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Sequencing Enabling Design and Learning in Synthetic Biology
Version 1
: Received: 16 February 2020 / Approved: 17 February 2020 / Online: 17 February 2020 (07:41:52 CET)
Version 2 : Received: 22 April 2020 / Approved: 23 April 2020 / Online: 23 April 2020 (03:47:02 CEST)
Version 2 : Received: 22 April 2020 / Approved: 23 April 2020 / Online: 23 April 2020 (03:47:02 CEST)
How to cite: Gilliot, P.; Gorochowski, T.E. Sequencing Enabling Design and Learning in Synthetic Biology. Preprints 2020, 2020020243 Gilliot, P.; Gorochowski, T.E. Sequencing Enabling Design and Learning in Synthetic Biology. Preprints 2020, 2020020243
Abstract
The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de novo biological design. Here, we show how sequencing is supporting the creation of a new wave of biological parts and systems, as well as providing the vast data sets needed for the machine learning of design rules for predictive bioengineering. However, we believe this is only the tip of the iceberg and end by providing an outlook on recent advances that will likely broaden the role of sequencing in synthetic biology and its deployment in real-world environments.
Keywords
sequencing; omics; synthetic biology; systems biology; machine learning; biological design
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Thomas Gorochowski
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