Parameter and structural identifiability concepts and ambiguities: a critical review and analysis

Area of Science:

• Systems biology
• Mathematical modeling
• Pharmacokinetics

Background:

• Identifiability analysis is essential for parameter estimation in dynamic systems.
• Unique solutions for model parameters are sought from stimulus-response experiments.
• Applications in biology highlight the need for robust modeling.

Purpose of the Study:

• To review, analyze, and unify the concept of model identifiability.
• To emphasize the importance of identifiability analysis in experiment design.
• To examine the impact of identifiability on model predictions.

Main Methods:

• Review and comparison of identifiability testing methods.
• Analysis of algebraic difficulties and potential pitfalls.
• Examination of physiological system models.

Main Results:

• Identifiability analysis is critical before parameter estimation.
• Ambiguities can arise even in simple models.
• Model prediction accuracy depends on identifiability properties.

Conclusions:

• Identifiability analysis is fundamental for valid parameter estimation and reliable model predictions.
• Understanding identifiability prevents ambiguous outcomes in biological system modeling.
• This analysis is vital for effective experimental design in systems biology.