I’m broadly interested in the application of computation and mathematics to biology, but at the moment I enjoy looking at the interactions between humans and microbes (e.g. bacteria, viruses, and fungi), and how these relate to health and disease.
At the moment I’m in my second of the 4-year Wellcome Trust PhD Programme in Mathematical Genomics and Medicine. Having previously done a rotation project in Emma Davenport’s lab at the Sanger Institute (blog post here), I then decided to make this my full PhD Project. I’ll be focusing on human variation in gene transcription (such as alternative splicing) and how this contributes to the heterogeneity of immune responses. I’ll also be looking at the interactions of different microbes (both pathogenic and commensal) on this.
Check out my blog for more details on these.
Phylogenomic analysis of an infant gut commensal
For my second rotation project, I performed the largest to-date analysis of the infant probiotic bacteria Bifidobacterium breve. This was in the lab of Trevor Lawley at the Wellcome Sanger Institute. By looking at a collection of 414 genomes, I was able to contrast the metabolic potential of each, as well as make inferences about the distribution and capabilities of the species. I also demonstrated that existing studies which have only assessed in the order of 20-30 isolates are vastly under powered and neglect geographically diverse lineages which I correct for.
Asthma transcription network analyses
In the lab of Mike Inouye at the Baker Heart and Diabetes Institute in Melbourne, Australia, I clustered gene expression networks to aid in analysing the development and progression of asthma in infants. This approach summarises large groups of similarly-regulated genes as clusters to simplify downstream analysis.
With Kat Holt at the Bio21 Institute in Melbourne, Australia, I analysed the genetics of plasmids in bacteria using a network based approach. This was based on the observation that even among closely related plasmids, their shared genetic content wildly differs, and can easily become mixed up with the genes of the host bacteria. Scripts for running and visualising this yourself is available on GitHub.
In the lab of Mirana Ramialison at the Australian Regenerative Medicine Institute in Clayton, Australia, I explored how heart transcription data could be interactively visualised in 3D. This visualisation can be run in a web browser, and is accessible online at 3D Cardiomics. We also have a paper out on bioRxiv.