(619h) Predicting Coupled Expression Dynamics in Bacterial Operons

The operon is the central architectural unit of all natural and engineered genetic systems, but we do not yet have an accurate sequence-to-function model that predicts how transcription, translation, and mRNA stability collectively control its expression dynamics. Here, we have developed and experimentally validated a nucleotide-resolution biophysical model of operon expression dynamics that enables the accurate prediction of translation rate, mRNA stability, and polarity for arbitrary operon sequences. To develop this model, we characterized the mRNA and protein levels from over 100 rationally designed operons to quantify the sequence determinants of mRNA stability and their coupling with translation. We show that (1) increasing RBS translation rates by 1000-fold improves mRNA stability by 11.3-fold; (2) increasing unstructured RNA in the 5’ UTR can reduce mRNA stability by 6.1-fold; and (3) increasing unstructured RNA in CDSs decreases mRNA stability by 3.6-fold. We then created a nucleotide-resolution Markov model to simulate the stochastic dynamics of transcription (elongation, and RNAP fall-off), translation (initiation, elongation, termination), and mRNA decay (RNAse E, G, III, RNAse II, PNPase, RppH, and PAPI) to calculate a mRNA transcript’s translational efficiency and half-life, and provide a visual illustration of its expression dynamics. These results will enable the reverse-engineering of natural or engineered operons, and a comprehensive redesign to achieve desired mRNA and protein expression levels.