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Philip Fowler

University Research Lecturer

Predicting AMR

Research Summary

My research focusses on mitigating antimicrobial resistance, with a current focus on tuberculosis. 

1. Improve the quality of phenotype measurements collected by the international TB consortium, CRyPTIC. This project is tests the antibiotic susceptibility of >25,000 clinical strains collected worldwide using a commercial 96-well microtitre plate. In addition to the measurements carried out in the partner laboratories, a photo of each plate is read by some software, AMyGDA, that I developed. I also run BashTheBug which is a Citizen Science project that invites anyone to help us classify especially difficult images. The large dataset collected by CRyPTIC will not only enable the construction of a more accurate tuberculosis resistance catalogue but also will open up new, interesting avenues of AMR research.

2. Develop de novo methods that can predict the effect of protein mutations on a specific antibiotic. I approach this problem from a protein structure perspective; to date we have applied molecular simulation and machine learning methods to predict AMR.


AMyGDA

Example of photograph of 96-well plate processed by AMyGDA © PWF 2018
Example of photograph of 96-well plate processed by AMyGDA

BashTheBug

Screengrab of BashTheBug Citizen Science project; for more info please click link in the Websites widget. © PWF 2018
Screengrab of BashTheBug Citizen Science project; for more info please click link in the Websites widget.