Office: Mathematical Sciences Building 5.06
A 4 year PDRA to work on Statistical analysis and integration of multi-variate biological data is available.
Would possibly interest people with a PhD in statistics, mathematics, physics, operations research, computer science or data science.
Teaching Responsibilities 2020/21:
Term 2: MA250 Introduction to PDEs
Tutees: Office hours Thursday 2-3pm (primarily for 3rd, 4th years but all welcome).
My main interests are in using mathematics and statistical methods to understand biological and medical phenomena. My main focus is reverse engineering/model inference - fitting a biological or biophysically motivated model to experimental data to infer the model parameters and answer mechanistic hypotheses directly from the data. This can be challenging, primarily because the fitted model must be both simple enough to fit to data but also encompass sufficient realism that it is informative. I typically use Bayesian techniques within a Markov chain Monte Carlo (MCMC) framework which have the advantage of estimating parameter confidence, propagate noise to the parameter estimates, and have a powerful model selection framework which can be used to formulate and address biological hypotheses. I have used such techniques in gene regulatory network inference, immunological synapse patternation, chromosome movements during cell division and DNA replication. I currently have projects on the dynamics (congression) of replicated chromosomes during cell division (BBSRC funded), kinetochore conformation dynamics (Leverhulme Trust funded), both employing modeling within an MCMC context, whilst other projects chromosome packing in the nucleus, microtubule dynamics and chromosome movements during human meiosis (egg formation). I work with a number of experts in the medical school who challenge me and these methods with excellent data from their latest top of the range microscopes (light sheet, super-resolution)!
Main methodologies: developing biophysically motivated models, stochastic modeling, Markov chain Monte Carlo (MCMC) algorithms, model selection, dynamical systems, PDEs, Monte Carlo simulations, perturbation theory .
For more information see my pages on the Zeeman Institute website.
Research income. I am funded currently by BBSRC, the Leverhulme Trust and the Wellcome Trust. I have also previously received EPSRC funding.