Computing all identifiable functions of parameters for ODE models

Parameter identifiability is a structural property of an ODE model for recovering the values parameters from data (i.e., input and output variables). This prerequisite meaningful parameter identification in practice. In presence nonidentifiability, it important to find all functions that are identifiable. The existing algorithms check whether given function identifiable or, under solvability condition, functions. However, this condition not always satisfied, which presents challenge. Our first main result algorithm computes without any additional assumptions, such as far we know. second concerns multiple experiments (with generically different inputs initial conditions among experiments). For problem, prove set what would actually be computed by input–output equation-based (whether or fulfilled), was known before. We give only finds these but also provides upper bound number performed identify provide implementation presented algorithms.