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

该项目以跨学科的方式结合了监督团队开创的研究领域：先进的基因组挖掘和编辑；表达主机工程；生物合成途径的发现、设计和优化；统计学习策略。它利用了参与小组的高通量机器人、大规模筛选和机器学习能力。本项目的应用案例重点是创建一种优化的大肠杆菌宿主，用于生产II型聚酮类药物（抗菌剂和抗癌剂）。该工作将包括三个紧密相连的工作包，它们将在项目生命周期内的几轮迭代设计-构建-测试-学习循环中相互作用

The project combines research areas pioneered by the supervising team, in an interdisciplinaryapproach: advanced genome mining and editing; expression host engineering; biosynthetic pathwaydiscovery, design and optimisation; and statistical learning strategies. It exploits capabilities in high-throughput robotics, large-scale screening and machine learning available in the participatinggroups.The application case targeted in this project focuses on creating an optimized Escherichia coli hostfor the production of type II polyketide drugs (antimicrobials and anticancer agents). The work willinclude 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