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

该项目以一种独特的跨学科方法结合了监督小组开创的研究领域：先进的基因组编辑；无细胞生产系统；预测性计算建模；以及统计学习战略。它利用了参与小组在高通量机器人、大规模筛选和机器学习方面的能力。本项目的应用案例重点是为生产聚酮药物(抗菌剂和抗癌剂)创造一个优化的大肠杆菌宿主。这项工作将在项目生命周期内的几轮迭代设计-建造-测试-学习循环的基础上采取综合方法。主要方法将是：识别和表达在大肠杆菌中产生核心II型聚酮结构所需的几个最小单位，通过计算从基因组序列中识别并分析获得的化学结构。通过基因组规模的大肠杆菌代谢模型来预测一组酶编码基因作为干扰或过度表达的目标的模型驱动的干扰。统计学习来优化基因组编辑策略。

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.