Richard Reeve
PDRA
Room 314, Graham Kerr Building,
Division of Ecology and Evolutionary Biology,
University of Glasgow, Glasgow, G12 8QQ
Tel.: +44 (0)141 330 6638
Fax: +44 (0)141 330 2792
Email: r.reeve@bio.gla.ac.uk
Academic History
2003-2005: PDRA (Artificial Intelligence), University of Edinburgh2003: PDRA (Neuroinformatics), ETH (Zurich) / University of Zurich
2000-2002: PDRA (Psychology), University of Stirling
1999: PDRA (Cognitive Science), University of Edinburgh
1994-1999: PhD (Artificial Intelligence) University of Edinburgh
1993-1994: MSc (IT) University of Edinburgh
1989-1992: BA (Mathematics) University of Cambridge
Research Interests
I am a mathematical modeller with a background in Artificial Intelligence. My interests lie in the application of mathematical, computational and statistical tools to underexploited biological datasets, particularly focussing on pathogens and vaccines, where large amounts of data are collected on protection for testing purposes which can be reused to investigate vaccine:immune system interaction. I am more generally interested in epidemiological systems and host-pathogen interactions, particularly how they affect our ability to predict the effects of vaccines in the real world.
I am currently involved in a BBSRC research project to predict computationally the immunological cross reactivity of different strains of the foot-and-mouth disease virus (FMDV) from the amino-acid sequences of their antigenic proteins. Existing and new collaborations in this area with the Institute for Animal Health in Pirbright, Onderstepoort Veterinary Institute (South Africa) and Plum Island Animal Disease Center (USA) feed into and extend this research as well as providing access to new sources of data. I am also collaborating with the FMD Vaccine Group at Pirbright to understand how we can better assess vaccine efficacy, and this collaboration is now extending to SENASA (Argentina), as well as various European FMD research laboratories. I am also part of an EU project starting in 2009 to improve FMDV vaccine efficacy testing and cross-protection prediction. Further work is looking at extending the approaches used here to other viruses including Influenza A.