ArticleJournal of the Royal Society, Interface2021
Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation.
Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Random walk models in the life sciences: including births, deaths and local interactions.Journal of the Royal Society, Interface · 2025Review
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response.Bulletin of mathematical biology · 2024Article
- Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation.Journal of mathematical biology · 2024Article
- Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Article
- Using mechanistic model-based inference to understand and project epidemic dynamics with time-varying contact and vaccination rates.Scientific reports · 2022Article
- A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling.Journal of the Royal Society, Interface · 2022Article
- Designing and interpreting 4D tumour spheroid experiments.Communications biology · 2022Article
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Authors and funding
3 authors.
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Abstract
We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at https://github.com/michaelcarr-stats/FUCCI.
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