Throughout the life sciences, we routinely seek to interpret measurements and observations
using parametrized mechanistic mathematical models. A fundamental and often overlooked …

Interpreting data using mechanistic mathematical models provides a foundation for
discovery and decision-making in all areas of science and engineering. Developing …

Mathematical modelling of biological population dynamics often involves proposing high-
fidelity discrete agent-based models that capture stochasticity and individual-level …

Throughout the life sciences we routinely seek to interpret measurements and observations
using parameterised mechanistic mathematical models. A fundamental and often …

We consider optimal experimental design (OED) for Bayesian nonlinear inverse problems
governed by partial differential equations (PDEs) under model uncertainty. Specifically, we …

Calibrating mathematical models to describe ecological data provides important insight via
parameter estimation that is not possible from analysing data alone. When we undertake a …

Existing studies comparing individual-based models of growing cell populations and their
continuum counterparts have mainly focused on homogeneous populations, in which all …

Likelihood profiling is an efficient and powerful frequentist approach for parameter
estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately …

Interpreting data using mechanistic mathematical models provides a foundation for
discovery and decision-making in all areas of science and engineering. Key steps in using …

Collective migration, or the coordinated movement of many individuals, is an important
component of many biological processes, including wound healing, tumorigenesis, and …