collaborators

7 papers

q-bio.TO2026

Adaptive therapy under parametric, structural, and measurement uncertainty

Alexander P Browning, Rebecca M Crossley, Ryan J Murphy +2

Adaptive therapy has emerged as a promising treatment strategy that exploits within-tumour competition to delay disease progression. Implementation, however, typically relies on in…

cs.LG2026

Physics-Informed Neural Networks for Biological Reaction-Diffusion Systems

William Lavery, Jodie A. Cochrane, Christian Olesen +3

Physics-informed neural networks (PINNs) provide a powerful framework for learning governing equations of dynamical systems from data. Biologically-informed neural networks (BINNs)…

stat.CO2026

A practical introduction to ODE modelling in Stan for biological systems

Sara Hamis, John Forslund, Cici Chen Gu +1

Integrating dynamical systems models with time series data is a central part of contemporary mathematical biology. With the rich variety of available models and data, numerous meth…

stat.ME2025

A nonparametric approach to practical identifiability of nonlinear mixed effects models

Tyler Cassidy, Stuart T. Johnston, Michael Plank +4

Mathematical modelling is a widely used approach to understand and interpret clinical trial data. This modelling typically involves fitting mechanistic mathematical models to data…

physics.soc-ph2025

A bibliometric study on mathematical oncology: interdisciplinarity, internationality, collaboration and trending topics

Kira Pugh, Linnéa Gyllingberg, Stanislav Stratiev +1

Mathematical oncology is an interdisciplinary research field where the mathematical sciences meet cancer research. Being situated at the intersection of these two fields makes math…

q-bio.PE2025

Phenotypic heterogeneity in temporally fluctuating environments

Alexander P Browning, Sara Hamis

Many biological systems regulate phenotypic heterogeneity as a fitness-maximising strategy in uncertain and dynamic environments. Analysis of such strategies is typically confined…