ArticlePLoS computational biology2021
On testing structural identifiability by a simple scaling method: Relying on scaling symmetries can be misleading.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantitative Systems Pharmacology Models of Anti-Amyloid Treatments for Alzheimer's Disease: A Systematic Review.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- The limitations, dangers, and benefits of simple methods for testing identifiability.PLoS computational biology · 2021Article
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2 authors.
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No grant is acknowledged in the PubMed record.
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.
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