ArticleJournal of the Royal Society, Interface2018
Inference-based assessment of parameter identifiability in nonlinear biological models.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Walking the tightrope: Balancing opposing cooperativities in dynein assembly.The Journal of biological chemistry · 2026Article
- Walking the Tightrope: Balancing Opposing Cooperativities as an Operating Principle in Dynein Assembly.bioRxiv : the preprint server for biology · 2026Article
- A network-based analysis of bacterial growth on substrate mixtures uncovers glucose inhibition in Phaeobacter inhibens.Scientific reports · 2026Article
- Bayesian model selection for COVID-19 pandemic state estimation using extended Kalman filters: Case study for Saudi Arabia.PLOS global public health · 2024Article
- Bayesian inference for fitting cardiac models to experiments: estimating parameter distributions using Hamiltonian Monte Carlo and approximate Bayesian computation.Medical & biological engineering & computing · 2023Article
- Limits of epidemic prediction using SIR models.Journal of mathematical biology · 2022Article
- Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters.Nucleic acids research · 2021Article
- Identifiability analysis for stochastic differential equation models in systems biology.Journal of the Royal Society, Interface · 2020Article
- Pleione: A tool for statistical and multi-objective calibration of Rule-based models.Scientific reports · 2019Article
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Authors and funding
4 authors.
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Abstract
As systems approaches to the development of biological models become more mature, attention is increasingly focusing on the problem of inferring parameter values within those models from experimental data. However, particularly for nonlinear models, it is not obvious, either from inspection of the model or from the experimental data, that the inverse problem of parameter fitting will have a unique solution, or even a non-unique solution that constrains the parameters to lie within a plausible physiological range. Where parameters cannot be constrained they are termed 'unidentifiable'. We focus on gaining insight into the causes of unidentifiability using inference-based methods, and compare a recently developed measure-theoretic approach to inverse sensitivity analysis to the popular Markov chain Monte Carlo and approximate Bayesian computation techniques for Bayesian inference. All three approaches map the uncertainty in quantities of interest in the output space to the probability of sets of parameters in the input space. The geometry of these sets demonstrates how unidentifiability can be caused by parameter compensation and provides an intuitive approach to inference-based experimental design.
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