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The authors evaluate practical parameter identifiability in a coupled, non-linear neuroendocrine-inflammatory dynamical model across six experimentally feasible observation operators with varying measurement noise. Uniquely recovering parameters in complex physiological systems is critical for scientific validity, yet conventional diagnostics frequently yield conflicting conclusions. The benchmark reveals that Fisher Information Matrix methods consistently overestimate parameter identifiability compared to profile-likelihood analysis, demonstrating that profile-likelihood is indispensable for avoiding false confidence despite its computational cost.
Standard Fisher information metrics can severely overestimate how well a model is specified, masking non-identifiable parameters that only rigorous profile-likelihood sweeps reliably catch.
Mathematical modeling helps us identify and investigate complex mechanisms of function. This is especially useful for understanding inflammation, which is a complex, multiscale process that interacts nonlinearly with other physiological systems. The full potential of these models can only be realized when combined with experimental data through parameter estimation. However, uniquely determining model parameters requires that they are practically identifiable, which can be assessed by multiple, possibly inconsistent, methods. Identifiability can also vary with experimental designs and observation operators. This study investigates this issue by assessing parameter identifiability in a coupled model of the neuroendocrine-inflammatory system. We compare three workflows following a global sensitivity analysis: Fisher-information matrix based identifiability, global optimization with frequentist confidence intervals, and the profile-likelihood. We compare results across six observation operators with and without measurement noise, all of which are experimentally feasible given prior literature. Our results show that Fisher-information-based methods typically lead to larger sets of identifiable parameters while profile-likelihood analyses lead to a smaller number of identifiable parameters. This suggests that, while computationally expensive, the profile-likelihood is necessary for accurately assessing identifiability, and must be recalculated when the experimental design is altered.