Email Address: firstname.lastname@example.org
Other Research Theme Collaborations: SOMX
I am currently working between the MRC Biostatistics and Epidemiology Units and CEDAR (Centre for Diet and Activity Research). I am part of James Woodcock's team working on the Integrated Transport and Health Impact Model (ITHIM, http://www.cedar.iph.cam.ac.uk/research/modelling/ithim/). I am particularly interested in uncertainties within the model: how they are understood and incorporated, and how they influence the outcome. In parallel, I am developing a comprehensive road traffic injuries model, incorporating uncertainty, and estimating risks for smaller geographies and areas with less good data.
Previously, working with Simon White and Paul Kirk at the MRC Biostatistics Unit, I used Gaussian processes to model longitudinal data for their inclusion in Bayesian profile regression. Based on PReMiuM, the code currently resides here: https://github.com/robj411/Longitudinal-PReMiuM.
I completed a PhD in the Theoretical Systems Biology group of Imperial College London under the supervision of Prof. Michael Stumpf. I had a broad interest in theoretical models of noisy biological systems, and Bayesian model selection and parameter inference. Ultimately I developed a protein-driven population-level modelling methodology, in which cells in a population divide at a rate dependent on the cell’s protein complement.
- R. Johnson and B.E.M. Munsky (2017), ‘The finite state projection approach to analyze dynamics of heterogeneous populations’, Physical Biology, 14(3), 035002.
- B. Klapholz, S.L. Herbert, B.T. Goult, J. Wellmann, M. Bouaouina, D.A. Calderwood, R. Johnson, M. Parsons and N.H. Brown (2015), ‘Integrin function is mediated by alternate perpendicular and parallel orientations of talin’, Current Biology, 25(7), 847–857.
- R. Johnson, P. Kirk and M.P.H. Stumpf (2014), ‘SYSBIONS: nested sampling for systems biology’, Bioinformatics.
- S.S. McMahon, A. Sim, S. Filippi, R. Johnson, J. Liepe, D. Smith and M.P.H. Stumpf (2014), ‘Information theory and signal transduction systems: from molecular information processing to network inference’, Semin Cell Dev Biol 35, 98–108.