A deep learning approach to programmable RNA switches

Nicolaas M. Angenent-Mari; Alexander S. Garruss; Luis R. Soenksen; George Church; James J. Collins

doi:10.1038/s41467-020-18677-1

Nature Communications (Oct 2020)

A deep learning approach to programmable RNA switches

Nicolaas M. Angenent-Mari,
Alexander S. Garruss,
Luis R. Soenksen,
George Church,
James J. Collins

Affiliations

Nicolaas M. Angenent-Mari: Department of Biological Engineering, Massachusetts Institute of Technology (MIT)
Alexander S. Garruss: Wyss Institute for Biologically Inspired Engineering, Harvard University
Luis R. Soenksen: Department of Biological Engineering, Massachusetts Institute of Technology (MIT)
George Church: Wyss Institute for Biologically Inspired Engineering, Harvard University
James J. Collins: Department of Biological Engineering, Massachusetts Institute of Technology (MIT)

DOI: https://doi.org/10.1038/s41467-020-18677-1
Journal volume & issue: Vol. 11, no. 1
pp. 1 – 12

Abstract

Read online

RNA can be used as a programmable tool for detection of biological analytes. Here the authors use deep neural networks to predict toehold switch functionality in synthetic biology applications.

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/

About the journal