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Identifiability in tracer experiments

C Cobelli, A Lepschy, G R Jacur

    Federation Proceedings
    |January 1, 1980
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a method to determine if tracer experiments can identify all parameters in compartmental systems. The approach uses compartmental diagrams for a priori identifiability testing.

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    Area of Science:

    • Systems Biology
    • Pharmacokinetics
    • Control Theory

    Background:

    • Compartmental models are widely used to represent biological and chemical processes.
    • Tracer experiments are crucial for parameter estimation in these systems.
    • Assessing the feasibility of parameter estimation a priori is essential for experimental design.

    Purpose of the Study:

    • To develop a method for assessing the structural identifiability of compartmental systems using tracer experiment data.
    • To provide a priori criteria for determining if all model parameters can be uniquely estimated.
    • To simplify the identifiability analysis by focusing on the compartmental diagram.

    Main Methods:

    • A formal definition of structural identifiability based on system theory concepts was established.

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  • A novel identifiability test was developed, focusing on necessary conditions.
  • The test allows for analysis directly from the compartmental diagram, bypassing complex symbolic manipulations.

    • Main Results:

    • A practical, diagram-based method for structural identifiability testing was presented.
    • Necessary conditions for identifiability were derived and demonstrated.
    • The method's applicability was validated through three examples, including a pharmacokinetic model.

    Conclusions:

    • The proposed method offers an efficient way to assess the structural identifiability of compartmental systems before conducting experiments.
    • Diagram-based analysis simplifies the identifiability problem, making it more accessible.
    • This work aids in the optimal design of tracer experiments for accurate parameter estimation.