Working Paper Review Version 2 This version is not peer-reviewed

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)

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.

Subject Areas

sequencing; omics; synthetic biology; systems biology; machine learning; biological design

Comments (1)

Comment 1
Received: 23 April 2020
Commenter: Thomas Gorochowski
Commenter's Conflict of Interests: Author
Comment: Inclusion of new figure and correction of minor typos.
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