CAREER: Uncovering Quantitative Design Principles of RNA Regulators for Synthetic Biology

The overall goal of this CAREER proposal is to build an integrated research and education program focused on uncovering quantitative design principles that link RNA sequence, structure, and function, and to use these principles to design synthetic RNAs that can precisely regulate gene expression. The education plan focuses on integrating research into the training of the next generation of synthetic biology students and teachers, and informing and exciting the broader public about synthetic biology. Small RNAs (sRNAs) are an abundant class of natural genetic regulators that have found wide use in a broad array of synthetic biology applications. RNAs are potentially powerful substrates for cellular engineering because of their functional diversity, the close coupling of RNA structure and function, and the availability of tools that can characterize RNA properties at an ‘omics’ level. We have yet to fully realize this potential though, as we still lack a quantitative understanding of the design principles that link an sRNA’s structure to its regulatory function, and how we can use these principles to predictably control gene expression. The PI’s laboratory recently created a new synthetic sRNA mechanism called Small Transcription Activating RNAs, or STARs. STARs are hypothesized to allow the construction of unique RNA-only logic gates and networks since they represent a brand new function for sRNAs. The central goal of this CAREER proposal is to use STARs as a test-bed for uncovering the quantitative design principles of RNA regulators for synthetic biology. Three specific aims will be pursued: 1) elucidate the molecular-level design principles of STARs; 2) uncover design principles for composing STARs into modules that allow fast genetic decision making; and 3) create circuit-level design rules for integrating STARs into regulatory networks. These objectives will be pursued using two approaches recently developed by the PI that characterize cellular RNA structures and functions, and that rapidly measure RNA circuitry dynamics.