The Computational Systems and Synthetic Biology group applies mathematical modelling, experimental methods and Bayesian statistics to problems in systems and synthetic biology. Our goal is to make biology more predictive.

We build models of biological systems expressed as dynamical systems (differential equations, stochastic processes) in order to better understand them, elucidate their regulatory mechanisms and enable us to engineer them for therapeutic purposes. We use techniques from molecular biology, fluorescent time course microscopy and flow cytometry to test model predictions and to help us better understand the underlying biology so that we can create more accurate representations.

Our current research areas include developing methods for engineering more robust synthetic biological systems, dynamical modelling of the DNA damage response and understanding the signalling mechanisms controlling phospholipid metabolism. For more details see here.


This page was last modified on 03 Jan 2017.