Design/Build/Test/Learn: novel antibiotic discovery using a plug-and-play robotic platform

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The project combines research areas pioneered by the supervising team, in a unique interdisciplinary approach: advanced genome editing; cell-free production systems; predictive computational modelling; 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 polyketide drugs (antimicrobials and anticancer agents). The work will have an integrated approach based on an iterative design - build - test - learn cycle in several rounds during the lifetime of the project. Key approaches will be:Identify and express several minimal units required to produce core type II polyketide structures in E. coli identified computationally from genome sequences and analyse the obtained chemical structure.Model-driven disruption using genome editing using genome-scale models of E. coli metabolism to predict a set of enzyme-coding genes as targets for disruption or overexpression.Statistical learning to optimize genome editing strategies.