ArticleBiophysical journal2012
MCMC can detect nonidentifiable models.
Article in Biophysical journal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Generalised Hodgkin-Huxley model captures human P2X and AMPA receptor currents.The Journal of physiology · 2026Article
- Modelling ion channels with a view towards identifiability.Bulletin of mathematical biology · 2025Article
- 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
- Evaluating the predictive accuracy of ion-channel models using data from multiple experimental designs.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences.Royal Society open science · 2024Article
- Making Predictions Using Poorly Identified Mathematical Models.Bulletin of mathematical biology · 2024Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- Profile-Wise Analysis: A profile likelihood-based workflow for identifiability analysis, estimation, and prediction with mechanistic mathematical models.PLoS computational biology · 2023Article
- Quantifying tissue growth, shape and collision via continuum models and Bayesian inference.Journal of the Royal Society, Interface · 2023Article
- The fractal brain: scale-invariance in structure and dynamics.Cerebral cortex (New York, N.Y. : 1991) · 2023Review
- Are Staphylococcus aureus Carrier Types Evidence of Population Heterogeneity?American journal of epidemiology · 2023Article
- Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.Journal of the Royal Society, Interface · 2022Article
- Efficient inference and identifiability analysis for differential equation models with random parameters.PLoS computational biology · 2022Article
- An in-silico analysis of experimental designs to study ventricular function: A focus on the right ventricle.PLoS computational biology · 2022Article
- Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans.Scientific reports · 2022Article
- Article
- Parameter estimation and uncertainty quantification using information geometry.Journal of the Royal Society, Interface · 2022Review
- A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability.Bulletin of mathematical biology · 2022Article
- Identification of structures for ion channel kinetic models.PLoS computational biology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Continuous-time Markov models have been considered the best representation for the stochastic dynamics of ion channels for more than thirty years. For most single-channel data sets, several open and closed states are required for accurately representing the dynamics. However, each data point only shows if the channel is open or closed but not in which state it is. Consequently, some model structures are inherently overparameterized and therefore, in principle, unsuitable for representing any data--those models are called "nonidentifiable". As of this writing, it seems to be poorly understood which continuous-time Markov models are identifiable and which are not, therefore the unconscious use of a nonidentifiable model is a considerable concern. To address this problem, an improved variant of a recently published Markov-chain Monte Carlo method is presented. The algorithm is tested using test data as well as experimental data. We demonstrate that, opposed to a widely used maximum-likelihood estimator, it gives clear warning signs when a nonidentifiable model is used for fitting. Furthermore, for test data that was generated from a nonidentifiable model, the Markov-chain Monte Carlo results recover much more information from the data than maximum-likelihood estimation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.