Advancing single cell microbiology and bacteriophage-bacteria interactions using microfluidics and image-based deep learning (Ref: 3976)

Antimicrobial resistance has evolved into a major healthcare threat which is further exacerbated by the diminished number of antibiotics in development. Alternatives to antibiotics such as phages and phage endolysins (their lytic enzymes) are being actively researched. This PhD project aims at understanding sequence-function relationships for endolysin activity and unravel the rules for successful bacteriolysis.

In this project, we will combine cutting-edge high-throughput screening technologies in microfluidic droplets with protein engineering to create an endolysin evolution platform. Microdroplet technology has already shown enormous potential for enzyme evolution and the exploration of sequence space at high-throughput and low cost. Each droplet, whose volume typically ranges from femto to nanoliter scale represents a single reaction in which individual mutant enzymes are confined. By encapsulating a single gene corresponding to a single enzyme variant, large gene libraries can be screened in a time and resource efficient manner. In summary, this is an experimental project to develop novel high-throughput functional assays that can identify novel bacteriolytic enzymes using a combination of microfluidics, optical interrogation setups, protein engineering, gene expression and microbiology.