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Parameter estimation: local identifiability of parameters.

J A Jacquez1, T Perry

  • 1Department of Physiology, University of Michigan, Ann Arbor 48109-0622.

The American Journal of Physiology
|April 1, 1990
PubMed
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Researchers developed a method to identify unestimable parameters in biological systems when not all variables can be measured. This approach checks parameter identifiability for given experimental models.

Area of Science:

  • Systems biology
  • Mathematical modeling
  • Computational biology

Background:

  • Biological systems analysis often faces limitations in measuring all state variables.
  • Incomplete measurements can lead to unidentifiable parameters, hindering accurate system modeling and estimation.
  • Unidentifiable parameters cannot be uniquely determined from experimental data.

Purpose of the Study:

  • To develop a method for assessing parameter identifiability in biological models.
  • To provide a computational tool for checking local identifiability of model parameters given experimental constraints.
  • To address the challenge of unestimable parameters in systems with incomplete state variable measurements.

Main Methods:

  • Utilized a theoretical framework to analyze parameter identifiability.

Related Experiment Videos

  • Developed a computational program, IDENT, to check local identifiability.
  • Applied the method to linear and nonlinear system models with initial parameter estimates.

    • Main Results:

    • Demonstrated a simple theory for checking local parameter identifiability.
    • The IDENT program provides a practical means to assess parameter estimability.
    • Identified conditions under which parameters remain unidentifiable due to experimental limitations.

    Conclusions:

    • The developed theory and IDENT program offer a solution for evaluating parameter identifiability in biological systems.
    • This work aids in understanding model limitations and experimental design for better parameter estimation.
    • Facilitates more reliable modeling of biological systems with partial observability.