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Determining identifiable parameter combinations using subset profiling.

Marisa C Eisenberg1, Michael A L Hayashi2

  • 1Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, United States; Department of Mathematics, University of Michigan, Ann Arbor, United States.

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This study introduces a new method to find identifiable parameter combinations in dynamic systems. This approach aids in model reparameterization and experimental design for better parameter estimation.

Keywords:
Fisher Information MatrixIdentifiabilityMathematical modelingParameter estimationProfile likelihood

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Area of Science:

  • Systems Biology
  • Pharmacokinetics
  • Physiology

Background:

  • Identifiability analysis is crucial for accurate parameter estimation in dynamic system models.
  • Determining identifiable parameter combinations is key for model reparameterization and experimental design.
  • Existing numerical methods for differential equation model identifiability do not fully address identifiable combinations.

Purpose of the Study:

  • To present a novel approach for estimating identifiable parameter combinations.
  • To improve the process of identifiability analysis in dynamic systems.
  • To provide tools for model reparameterization and guide experimental measurements.

Main Methods:

  • Utilizes parameter subset selection methods.
  • Employs the Fisher Information Matrix (FIM).
  • Integrates profile likelihood analysis for robust estimation.

Main Results:

  • Successfully estimates identifiable parameter combinations.
  • Demonstrates the approach's effectiveness on diverse models.
  • Provides a practical method for addressing a key challenge in systems modeling.

Conclusions:

  • The proposed method effectively determines identifiable parameter combinations.
  • This approach enhances the identifiability analysis of dynamic models.
  • Applicable across various scientific domains including pharmacokinetics, cellular biology, and physiology.