I am Professor of computational Biology at the School of Biosciences, University of Nottingham. I have been working at the University of Nottingham since 2009, when I was appointed as Associate Professor, and have been Professor since 2018. Before working at the University of Nottingham, I spent five years at the University of Birmingham as Lecturer in Bioinformatics. I studied mathematics at Trinity College, Cambridge and for a D.Phil. at the Department of Zoology, University of Oxford, supervised by Martin Nowak. My D.Phil. topic was on mathematical modeling of T lymphocyte recirculation and virus dynamics. Following my D.Phil., I worked briefly with Boris Vojnovic at the Gray Laboratory, before taking a position in the Bioinformatics group at Glaxo Wellcome Research and Development (as it was), and then as Head of Bioinformatics at Ed Southern's microarray company, Oxford Gene Technology. I left OGT after receiving a commission from Cambridge University Press to write Microarray Bioinformatics, and worked as a free-lance bioinformatics consultant during that time.
I teach on a range of modules associated with mathematical and computer modelling in the biological and environmental sciences. Specifically:
- Computer Modelling in Science: Introduction (D224E4) - Year 2
- Computer Modelling in Science: Applications (C135E9) - Year 3
- Project Management - MSci year
We use mathematical, computing and statistical techniques to build predictive models for biological systems. There are three main areas of activity:
- Antimicrobial resistance. We use mathematical and computer models at both molecular and population levels to study mechanisms for, and spread of, antimicrobial resistance. Current work is focussed on the emergence of antimicrobial resistance in the environment, especially in agriculture. Past work has included modelling plasmid regulation, and models for molecular mechanisms for antimicrobial metals, including zinc and mercury. We have also carried out in silico evolution of gene network responses to antimicrobial agents.
- Model-driven data analysis. We combine mathematical models with both frequentist and Bayesian methods with the aims of best interpretation of experimental data. Applications have included bioluminescent reporter data, Biolog phenotype arrays and metagenomics sequencing data, with applications in brewing, bioenergy, food safety and antimicrobial resistance.
- Quantitative bioinformatics. We retain an interest in using statistical and machine learning techniques as applied to large scale quantitative data sets from Omics technologies, including transcriptomics, proteomics and metabolomics.
We try, where possible, to work on projects with active collaborations with experimental biologists. While our main focus is on microbiology, we are happy to foster collaborations with scientists working in any area of biology, and this range is reflected in our publications.