Using synthetic biology and machine learning tools to characterize and engineer RNAs

Regulatory RNAs enable bacteria to dynamically respond to stresses caused by changes in environmental conditions. Specifically, bacterial small RNAs, a class of RNA regulators, exert dynamic control on complex networks by regulating gene expression. Understanding their functions is a goal in both medicine and metabolic engineering given their relevance to pathogenesis and their potential to manage global regulatory networks that affect biological production of industrially-relevant compounds. Given the importance of molecular structure to RNA functioning, fundamental sRNA characterization and applied engineering efforts  depend heavily on the understanding and design of their specific shapes. Specifically, knowledge of the RNA structural landscape supports identification of interfaces relevant to regulation. In this talk, we will describe the development and application of tools that allow in vivo characterization of thousands of potential interacting interfaces in RNA molecules, as determined based on their molecular accessibility. We will describe how insights obtained from these synthetic probing approaches can be used in the functional characterization of newly discovered RNAs and in the rational design of bacterial sRNAs to achieve a tunable gradient of global control for metabolic engineering applications.