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AutoRepar: A method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights
Author(s) -
Massonis Gemma,
Banga Julio R.,
Villaverde Alejandro F.
Publication year - 2023
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5887
Subject(s) - observable , observability , identifiability , computer science , ode , parameterized complexity , nonlinear system , interpretation (philosophy) , algorithm , mathematics , machine learning , physics , quantum mechanics , programming language
Mechanistic dynamic models of biological systems allow for a quantitative and systematic interpretation of data and the generation of testable hypotheses. However, these models are often over‐parameterized, leading to nonidentifiability and nonobservability, that is, the impossibility of inferring their parameters and state variables. The lack of structural identifiability and observability (SIO) compromises a model's ability to make predictions and provide insight. Here we present a methodology, AutoRepar, that corrects SIO deficiencies of nonlinear ODE models automatically, yielding reparameterized models that are structurally identifiable and observable. The reparameterization preserves the mechanistic meaning of selected variables, and has the exact same dynamics and input‐output mapping as the original model. We implement AutoRepar as an extension of the STRIKE‐GOLDD software toolbox for SIO analysis, applying it to several models from the literature to demonstrate its ability to repair their structural deficiencies. AutoRepar increases the applicability of mechanistic models, enabling them to provide reliable information about their parameters and dynamics.