Journal
JOURNAL OF THE ROYAL SOCIETY INTERFACE
Volume 17, Issue 164, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsif.2020.0055
Keywords
identifiability analysis; Bayesian inference; profile likelihood; reaction-diffusion; cell cycle
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Funding
- Australian Research Council [DP170100474]
- University of Canterbury Erskine Fellowship
- University of Auckland, Faculty of Engineering James and Hazel D. Lord Emerging Faculty Fellowship
- Royal Society Wolfson Research Merit Award
- Leverhulme Trust Research Fellowship
- BBSRC [BB/R000816/1]
- BBSRC [BB/R000816/1] Funding Source: UKRI
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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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