🤖 AI Summary
This work addresses the structural identifiability of ordinary differential equation (ODE)-based mechanistic models—specifically, whether model parameters can be uniquely determined from ideal observational data—and proposes a unified symbolic analysis framework implemented in Julia. Built upon the StructuralIdentifiability.jl package, the framework integrates symbolic computation with parameter-output mapping analysis to support assessments of local and global identifiability, observability, and extraction of identifiable parameter combinations. As the first fully reproducible tutorial within the SciML ecosystem, it not only enables model reparameterization and informs experimental design but also demonstrates its efficacy across seven representative case studies spanning epidemiology, pharmacokinetics, and other domains, thereby offering both a practical workflow and theoretical foundation for modeling complex dynamical systems.
📝 Abstract
Structural identifiability analysis determines whether the parameters of a mechanistic ordinary differential equation (ODE) model can be uniquely recovered from ideal observations and is therefore a fundamental prerequisite for reliable parameter estimation. This tutorial presents a modern, reproducible computational framework for symbolic structural identifiability analysis using the Julia package StructuralIdentifiability.jl. We provide a rigorous yet accessible introduction to local and global identifiability, observability, parameter-to-output mappings, and identifiable parameter combinations, together with a unified workflow based on the core functions @ODEmodel, assess_local_identifiability, assess_identifiability, and find_identifiable_functions. The framework is demonstrated through seven case studies from epidemiology, pharmacokinetics, and systems biology, illustrating globally identifiable systems, local-only identifiability, structural non-identifiability, and recovery of identifiability through additional measurements and reparameterization. Beyond the theoretical foundations, the tutorial emphasizes practical model reformulation, experimental design, and reproducible scientific workflows within the Julia SciML ecosystem, providing a comprehensive reference for researchers and graduate students working with mechanistic ODE models.