1 code implementation • 13 Jun 2023 • Stuart T. Johnston, Matthew J. Simpson
We obtain the solution by identifying a series of transformations that converts the model of a nonlinear diffusive process on an evolving domain to a nonlinear diffusion equation on a fixed domain, which admits known exact solutions for certain choices of diffusivity functions.
1 code implementation • 19 Apr 2023 • Stuart T. Johnston, Matthew J. Simpson
The exact solutions reveal the relationship between model parameters, such as the diffusivity and the type and rate of domain growth, and key statistics, such as the survival and splitting probabilities.
no code implementations • 15 Feb 2023 • Yuting Fang, Stuart T. Johnston, Matt Faria, Xinyu Huang, Andrew W. Eckford, Jamie Evans
Our results show that the activation probability at the B-NM increases as this B-NM is located closer to the center of the B-NM population and the aggregate absorption rate of the drug molecules non-linearly increases as the population density increases.
1 code implementation • 9 May 2022 • Domenic P. J. Germano, Stuart T. Johnston, Edmund J. Crampin, James M. Osborne
We also demonstrate the model's robustness under tissue renewal, cell migration and cell removal.
1 code implementation • 5 Jan 2021 • Yifei Li, Stuart T. Johnston, Pascal R. Buenzli, Peter van Heijster, Matthew J. Simpson
In this work we study population survival or extinction using a stochastic, discrete lattice-based random walk model where individuals undergo movement, birth and death events.