Comment on "Parameter and structural identifiability concepts and ambiguities: a critical review and analysis"

Area of Science:

• Physiological modeling
• Systems biology
• Mathematical biology

Background:

• The identifiability of mathematical models is crucial for understanding biological systems.
• Previous work by Cobelli and DiStefano explored glucose tolerance test models.
• Model identifiability can be influenced by experimental design and input signals.

Purpose of the Study:

• To analyze the identifiability of a glucose tolerance test model.
• To investigate the influence of external perturbation shape on model identifiability.
• To determine the appropriate analytical approach for multi-input systems.

Main Methods:

• Laplace transform analysis of system observations.
• Examination of model identifiability under varying external perturbations.
• Comparative analysis of transfer functions versus observation Laplace transforms.

Main Results:

• Model identifiability for the glucose tolerance test is contingent upon the specific shape of the applied external perturbation.
• For systems with multiple simultaneous inputs, analyzing the Laplace transform of the overall observation is essential.
• Individual transfer functions are not separately measurable in such multi-input scenarios and are thus insufficient for identifiability analysis.

Conclusions:

• The shape of external perturbations significantly impacts the identifiability of physiological models like the glucose tolerance test.
• Laplace transform analysis of observations provides a more robust method for assessing identifiability in complex, multi-input biological systems.
• Accurate model identifiability relies on appropriate analytical techniques tailored to the system's input structure.