14 citations · 49 across the 11 of their papers we have counts for
11 papers
Dimension-dependent continuum limits in tissue mechanics
Matthew J Simpson, Pascal R Buenzli
Continuum descriptions of epithelial tissue mechanics can replace expensive individual-based simulations with tractable macroscopic models, yet the link between cell-scale forces a…
Continuum models describing probabilistic motion of tagged agents in exclusion processes
Michael J. Plank, Matthew J. Simpson
Lattice-based random walk models are widely used to study populations of migrating cells with motility bias and proliferation. Crowding is typically represented by volume exclusion…
Mechanical cell interactions on curved interfaces
Pascal R. Buenzli, Shahak Kuba, Ryan J. Murphy +1
We propose a simple mathematical model to describe the mechanical relaxation of cells within a curved epithelial tissue layer represented by an arbitrary curve in two-dimensional s…
Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences
Michael J. Plank, Matthew J. Simpson
Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations, and when estimating uncertainty in model predic…
Discrete and continuous mathematical models of sharp-fronted collective cell migration and invasion
Matthew J Simpson, Keeley M Murphy, Scott W McCue +1
Mathematical models describing the spatial spreading and invasion of populations of biological cells are often developed in a continuum modelling framework using reaction-diffusion…
Pushing coarse-grained models beyond the continuum limit using equation learning
Daniel J. VandenHeuvel, Pascal R. Buenzli, Matthew J. Simpson
Mathematical modelling of biological population dynamics often involves proposing high fidelity discrete agent-based models that capture stochasticity and individual-level processe…
Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis, and prediction in the life sciences
Ryan J. Murphy, Oliver J. Maclaren, Matthew J. Simpson
Throughout the life sciences we routinely seek to interpret measurements and observations using parameterised mechanistic mathematical models. A fundamental and often overlooked ch…
Generalised likelihood profiles for models with intractable likelihoods
David J. Warne, Oliver J. Maclaren, Elliot J. Carr +2
Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, th…
Geometric analysis enables biological insight from complex non-identifiable models using simple surrogates
Alexander P Browning, Matthew J Simpson
An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have…
Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
David J. Warne, Thomas P. Prescott, Ruth E. Baker +1
Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistica…
A novel mathematical model of heterogeneous cell proliferation
Sean T. Vittadello, Scott W. McCue, Gency Gunasingh +2
We present a novel mathematical model of heterogeneous cell proliferation where the total population consists of a subpopulation of slow-proliferating cells and a subpopulation of…