Applied Statistics (STAT200, spring semesters)
In Applied Statistics we look at how tools from stats can connect our ideas about how the world works to real observations. There are no prerequisites, though some experience with probability and the R software environment is useful. We cover hypothesis testing, regression, parameteric and nonparameteric methods, resampling, and usually generalised linear models and mixed effects models. Lectures and practical classes (using R) combine to provide a general “roadmap” for statistical methods, with a particular focus on real-world applications and critical analysis.
Biostatistics (STAT202, irregular fall semesters)
In Biostatistics we focus on statistical methods that are designed for particular biological questions. We go across length scales, starting from genomes (statistical and population genetics) through systems biology (stochastic gene expression, ODE and network models for regulation, constraint-based modelling for metabolism) and to ecosystems (capture-recapture and spatial sampling for estimating population sizes and survival).
Discrete Stochastic Modelling in Cell Biology (irregular)
This advanced course looks at methods for modelling discrete-space, continuous-time stochastic processes, with application to questions in cell biology (gene expression, organelle biology, cell dynamics). We cover chemical master equations, direct solutions and approximation methods, Gillespie’s stochastic simulation algorithm, and issues in parameterising stochastic models in biology.
Project Supervision (ongoing)
There are typically a collection of potential BSc and MSc research projects in the group available for interested candidates, and Iain is very happy to discuss tailor-made research projects for folks interested in modelling and statistical approaches (not necessarily in biology). Projects can often include method development (maths and coding), method application to new datasets, and combinations thereof. Previous projects have included modelling cancer progression, anti-microbial resistance evolution, organelle biology, electricity markets, and more!
Past Teaching
Iain’s past teaching portfolio has included evolutionary modelling, statistics, inference and control theory in biology, photosynthesis, algorithmic botany, mathematical methods for physics, C programming for physics, and other topics usually somewhere in the general space of modelling, simulation, and biology.