Deep learning makes synthetic biology comprehensible
October 2020
Massachusetts Institute of Technology, Cambridge, USA
A set of machine learning algorithms was developed, that can analyse reams of RNA-based "toehold" sequences and predict which ones will be most effective at sensing and responding to a desired target sequence. These achievements could be helpful to better understand the fundamental principles of RNA folding. This method demonstrates the power of combining computational with synthetic biology to develop new and more powerful bioinspired technologies. It also leads to new insights into the fundamental mechanisms of biological control. The algorithms could be applied to other problems in synthetic biology and could accelerate the development of biotechnological tools.
A deep learning approach to programmable RNA switches
James J. Collins
Added on: 11-11-2020
[1] https://www.nature.com/articles/s41467-020-18677-1[2] https://www.technologynetworks.com/informatics/news/deep-learning-gets-a-toehold-on-synthetic-biology-341383
