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A new method for a priori practical identifiability.

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New methods relax derivative assumptions in structural identifiability analysis for biomedical models. This brings model parameter estimation closer to practical applications by reducing overestimations of identifiable parameters.

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Area of Science:

  • Biomedical modeling
  • Systems biology
  • Mathematical biology

Background:

  • Practical identifiability analysis is crucial for determining model properties from data in biomedicine.
  • Current methods often require exhaustive parameter space coverage, which is infeasible.
  • Existing structural identifiability methods assume availability of all higher-order derivatives, limiting their practical use.

Purpose of the Study:

  • To develop new definitions and methods for structural identifiability analysis that relax the assumption of requiring all higher-order derivatives.
  • To introduce a novel concept of [Formula: see text]-identifiability, considering only the first [Formula: see text] derivatives.
  • To enable the assessment of identifiability for any model property, including parameters and observability.

Main Methods:

  • Introduced the concept of [Formula: see text]-identifiability, based on a limited number of derivatives.
  • Developed new algorithms to determine this novel type of identifiability.
  • Applied the methods to various published biomedical models, including Drosophila and NF-[Formula: see text]B models.

Main Results:

  • Demonstrated the applicability of the new algorithms to assess identifiability in biomedical models.
  • Quantified the impact of derivative availability assumptions, showing significant drops in identifiable parameters when fewer derivatives are assumed.
  • Showcased that previously obtained identifiability often requires at least 20 derivatives.

Conclusions:

  • The assumption of available derivatives in traditional structural identifiability analysis leads to overestimation of estimable parameters.
  • The developed methods and algorithms relax this assumption, advancing structural identifiability towards practical applications.
  • This work bridges the gap between theoretical structural identifiability and practical identifiability analysis in biomedical modeling.