176 citations · 683 across the 36 of their papers we have counts for
7 papers · 1 filter
Learning differential equation models from stochastic agent-based model simulations
John T. Nardini, Ruth E. Baker, Matthew J. Simpson +1
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology, and epidemiolo…
Profile likelihood analysis for a stochastic model of diffusion in heterogeneous media
Matthew J Simpson, Alexander P Browning, Christopher Drovandi +3
We compute profile likelihoods for a stochastic model of diffusive transport motivated by experimental observations of heat conduction in layered skin tissues. This process is mode…
Crowded transport within networked representations of complex geometries
Daniel B. Wilson, Francis G. Woodhouse, Matthew J. Simpson +1
Transport in crowded, complex environments occurs across many spatial scales. Geometric restrictions can hinder the motion of individuals and, combined with crowding between indivi…
Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
John H. Lagergren, John T. Nardini, Ruth E. Baker +2
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological syste…
Travelling waves in a free boundary mechanobiological model of an epithelial tissue
Ryan J Murphy, Pascal R Buenzli, Ruth E Baker +1
We consider a free boundary model of epithelial cell migration with logistic growth and nonlinear diffusion induced by mechanical interactions. Using numerical simulations, phase p…
Efficiently simulating discrete-state models with binary decision trees
Christopher Lester, Ruth E. Baker, Christian A. Yates
Stochastic simulation algorithms (SSAs) are widely used to numerically investigate the properties of stochastic, discrete-state models. The Gillespie Direct Method is the pre-emine…
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling
Thomas P. Prescott, Ruth E. Baker
Multifidelity approximate Bayesian computation (MF-ABC) is a likelihood-free technique for parameter inference that exploits model approximations to significantly increase the spee…