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Professor Nigel Burroughs

On leave terms 1-3 2017/18

Picture of Nigel Burroughs  

Nigel Burroughs

Professor of Mathematics, Mathematics Intitute

Warwick Systems Biology Centre and Zeeman Institute


Office: Senate House 333
Phone: +44 (0)24 7652 4682
Email: N.J.BurroughsATwarwick.ac.uk (please replace AT with @)


Teaching Responsibilities 2017/18: None

TCC - Taught Course Centre module: Mathematical and Statistical challenges in Cancer

MathSys Group project (MA932) on Seeing is believing – unravelling biological dynamics from the next generation of light microscopes.

Research Interests:
My main interests are in using mathematics and statistical methods to generate an understanding of the biological and medical systems. My main focus is reverse engineering - fitting a biological or biophysical 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 biological realism that it is informative. I typically use Bayesian techniques 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, the immunological synapse and chromosome oscillations during metaphase. I currently have projects on DNA replication, the dynamics of the separation of replicated chromosomes during mitosis and microtubule dynamics. Other projects include enhancing photosynthesis by modelling potential designs, linked to a transatlantic synthetic biology programme and using single particle tracking data to infer the protein environment in the cell membrane. I work with many experts in the medical school who challenge me and my 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 Warwick Systems Biology website and associated pages


Recent publications

Research income. I am funded currently by BBSRC. I have also received EPSRC and Leverhulme Trust grants in the past.