BME PhD Dissertation Defense: Aidan Riley

Title: "Generative RNA Design"

Advisory Committee: Alexander Green, PhD – BME (Research Advisor) Wilson Wong, PhD – BME (Chair) Mark Grinstaff, PhD – BME Brian Cleary, PhD – BME Pawel Przytyscki, PhD – CDS

Abstract: Ribonucleic acid (RNA) has emerged as one of the most potent substrates for engineering biology. From the development of vaccines on unprecedented timelines to the construction of programmable logic operators that function in vivo, the engineering applications of RNA are immense. Compared to analogous systems constructed from either DNA or protein, the ability to encode genetic information, execute diverse enzymatic functions, and ease of laboratory synthesis provide RNA several distinct advantages across synthetic biology applications. Still, the complex relationship between the sequence and function of RNA molecules introduces the need to conduct high-throughput screening experiments and leverage data-driven design tools to accelerate the deployment of these systems. In this work we present a toolkit of generative algorithms that learn from experimental data to allow functionally informed design of diverse RNA classes. These methods prioritize the data and computing constraints inherent to biotechnology research, offering an alternative approach to foundation modeling techniques. Through the use of these tools we achieve several RNA design milestones, including the design of synthetic RNA switches that outperform thermodynamic tools, the generation of functional RNAs that outperform encountered training examples, effective generative modelling with as few as 20 labelled examples, the first successful generation of pseudoknotted RNA structures, and the first tool capable of generating complete, functionally informed messenger RNA molecules. By prioritizing interpretability in these frameworks, we uncover new biological principles governing the performance of coding and non-coding messenger RNA components when assembled into complete transcripts, allowing us to generalize high performance designs to unobserved biological contexts. These results converge to a toolkit that efficiently designs and assembles RNA systems, and could greatly accelerate their deployment across many contexts.