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工程生物学与自动化——作为一种设计原则的可重复性

Engineering biology and automation-Replicability as a design principle.

作者信息

Bultelle Matthieu, Casas Alexis, Kitney Richard

机构信息

Department of Bioengineering Imperial College London London UK.

出版信息

Eng Biol. 2024 Jul 12;8(4):53-68. doi: 10.1049/enb2.12035. eCollection 2024 Dec.

Abstract

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.

摘要

工程生物学中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、当前计算模型预测能力有限以及为解决该学科有望解决的重大难题而探索非常大的设计空间的直接结果。自动化已被提议作为对生物样本进行大量操作的关键组成部分。这是因为它与更高的通量以及更高的可重复性密切相关(得益于减少了人为干预)。作者关注可重复性,并指出可重复性远非自动化努力的额外负担,而应被视为大规模处理生物样本的自动化流程设计的核心——正如在生物铸造厂中所试验的那样。没有有效的错误控制就不可能实现成功的自动化。本文提出了支持可重复性的IT基础设施的设计原则。最后,作者就工程生物学中自动化的发展给出了一些观点。特别是,他们推测硬件和软件的集成将取得快速进展,并为用户提供比目前所体验到的程度显著更高的对机器人基础设施的控制和抽象水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1ce8/11681252/be8bf832d82e/ENB2-8-53-g001.jpg

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