ArticleThe Journal of general physiology2014
Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach.
Article in The Journal of general physiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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Who cites it
89 citing papers in PubMed.
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- Ligand Binding Dynamics of Ion Channels and GPCRs Using Single-Molecule Fluorescence.Annual review of biophysics · 2026Review
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- An inter-subunit path is required for entropically-driven and negatively cooperative binding of cyclic nucleotides in the HCN2 channel.Communications biology · 2026Article
- Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State.bioRxiv : the preprint server for biology · 2026Article
- Bayesian data driven modelling of kinetochore dynamics: Space-time organisation of the human metaphase plate.PLoS computational biology · 2026Article
- Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours.PloS one · 2026Article
- Inference and prediction for stochastic models of biological populations undergoing migration and proliferation.Journal of the Royal Society, Interface · 2025Review
- Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.bioRxiv : the preprint server for biology · 2025Article
- Conformal prediction for uncertainty quantification in dynamic biological systems.PLoS computational biology · 2025Article
- Quantifying cell cycle regulation by tissue crowding.Biophysical journal · 2025Article
- Evaluating the predictive accuracy of ion-channel models using data from multiple experimental designs.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Employing Observability Rank Conditions for Taking into Account Experimental Information a priori.Bulletin of mathematical biology · 2025Article
29 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
Abstract
A major goal of biophysics is to understand the physical mechanisms of biological molecules and systems. Mechanistic models are evaluated based on their ability to explain carefully controlled experiments. By fitting models to data, biophysical parameters that cannot be measured directly can be estimated from experimentation. However, it might be the case that many different combinations of model parameters can explain the observations equally well. In these cases, the model parameters are not identifiable: the experimentation has not provided sufficient constraining power to enable unique estimation of their true values. We demonstrate that this pitfall is present even in simple biophysical models. We investigate the underlying causes of parameter non-identifiability and discuss straightforward methods for determining when parameters of simple models can be inferred accurately. However, for models of even modest complexity, more general tools are required to diagnose parameter non-identifiability. We present a method based in Bayesian inference that can be used to establish the reliability of parameter estimates, as well as yield accurate quantification of parameter confidence.
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