ArticlePLoS computational biology2020
Testing structural identifiability by a simple scaling method.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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24 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
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- Classical structural identifiability methodology applied to low-dimensional dynamic systems in receptor theory.Journal of pharmacokinetics and pharmacodynamics · 2024Article
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- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
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- Sloppiness: Fundamental study, new formalism and its application in model assessment.PloS one · 2023Article
- The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission.Viruses · 2022Article
- Structural identifiability of compartmental models for infectious disease transmission is influenced by data type.Epidemics · 2022Article
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- Quantification of Type I Interferon Inhibition by Viral Proteins: Ebola Virus as a Case Study.Viruses · 2021Article
- The limitations, dangers, and benefits of simple methods for testing identifiability.PLoS computational biology · 2021Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Successful mathematical modeling of biological processes relies on the expertise of the modeler to capture the essential mechanisms in the process at hand and on the ability to extract useful information from empirical data. A model is said to be structurally unidentifiable, if different quantitative sets of parameters provide the same observable outcome. This is typical (but not exclusive) of partially observed problems in which only a few variables can be experimentally measured. Most of the available methods to test the structural identifiability of a model are either too complex mathematically for the general practitioner to be applied, or require involved calculations or numerical computation for complex non-linear models. In this work, we present a new analytical method to test structural identifiability of models based on ordinary differential equations, based on the invariance of the equations under the scaling transformation of its parameters. The method is based on rigorous mathematical results but it is easy and quick to apply, even to test the identifiability of sophisticated highly non-linear models. We illustrate our method by example and compare its performance with other existing methods in the literature.
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