DPhil thesis: Uncertainty characterisation in action potential modelling for cardiac drug safety
We used bespoke algorithms implemented in Python that interfaced with the C++ library Chaste to perform Bayesian inference on single-cell action potential recordings from ventricular myocytes.
Data visualisation and analysis was performed in Python and Matlab.
I obtained a MMath degree in Mathematics from the University of Oxford in 2013. I then joined the Systems Approaches to Biomedical Science Industrial Doctorate Centre in Oxford and completed my DPhil within the Computational Biology group in 2018. My DPhil focused on uncertainty quantification in mathematical modelling for drug cardiac safety assessment and was undertaken in collaboration with Roche. My DPhil was supervised by Dr Gary Mirams (Nottingham), Professor David Gavaghan (Oxford), Dr Rémi Bardenet (Lille), Dr Liudmila Polonchuk (Roche), and Dr Mark Davies (Roche).
My personal website is here, currently under construction.
Hierarchical Bayesian inference for ion channel screening dose−response data
RH Johnstone‚ R Bardenet‚ DJ Gavaghan and GR Mirams
In Wellcome Open Research. Vol. 1. Pages 6. 2016.
Uncertainty and variability in models of the cardiac action potential: can we build trustworthy models?
RH Johnstone‚ ET Chang‚ R Bardenet‚ TP de Boer‚ DJ Gavaghan‚ P Pathmanathan‚ RH Clayton and GR Mirams
In Journal of Molecular and Cellular Cardiology. Vol. 96. Pages 49−62. 2016.