Picture by IBM Research

Research expertise

Dr Andrew Hone focuses on Biologically inspired computing; artificial immune systems; solvable models in mathematical biology.

Professor Phil Brown specialises in Statistical modelling of proteomic data using Mass Spectroscopy; whole genome data analysis; statistical analysis of microarray data.

Dr Alfred Kume focuses on a Statistical analysis of shape; directional data; image analysis.

Dr Jim Griffin specialises in Clustering using Bayesian nonparametric techniques.

Professor Martin Ridout, Professor Byron Morgan and Dr Diana Cole specialise in Stochastic modelling in Biology.

Cross-disciplinary research and projects

Professor Phil Brown’s research projects;

  • Pfizer case award
    Funded By:    Pfizer
    Period:           2001 – 2004
  • Pfizer Consultancy Analysing Mass Spectroscopy Proteomic Data
    Funded By:    Pfizer
    Period:           2007 – 2009
  • Adaptive Methodology for functional biomedical data
    Funded By:    NIH (USA – National Institutes of Health)
    Period:           2007
    Collaboration between Professor Phil Brown, University of Kent and Dr Jeffrey Morris, MD Anderson Cancer Center, Houston, USA.

The Stochastic models of yeast prions project was a collaboration of staff across the Faculty of Sciences;

  • Professor Mick Tuite (Biosciences)
  • Professor Martin Ridout (Maths and Statistics)
  • Professor Byron Morgan (Maths and Statistics)
  • Dr Lee Byrne (Biosciences)
  • Dr Diana Cole (Maths and Statistics)

Funded By:   Biotechnology and Biological Sciences Research Council (96/E18382)
Period:           2004 – 2007
The aim of the work is to use stochastic modelling techniques to improve our understanding of prion proteins in the yeast Saccharomyces cerevisiae.
The BBSRC end-of-grant review awarded this project an A grade – “Very high-class work that has produced results of considerable scientific importance in a cost-effective way and met almost all of the agreed or related key objectives”.

Selected publications

Morris, J. S.  & Brown, P. J., Baggerly, K.A. and Coombes, K. R. (2006) Analysing Mass Spectrometry Data Using Wavelet-Based Functional Mixed Models. In  Bayesian Inference for Gene Expression and Proteomics Eds K.Do, P. Mueller and M Vannucci, p269-292, Cambridge U Press.

Ahmad, N.& Zhang, J. Brown, P. J., James, D. C., Birch, J. R., Racher, A. J., Smales, C. M. (2006) On the statistical analysis of the GS-NSO cell proteome: Imputation, clustering and variability testing. Biochimica et Biophysica Acta, 1764, p1179-1187.

Morris, J. S.  &  Brown, P. J., Herrick, R.C., Baggerly, K.A. and Coombes, K. R. (2008) Bayesian analysis mass spectrometry proteomic data using wavelet-based functional mixed models. Biometrics 64, 479-489.

Strimenopoulou, F. & Brown, P. J. (2008) Empirical Bayes logistic regression analysis. Statistical Applications in Genetics and Molecular Biology Vol. 7 Iss. 2 Article 9, available at:

Byrne, L.J., Cole, D.J., Cox, B.S., Ridout, M.S., Morgan, B.J.T. and Tuite, M.F. (2009) The number and transmission of [PSI+] prion seeds (propagons) in the yeast Saccharomyces cerevisiaePLoS ONE 4: e4670. doi:10.1371/journal.pone.0004670 [pdf]

Ridout, M.S. (2008) Computational methods for yeast prion curing curves. Mathematical Biosciences215, 152-157. [Journal link]

Palmer, K.J., Ridout,M.S. and Morgan, B.J.T. (2008). Modelling cell generation times using the tempered stable distribution. Applied Statistics57, 379-397.

Byrne, L.J., Cox, B.S., Cole, D.J., Ridout,M.S., Morgan, B.J.T. and Tuite, M.F. (2007). Cell division is essential for elimination of the yeast [PSI+] prion by guanidine hydrochloride. PNAS104, 11688-11693. Journal link

Cole, D.J., Ridout, M.S., Morgan, B.J.T., Byrne, L.J. and Tuite, M.F. (2007). Approximations for expected generation number. Biometrics63, 1023–1030.

Ridout, M.S., Cole, D.J., Morgan, B.J.T., Byrne, L.J. and Tuite, M.F. (2006). New approximations to the Malthusian parameter. Biometrics62, 1216-1223.

Cole, D.J., Morgan, B.J.T., Ridout, M.S., Byrne, L.J. and Tuite, M.F. (2004). Estimating the number of prions in yeast cells. Mathematical Medicine and Biology21, 369-395.

Morgan, B.J.T., Ridout, M.S. and Ruddock, L.W. (2003). Models for yeast prions. Biometrics59, 562-569.

Memberships of learned societies

  • Fellow of American Statistical Association (Elected 2003).
  • Fellow of the Institute of Mathematical Statistics (Elected 1992)
  • Mitchell Prize 2003 for the best Bayesian paper in Applied Statistics (joint with J Morris, M Vannucci and R Carroll).