In silico design and automated learning to boost next-generation smart biomanufacturing.
In silico design and automated learning to boost next-generation smart biomanufacturing.
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DOI：
10.1093/synbio/ysaa020
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Scrutton NS
中科院分区：
文献类型：
--
作者：
Carbonell P;Le Feuvre R;Takano E;Scrutton NS
对从废物或可持续来源生产的生物基化合物的需求不断增长，这推动了生物制造业提供新一代原型生物制造平台。在曼彻斯特和地球仪（全球生物制品联盟）的SYNBIOCHEM等中心，设计、构建、测试和学习（DBTL）步骤的集成和自动化有助于缩短从初始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，最近开发了一套材料单体的生产菌株组合，其中一些接近工业滴度，这是曼彻斯特中心在不到90天内完成的杰作。新的计算机设计工具为DBTL管道的前端做出了重大贡献。与此同时，现代生物制造业的深远举措正在产生大量的高维数据和知识，这些数据和知识可以通过自动化学习进行整合，以加快DBTL周期。在这个角度来看，新的设计工具和学习组件作为下一代自动化生物制品的使能技术的作用进行了讨论。未来的生物加工厂将在完全自动化的DBTL周期下运行，由计算机优化实验规划、完整的生物制造设备连接、虚拟化平台和基于云的设计驱动。机器人构建工作列表的自动生成和机器学习算法的集成将共同实现高水平的适应性和快速的设计更改，以实现全自动智能生物制造。
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 tour de force by the Manchester Centre that was achieved in less than 90 days. New in silico 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 in silico 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.
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影响因子：
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作者：
Cho A;Yun H;Park JH;Lee SY;Park S
通讯作者：
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影响因子：
2.7
作者：
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