{"title":"工程生物学与自动化--作为设计原则的可复制性","authors":"Matthieu Bultelle, Alexis Casas, Richard Kitney","doi":"10.1049/enb2.12035","DOIUrl":null,"url":null,"abstract":"<p>Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale—as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.</p>","PeriodicalId":72921,"journal":{"name":"Engineering biology","volume":"8 4","pages":"53-68"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enb2.12035","citationCount":"0","resultStr":"{\"title\":\"Engineering biology and automation–Replicability as a design principle\",\"authors\":\"Matthieu Bultelle, Alexis Casas, Richard Kitney\",\"doi\":\"10.1049/enb2.12035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale—as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.</p>\",\"PeriodicalId\":72921,\"journal\":{\"name\":\"Engineering biology\",\"volume\":\"8 4\",\"pages\":\"53-68\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/enb2.12035\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/enb2.12035\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering biology","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/enb2.12035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
工程生物学的应用越来越多地需要对大量生物样本进行操作。这是应用良好工程实践、当前计算模型的预测能力有限以及希望研究超大设计空间以解决该学科有望解决的重大难题的直接结果。自动化被认为是对生物样本进行大量操作的关键组成部分。这是因为自动化与更高的吞吐量和更高的可复制性(由于减少了人工输入)密切相关。作者重点论述了可复制性,并指出,可复制性不仅不会成为自动化工作的额外负担,反而应被视为大规模处理生物样本的自动化流水线设计的核心--正如在生物工厂所试用的那样。没有有效的错误控制,自动化就不可能成功。本文介绍了支持可复制性的 IT 基础设施的设计原则。最后,作者对工程生物学自动化的发展提出了一些看法。特别是,他们推测硬件和软件的整合将取得快速进展,并为用户提供一定程度的控制和机器人基础设施的抽象化,其水平将大大超过今天的经验。
Engineering biology and automation–Replicability as a design principle
Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale—as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.