sloppiness' approach offers a scalable solution for identifying and understanding parameter unidentifiabilities in complex models.
    • This method facilitates model simplification and clarifies limitations in parameter estimation from experimental data.
    • The findings challenge the sufficiency of traditional local sensitivity analyses for assessing parameter estimation reliability.