RT Journal Article
T1 AutoRepar: a method to obtain identifiable and observable reparameterizations of dynamic models with mechanistic insights
A1 Massonis Feixas, Gemma
A1 Banga, Julio R.
A1 Fernandez Villaverde, Alejandro
K1 3328 Procesos tecnológicos
K1 1207.02 Sistemas de control
K1 3311.02 Ingeniería de control
AB 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.
PB International Journal of Robust and Nonlinear Control
SN 10498923
YR 2023
FD 2023-06
LK http://hdl.handle.net/11093/4174
UL http://hdl.handle.net/11093/4174
LA eng
NO International Journal of Robust and Nonlinear Control,  33(9): 5039-5057 (2023)
NO Consejo Superior de Investigaciones Científicas | Ref. PIE 202070E062
DS Investigo
RD 17-sep-2024