102 citations · 116 across the 5 of their papers we have counts for
10 papers
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…
Effects of different discretisations of the Laplacian upon stochastic simulations of reaction-diffusion systems on both static and growing domains
Bartosz J. Bartmanski, Ruth E. Baker
By discretising space into compartments and letting system dynamics be governed by the reaction-diffusion master equation, it is possible to derive and simulate a stochastic model…
Convergence of solutions in a mean-field model of go-or-grow type with reservation of sites for proliferation and cell cycle delay
Ruth E. Baker, Péter Boldog, Gergely Röst
We consider the mean-field approximation of an individual-based model describing cell motility and proliferation, which incorporates the volume exclusion principle, the go-or-grow…
Displacement of transport processes on networked topologies
Daniel B. Wilson, Ruth E. Baker, Francis G. Woodhouse
Consider a particle whose position evolves along the edges of a network. One definition for the displacement of a particle is the length of the shortest path on the network between…