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Identifiability: the first step in parameter estimation.

J A Jacquez

    Federation Proceedings
    |June 1, 1987
    PubMed
    Summary
    This summary is machine-generated.

    Identifiability analysis ensures observational parameters uniquely determine kinetic parameters in models. This crucial step, performed before experiments, validates model structure and parameter estimation accuracy.

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    Area of Science:

    • Systems biology
    • Mathematical modeling
    • Biochemical engineering

    Background:

    • Experimental observations define parameters in dynamic models.
    • Parameter identifiability is key to model reliability.
    • Identifiability analysis should precede experimentation.

    Purpose of the Study:

    • To define and assess the problem of parameter identifiability in dynamic systems.
    • To establish identifiability as a prerequisite for reliable model-based experimentation.

    Main Methods:

    • Defining observational parameters as functions of basic kinetic parameters.
    • Assessing unique specification of kinetic parameters by observational parameters.
    • Utilizing simulated experimental data for local identifiability checks.

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    Main Results:

    • Identifiability depends on the functional relationship between parameter levels.
    • Observation errors and estimation procedures do not affect identifiability.
    • Local identifiability can be confirmed using simulated data and initial parameter estimates.

    Conclusions:

    • Parameter identifiability is a fundamental model property, independent of experimental noise.
    • Pre-experimental identifiability checks are essential for robust systems analysis.
    • Simulations provide a method for preliminary validation of model identifiability.