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Identifiability Results for Several Classes of Linear Compartment Models
Nicolette Meshkat1, Seth Sullivant2, Marisa Eisenberg3
1Department of Mathematics, North Carolina State University, Box 8205, Raleigh, NC, 27695-8205, USA. nmeshkat@scu.edu.
Bulletin of Mathematical Biology
|September 5, 2015
Summary
This study enhances the identifiability of biological models by modifying unidentifiable cycle models. New methods ensure parameter estimation from input-output data for complex biological systems.
Area of Science:
- Systems Biology
- Mathematical Biology
- Pharmacokinetics
Background:
- Identifiability is crucial for parameter estimation in mathematical models.
- Linear compartment models are widely used in pharmacokinetics, physiology, and ecology.
- Previous work identified 'identifiable cycle models' as a class of unidentifiable models.
Purpose of the Study:
- To develop methods for modifying unidentifiable linear compartment models into identifiable ones.
- To establish a constructive approach for combining identifiable models into larger, identifiable systems.
- To apply these theoretical advancements to real-world biological models.
Main Methods:
- Modification of identifiable cycle models by adding inputs, outputs, or removing leaks.
- Development of a constructive theorem for combining identifiable models based on graph theory.
- Application of theoretical results to biological models in physiology, cell biology, and ecology.
Main Results:
- Demonstrated how to transform unidentifiable cycle models into identifiable ones through specific modifications.
- Proved a constructive method for creating larger identifiable models from smaller, strongly connected identifiable models.
- Successfully applied these techniques to diverse biological systems, improving model identifiability.
Conclusions:
- The study provides practical strategies for enhancing model identifiability in biological systems.
- The developed methods offer a pathway to more robust parameter estimation from experimental data.
- This work contributes to the reliable application of mathematical modeling in life sciences.
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