NUS-Singapore (iGEM 2023 Finalist Presentation) - Undergrad 2nd Runner Up
Video from: iGEM 2023 Grand Jamboree Final Day Show: https://jamboree.igem.org/2023
Optimised Technique For swiTch Engineering and Ranking (OTTER)
RNA switches are robust and versatile tools in various fields including bioproduction, drug delivery, and disease detection. However, the difficulty in designing the switches hinders its wide applications. A key reason that makes the design process challenging is that the interaction between RNAs are hardly predictable using currently models/tools. Furthermore, there lacks available quality RNA data for developing the models.
In our project, we sought to develop OTTER, a tool that is designed to predict effectiveness of RNA switches and develop a new high throughput workflow (SIGNAL) and novel experimentally validated bioparts to facilitate the generation of large RNA library and data. To this end, we built new bioparts and focus on developing deep learning to model RNA-RNA interactions coupled with generative model to suggest RNA switch designs for OTTER. We use RNA switch systems such as STARs and Toehold as our model system.
