We develop and utilize bioinformatic approaches to monitor and combat the spread of bacterial pathogens.
For more information, go to microbe.dev to see our group website.
(Meta)genomic sequencing is playing an increasingly pivotal role in clinical and public health microbiology. As such, the amount of publicly available (meta)genomic data derived from microbes — including pathogens — is growing rapidly. As a computational microbiology group, we develop and utilize bioinformatic approaches, which can leverage these massive data sets to improve pathogen surveillance, source tracking, outbreak detection, and risk evaluation efforts.
We develop and deploy:
(i) phylodynamic models, which can be used to track the evolution and transmission of zoonotic pathogens between animal reservoirs and the human population;
(ii) machine learning approaches, which can be used to identify microbial genomic determinants associated with phenotypes of clinical and industrial importance (e.g., host disease states, antimicrobial resistance phenotypes); and
(iii) multi-omics methods, which can be used to predict pathogen virulence potential. We are also passionate about bridging the gap between experimental and computational microbiologists by making (meta)genomic data analysis methods accessible and approachable to all through teaching and outreach.
The research is financed by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science, DDLS.