dc.contributor.author | Angenent-Mari, Nicolaas M | |
dc.contributor.author | Garruss, Alexander S | |
dc.contributor.author | Soenksen, Luis R | |
dc.contributor.author | Church, George | |
dc.contributor.author | Collins, James J | |
dc.date.accessioned | 2021-10-27T20:31:08Z | |
dc.date.available | 2021-10-27T20:31:08Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/136157 | |
dc.description.abstract | Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology. 2 2 | en_US |
dc.language.iso | en | |
dc.publisher | Springer Science and Business Media LLC | en_US |
dc.relation.isversionof | 10.1038/S41467-020-18677-1 | en_US |
dc.rights | Creative Commons Attribution 4.0 International license | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.source | Nature | en_US |
dc.title | A deep learning approach to programmable RNA switches | en_US |
dc.type | Article | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | |
dc.contributor.department | Massachusetts Institute of Technology. Institute for Medical Engineering & Science | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | |
dc.contributor.department | Harvard University--MIT Division of Health Sciences and Technology | |
dc.relation.journal | Nature Communications | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dc.date.updated | 2021-08-25T18:06:33Z | |
dspace.orderedauthors | Angenent-Mari, NM; Garruss, AS; Soenksen, LR; Church, G; Collins, JJ | en_US |
dspace.date.submission | 2021-08-25T18:06:36Z | |
mit.journal.volume | 11 | en_US |
mit.journal.issue | 1 | en_US |
mit.license | PUBLISHER_CC | |
mit.metadata.status | Authority Work and Publication Information Needed | |