UNDER CONSTRUCTION: My personal website
Jun - Sep 2021
Having previously analysed data generated in vivo from tissue samples in the Prabakaran Group, I wondered if biological data could be generated in silico, using networks reconstructed from theoretical principles in mathematical models. What works and what doesn’t in mathematical models of networks may then hold information about the in vivo systems they model.
I joined the lab of Professor Jeremy Gunawardena at Harvard during the summer of 2021 to pursue this line of thought. Specifically, I focussed on modelling the dynamics of histone methylation marks and examining whether current models were representative of the complexity of the system. Most current models of histone methylation assume a limitless abundance of enzyme but fail to recognise their assumption. Since this is not biologically representative, we built an enzyme-explicit stochastic simulation of a histone modification system. The inclusion of enzymes in the model drastically altered the system’s behaviour, and I found that histone modification dynamics were dependent on three independent parameters – two of which had not been considered by previous models. I had previously learnt about histone modifications from a biochemical point-of-view, so approaching it from this new angle made me appreciate how different research methods can complement each other, leading to a deeper understanding of the subject at hand. I also realised that assumptions should be questioned and investigated, for therein may lie deeper understanding of phenomena.
I was chosen to present my work at a student symposium run by science societies at Oxford and Cambridge (Varsity Sci), alongside two Nobel Laureates. You can watch my talk here.