Engineering biology and automation–replicability as a design principle
Author(s)
Bultelle, Matthieu
Casas, Alexis
Kitney, Richard
Type
Journal Article
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.
Date Issued
2024-12-01
Date Acceptance
2024-07-07
Citation
Engineering Biology, 2024, 8 (4), pp.53-68
ISSN
2398-6182
Publisher
Institution of Engineering and Technology (IET)
Start Page
53
End Page
68
Journal / Book Title
Engineering Biology
Volume
8
Issue
4
Copyright Statement
© 2024 The Author(s). Engineering Biology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39734660
PII: ENB212035
Subjects
automation
bioinformatics
synthetic biology
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2024-07-12