Deep Learning Takes on Synthetic Biology

The collaboration between data scientists from the Wyss Institute's Predictive BioAnalytics Initiative and synthetic biologists in Wyss Core Faculty member Jim Collins' lab at MIT was created to apply the computational power of machine learning, neural networks, and other algorithmic architectures to complex problems in biology that have so far defied resolution. As a proving ground for their approach, the two teams focused on a specific class of engineered RNA molecules: toehold switches, which are folded into a hairpin-like shape in their "off" state. When a complementary RNA strand binds to a "trigger" sequence trailing from one end of the hairpin, the toehold switch unfolds into its "on" state and exposes sequences that were previously hidden within the hairpin, allowing ribosomes to bind to and translate a downstream gene into protein molecules. This precise control over the expression of genes in response to the presence of a given molecule makes toehold switches very powerful components for sensing substances in the environment, detecting disease, and other purposes.