I am a computational biologist (post-doctoral research scientist) working in the Lydall lab in the Institute for Cellular and Molecular Biosciences (ICaMB) at Newcastle University.
I am particularly interested in computational and mathematical models of biological systems and the assessment and development of such models (usually dynamic, mechanistic simulation models). I develop high-throughput, robotic, growth assays for carrying out genome-wide fitness screens in the model organism S. cerevisiae (budding yeast), comparing fitnesses to search for evidence for interactions between genes on a genome-wide scale. These quantitative, genome-wide tools allow us to develop a systematic understanding of targetted areas of eukaryotic biology.
The Lydall lab is specifically interested in applying these tools to develop a systematic understanding of how the telomere cap works, which may improve our understanding of replicative senescence (relevant for ageing) and cancer. Much of my previous work has involved studying replicative senescence and ageing in human cell cultures and I enjoy drawing parallels between the two model systems.
My university website contains other things I am interested in.
Mathematical modelling, numerical simulation, stochastic simulation, systems biology, optimisation, parameter estimation, Bayesian inference, quantitative growth assays, image analysis, epistasis, automated microscopy, data handling, data visualisation, robot-assisted science.
Currently I work on genome-wide screens in S. cerevisiae to understand the function of the telomere cap. I developed an image analysis tool: Colonyzer and complementary Quantitative Fitness Analysis (QFA) workflows to infer growth rates and genetic interaction strengths from robot-assisted timelapse photography of thousands of independent microorganism cultures growing in parallel on solid agar plates. I have developed software which automates QFA and allows visualisation of genome-wide datasets. There is a video article describing QFA in more detail. I also developed the computational infrastructure for capturing, archiving and analysing data generated by the High-throughput Screening service at Newcastle (which carries out QFA screens, for example).
As experimental datasets become bigger and more quantitative, and as computing becomes more ubiquitous, being able to read and write code has become an increasingly important component of scientific literacy. Python is a powerful general programming language, that is very clean and easy to learn and is extensible to an amazing range of applications. Each year I run a half day workshop introducing Python programming to biology researchers in the medical school. The course notes are available online.
I lecture on the MRes in Systems Biology course run at Newcastle.