Mathematical modelling and analysis

Mathematical models can be used to describe core biological mechanisms in order to help develop insight into the way that systems behave and predict behaviour. Ultimately, this may inform policy decisions, for example, when and who to vaccinate for infectious diseases, drug design or optimal light patterns for healthy circadian rhythmicity.

Overview

The kind of models that occur depend strongly on the nature of the research question and the particular system. Members of our centre have expertise in developing models in a variety of forms including ordinary differential equations, partial differential equations and agent-based models. Depending on the application, models may be deterministic or stochastic.

Models are analysed using a broad range of techniques including geometric singular perturbation methods, multiple spatial and/or time scale analysis, asymptotics and nonlinear dynamical systems techniques including bifurcation theory.

Members of our centre are also actively involved in developing novel numerical methods, for example for model data fusion (data assimilation).

Our expertise

Cancer
Ecology
Elasticity theory and mechanics of cell growth

Epidemiology
Evolutionary biology
Open quantum systems
Population models

Sleep and circadian rhythms
Skin adsorption of chemicals
Stem cell differentiation modelling
Systems pharmacology (PKPD).

Meet the team

Members

Professor Philip Aston

Professor of Mathematics

Professor Claudio Avignone Rossa

Professor of Systems Microbiology

Dr Matteo Barberis

Faculty Theme Leader for Infection and Immunity, School of Biosciences International Lead, Coordinator of Centre for Mathematical and Computational Biology (CMCB), and Reader in Systems Biology