On study leave terms 1-3 2017/18
Office: Senate House 333
Two exciting research fellow positions are available to work in my lab using mathematical and statistical techniques to gain understanding of mechanisms/dynamics in cell division:
- the kinetochore mechanical machine (advert, closed),
- chromosome self-organisation position (advert, closing date 4th June 2018).
(see my cell division projects page for scientific details).
Teaching Responsibilities 2017/18: None
In 2018/19 i will teach MA4L5 Mathematics of Cancer, a course covering the mathematics used in the analysis of cancer data and for understanding cancer.
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), cytokinesis and microtubule dynamics. Other projects include enhancing photosynthesis by modelling potential designs (BBSRC/NSF funded), and analysing single particle tracking data to infer the protein environment in the cell membrane. 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 methods: developing biophysically motivated models, stochastic models, 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 and the Leverhulme Trust. I have also received EPSRC grants in the past.