On testing structural identifiability by a simple scaling method: Relying on scaling symmetries can be misleading.

Alejandro F Villaverde, Gemma Massonis
Author Information
  1. Alejandro F Villaverde: Universidade de Vigo, Department of Systems Engineering and Control, Vigo, Galicia, Spain. ORCID
  2. Gemma Massonis: Universidade de Vigo, Department of Applied Mathematics II, Vigo, Galicia, Spain. ORCID

Abstract

A recent paper published in PLOS Computational Biology [1] introduces the Scaling Invariance Method (SIM) for analysing structural local identifiability and observability. These two properties define mathematically the possibility of determining the values of the parameters (identifiability) and states (observability) of a dynamic model by observing its output. In this note we warn that SIM considers scaling symmetries as the only possible cause of non-identifiability and non-observability. We show that other types of symmetries can cause the same problems without being detected by SIM, and that in those cases the method may lead one to conclude that the model is identifiable and observable when it is actually not.

References

  1. Bioinformatics. 2018 Apr 15;34(8):1421-1423 [PMID: 29206901]
  2. PLoS Comput Biol. 2020 Nov 3;16(11):e1008248 [PMID: 33141821]
  3. Bioinformatics. 2019 Aug 15;35(16):2873-2874 [PMID: 30601937]
  4. Math Biosci. 2012 Sep;239(1):139-53 [PMID: 22609467]
  5. Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Jul;92(1):012920 [PMID: 26274260]
  6. PLoS One. 2014 Oct 28;9(10):e110261 [PMID: 25350289]
  7. J R Soc Interface. 2019 Jul 26;16(156):20190043 [PMID: 31266417]
  8. Math Biosci. 1989 Apr;93(2):217-48 [PMID: 2520030]

MeSH Term

Computational Biology
Models, Theoretical

Word Cloud

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