ArticlePLoS computational biology2017
Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Mathematical Model-Derived Disposition Index Without Insulin Validated in Youth With Obesity.The Journal of clinical endocrinology and metabolism · 2025Observational
- Ideal adaptive control in biological systems: an analysis ofBMC bioinformatics · 2024Article
- Distilling identifiable and interpretable dynamic models from biological data.PLoS computational biology · 2023Article
- Structural identifiability of biomolecular controller motifs with and without flow measurements as model output.PLoS computational biology · 2023Article
- IDENTIFIABILITY OF INFECTION MODEL PARAMETERS EARLY IN AN EPIDEMIC.SIAM journal on control and optimization · 2022Article
- Article
- Computational modeling of stem and progenitor cell kinetics identifies plausible hematopoietic lineage hierarchies.iScience · 2021Article
- Multiplexing information flow through dynamic signalling systems.PLoS computational biology · 2020Article
- Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models.Journal of the Royal Society, Interface · 2019Article
- Parameter estimation and identifiability in a neural population model for electro-cortical activity.PLoS computational biology · 2019Article
- Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts.In silico biology · 2019Article
- Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.PLoS computational biology · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.
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