The limitations, dangers, and benefits of simple methods for testing identifiability

Mario Castro*, Rob J. de Boer

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorAcademicpeer-review

Abstract

In their Commentary paper, Villaverde and Massonis (On testing structural identifiability by a simple scaling method: relying on scaling symmetries can be misleading) have commented on our paper in which we proposed a simple scaling method to test structural identifiability. Our scaling invariance method (SIM) tests for scaling symmetries only, and Villaverde and Massonis correctly show the SIM may fail to detect identifiability problems when a model has other types of symmetries. We agree with the limitations raised by these authors but, also, we emphasize that the method is still valuable for its applicability to a wide variety of models, its simplicity, and even as a tool to introduce the problem of identifiability to investigators with little training in mathematics.

Original languageEnglish
Article numbere1009425
Pages (from-to)1-2
JournalPLoS Computational Biology
Volume17
Issue number10
DOIs
Publication statusPublished - 14 Oct 2021

Bibliographical note

Funding Information:
Funding: This work was funded by Agencia estatal de investigaci?n (AEI) and Fondo Europeo de Desarrollo Regional (Grant Number: PID2019-106339GB-I00) awarded to MC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
Copyright: © 2021 Castro, de Boer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding

Funding: This work was funded by Agencia estatal de investigaci?n (AEI) and Fondo Europeo de Desarrollo Regional (Grant Number: PID2019-106339GB-I00) awarded to MC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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