More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability
This work addresses efficiency issues in structural identifiability analysis for researchers in systems biology and control theory, but it is incremental as it builds on existing methods.
The paper tackles the problem of verifying global parameter identifiability in ODE models by reducing non-identifiable parameters, resulting in significant performance improvements across computer algebra frameworks.
Structural global parameter identifiability indicates whether one can determine a parameter's value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be infinitely many values, such parameters are called non-identifiable. We present a procedure for accelerating a global identifiability query by eliminating algebraically independent non-identifiable parameters. Our proposed approach significantly improves performance across different computer algebra frameworks.