Antibiotic discovery using a DBTL plug-and-play robotics pipeline

The project combines research areas pioneered by the supervising team, in an interdisciplinary
approach: advanced genome mining and editing; expression host engineering; biosynthetic pathway
discovery, design and optimisation; and statistical learning strategies. It exploits capabilities in high-
throughput robotics, large-scale screening and machine learning available in the participating
groups.

The application case targeted in this project focuses on creating an optimized Escherichia coli host
for the production of type II polyketide drugs (antimicrobials and anticancer agents). The work will
include three closely interlinked work packages, which will interact through an iterative design -
build - test - learn cycle in several rounds during the lifetime of the project