Carbonell Pablo, Le Feuvre Rosalind, Takano Eriko, Scrutton Nigel S
Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals (SYNBIOCHEM) and Future Biomanufacturing Research Hub, Manchester Institute of Biotechnology, The University of Manchester, Manchester M1 7DN, UK.
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain.
Synth Biol (Oxf). 2020 Oct 17;5(1):ysaa020. doi: 10.1093/synbio/ysaa020. eCollection 2020.
The increasing demand for bio-based compounds produced from waste or sustainable sources is driving biofoundries to deliver a new generation of prototyping biomanufacturing platforms. Integration and automation of the design, build, test and learn (DBTL) steps in centers like SYNBIOCHEM in Manchester and across the globe (Global Biofoundries Alliance) are helping to reduce the delivery time from initial strain screening and prototyping towards industrial production. Notably, a portfolio of producer strains for a suite of material monomers was recently developed, some approaching industrial titers, in a by the Manchester Centre that was achieved in less than 90 days. New design tools are providing significant contributions to the front end of the DBTL pipelines. At the same time, the far-reaching initiatives of modern biofoundries are generating a large amount of high-dimensional data and knowledge that can be integrated through automated learning to expedite the DBTL cycle. In this Perspective, the new design tools and the role of the learning component as an enabling technology for the next generation of automated biofoundries are discussed. Future biofoundries will operate under completely automated DBTL cycles driven by optimal experimental planning, full biomanufacturing devices connectivity, virtualization platforms and cloud-based design. The automated generation of robotic build worklists and the integration of machine-learning algorithms will collectively allow high levels of adaptability and rapid design changes toward fully automated smart biomanufacturing.
对由废物或可持续资源生产的生物基化合物的需求不断增加，促使生物铸造厂提供新一代的原型生物制造平台。像曼彻斯特的SYNBIOCHEM以及全球范围内（全球生物铸造厂联盟）的中心，将设计、构建、测试和学习（DBTL）步骤进行整合与自动化，有助于缩短从最初的菌株筛选和原型制作到工业生产的交付时间。值得注意的是，曼彻斯特中心最近开发了一系列用于多种材料单体的生产菌株组合，其中一些接近工业滴度，这一成果在不到90天内就实现了。新的设计工具为DBTL流程的前端做出了重大贡献。与此同时，现代生物铸造厂的广泛举措正在产生大量高维数据和知识，这些数据和知识可以通过自动学习进行整合，以加快DBTL循环。在这篇观点文章中，将讨论新的设计工具以及学习组件作为下一代自动化生物铸造厂使能技术的作用。未来的生物铸造厂将在由最佳实验规划、全生物制造设备连接、虚拟化平台和基于云的设计驱动的完全自动化DBTL循环下运行。机器人构建工作列表的自动生成以及机器学习算法的整合将共同实现高度的适应性，并朝着完全自动化的智能生物制造快速进行设计变更。
