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Parameter identifiability of linear-compartmental mammillary models
Katherine Clemens1,2, Jonathan Martinez3, Anne Shiu4
1Bryn Mawr College, Bryn Mawr, PA, USA.
Bulletin of Mathematical Biology
|December 24, 2025
Summary
This study analyzes parameter identifiability in linear compartmental models, specifically mammillary models. It distinguishes between local and global identifiability for individual parameters, providing formulas for globally identifiable ones.
Area of Science:
- Systems biology
- Mathematical modeling
- Pharmacokinetics
Background:
- Linear compartmental models are crucial for analyzing biological and medical systems.
- Parameter identifiability is essential for reliable model interpretation from experimental data.
- Previous work characterized structural identifiability for bidirected tree models.
Purpose of the Study:
- To differentiate between local and global identifiability for individual parameters in mammillary models.
- To identify which parameters in mammillary models are locally versus globally identifiable.
- To derive formulas for globally identifiable parameters.
Main Methods:
- Analysis of mammillary models, a specific class of linear compartmental models.
- Application of combinatorial formulas for input-output coefficients.
- Distinction between local and global identifiability criteria.
Main Results:
- Identifiability of individual parameters (local vs. global) was determined for five infinite families of mammillary models.
- Formulas were derived for certain globally identifiable parameters based on input-output equations.
- The study builds upon prior characterizations of structural identifiability.
Conclusions:
- The study provides a detailed understanding of parameter identifiability in mammillary models.
- This work advances the analysis of complex biological systems using compartmental modeling.
- The findings are crucial for accurate data interpretation in fields like pharmacokinetics.
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