Office: Mathematical Sciences Building 5.06
Joining my lab:
Interested in a PhD: I take PhD students from the Mathematics DTCs (Mathsys, Mathematics CDT), both offering scholarships for starting in 2023. I also have had interdisciplinary students joint with supervisors in WMS through MRC DTP and IBR.
If you are interested in working in my lab as a PDRA, you could apply for a Fellowship, see here for ideas. Contact me if interested and we can discuss suitable proposals. Also see the Eurotia European University fellowships scheme (which can be taken at Warwick, closes Dec 15th 2022. Relevant themes include Data and intelligence, and Health, possibly in mathematical modelling/statistical data analysis in reproductive health or cancer).
I also supervise/host UG research projects, either through URSS (summer) or research (R-)projects (eg MA4K9).
Teaching Responsibilities 2022:
Term 2/3: On research leave.
Tutees: Tutorials are to be arranged.
Current research themes:
Human cell division - mechanics and regulation across scales (funded by BBSRC, previously Leverhulme Trust). With Andrew McAinsh, WMS.
Understanding human egg development and the first cell divisions (funded by Wellcome Trust). With Andrew McAinsh, WMS and Adele Marston, Edinburgh.
Cancer therapy optimisation using PMP, HJB (MathSys PhD student, EPSRC). With Annabelle Ballesta, Marie-Curie Institute, Paris.
Predicting recurrent miscarriage, (UG research project and previously funded by Thommy's baby charity). With Siobhan Quenby, WMS.
Inferring and optimising Immunological memory from vaccination (MathSys PhD student, EPSRC). With Matt Keeling (SBIDER).
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, and chromosome movements during human meiosis (Wellcome Trust funded, egg formation). Other projects include optimising cancer treatments, predicting miscarriage, microtubule dynamics and in host vaccination dynamics. I work with a number of experts in Warwick 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, control theory.
Constandina Koki (PDRA, funded by Wellcome Trust). Working on Bayesian inference in cell division.
Abdullahi Daniyan (PDRA, funded by Wellcome Trust). Working on computational methods to analyse 3+1D imaging data.
Yiping Zhang (MathSys PhD student).
Byron Tzamarias (MathSys PhD student).
For more information on my research 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.