Benchmarking tools for a priori identifiability analysis
FECHA:
2023-01-31
IDENTIFICADOR UNIVERSAL: http://hdl.handle.net/11093/4421
VERSIÓN EDITADA: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btad065/7017524
TIPO DE DOCUMENTO: article
RESUMEN
Motivation: The theoretical possibility of determining the state and parameters of a dynamic model by measuring its outputs is given by its structural identifiability and observability. These properties should be analysed before attempting to calibrate a model, but their a priori analysis can be challenging, requiring symbolic calculations that often have a high computational cost. In recent years a number of software tools have been developed for this task, mostly in the systems biology community. These tools have vastly different features and capabilities, and a critical assessment of their performance is still lacking. Results: Here we present a comprehensive study of the computational resources available for analysing structural identifiability. We consider 13 software tools developed in 7 programming languages and evaluate their performance using a set of 25 case studies created from 21 models. Our results reveal their strengths and weaknesses, provide guidelines for choosing the most appropriate tool for a given problem, and highlight opportunities for future developments. Availability: https://github.com/Xabo-RB/Benchmarking_files.