ArticleJournal of the Royal Society, Interface2020
Practical parameter identifiability for spatio-temporal models of cell invasion.
Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.Bulletin of mathematical biology · 2026Article
- Modelling the impact of temperature and bird migration on the spread of West Nile virus.One health (Amsterdam, Netherlands) · 2026Article
- Actin network heterogeneity tunes activator-inhibitor dynamics at the cell cortex.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- Inference and prediction for stochastic models of biological populations undergoing migration and proliferation.Journal of the Royal Society, Interface · 2025Review
- Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction.PLoS computational biology · 2025Article
- Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators.Journal of mathematical biology · 2025Article
- Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- A new paradigm considering multicellular adhesion, repulsion and attraction represent diverse cellular tile patterns.PLoS computational biology · 2025Article
- Quantifying cell cycle regulation by tissue crowding.Biophysical journal · 2025Article
- Efficient data collection for establishing practical identifiability via active learning.Computational and structural biotechnology journal · 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
- Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.Bulletin of mathematical biology · 2024Article
- Structural and practical identifiability of contrast transport models for DCE-MRI.PLoS computational biology · 2024Article
- Parameter identifiability and model selection for partial differential equation models of cell invasion.Journal of the Royal Society, Interface · 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
- Estimating global identifiability using conditional mutual information in a Bayesian framework.Scientific reports · 2023Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We examine the practical identifiability of parameters in a spatio-temporal reaction-diffusion model of a scratch assay. Experimental data involve fluorescent cell cycle labels, providing spatial information about cell position and temporal information about the cell cycle phase. Cell cycle labelling is incorporated into the reaction-diffusion model by treating the total population as two interacting subpopulations. Practical identifiability is examined using a Bayesian Markov chain Monte Carlo (MCMC) framework, confirming that the parameters are identifiable when we assume the diffusivities of the subpopulations are identical, but that the parameters are practically non-identifiable when we allow the diffusivities to be distinct. We also assess practical identifiability using a profile likelihood approach, providing similar results to MCMC with the advantage of being an order of magnitude faster to compute. Therefore, we suggest that the profile likelihood ought to be adopted as a screening tool to assess practical identifiability before MCMC computations are performed.
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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.