Sequence-to-function deep learning frameworks for engineered riboregulators

Jacqueline A. Valeri; Katherine M. Collins; Pradeep Ramesh; Miguel A. Alcantar; Bianca A. Lepe; Timothy K. Lu; Diogo M. Camacho

doi:10.1038/s41467-020-18676-2

Nature Communications (Oct 2020)

Sequence-to-function deep learning frameworks for engineered riboregulators

Jacqueline A. Valeri,
Katherine M. Collins,
Pradeep Ramesh,
Miguel A. Alcantar,
Bianca A. Lepe,
Timothy K. Lu,
Diogo M. Camacho

Affiliations

Jacqueline A. Valeri: Wyss Institute for Biologically Inspired Engineering, Harvard University
Katherine M. Collins: Wyss Institute for Biologically Inspired Engineering, Harvard University
Pradeep Ramesh: Wyss Institute for Biologically Inspired Engineering, Harvard University
Miguel A. Alcantar: Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology
Bianca A. Lepe: Wyss Institute for Biologically Inspired Engineering, Harvard University
Timothy K. Lu: Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology
Diogo M. Camacho: Wyss Institute for Biologically Inspired Engineering, Harvard University

DOI: https://doi.org/10.1038/s41467-020-18676-2
Journal volume & issue: Vol. 11, no. 1
pp. 1 – 14

Abstract

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The design of synthetic biology circuits remains challenging due to poorly understood design rules. Here the authors introduce STORM and NuSpeak, two deep-learning architectures to characterize and optimize toehold switches.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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