DMS/NIGMS 1: Decoding RNA splicing dynamics from static images using mechanistic stochastic modeling

About This Grant

This project investigates splicing, a fundamental cellular process where RNA molecules from the same gene are processed to produce multiple distinct proteins. This flexibility is essential for healthy development, and splicing errors are linked to numerous human diseases, including cancer and neurodegenerative disorders. A major obstacle in understanding splicing control is that traditional live-cell imaging interferes with the very dynamics under observation. This research overcomes this limitation through a tightly integrated experimental-theoretical approach that infers molecular dynamics from spatial patterns in static, high-resolution cellular images. By combining advanced microscopy with predictive mathematical modeling, the investigators will uncover the rules governing RNA splicing. This work will advance biological understanding and national health by providing deeper, quantitative insights into gene regulation to inform future therapies. The project also trains interdisciplinary scientists and produces open-source software for the research community. This research quantifies the kinetic rates governing RNA fate, including co-transcriptional splicing, post-transcriptional splicing, and degradation. The project's central strategy is the close, iterative integration of experimental imaging and mechanistic modeling to infer these dynamics from static, single-molecule images. Experimentally, fluorescence in-situ hybridization (FISH) generates high-resolution spatial maps of RNA isoforms that directly inform and validate computational models. Computationally, this project develops novel spatial stochastic models based on reaction-diffusion processes to describe RNA dynamics within spatially heterogeneous cellular environments. Bespoke inference pipelines will connect these models to imaging data for robust parameter estimation and hypothesis testing between competing splicing mechanisms. This synergistic approach will yield dual impacts for biology and mathematics, providing biological resolution to RNA splicing dynamics while establishing new mathematical theory and a powerful paradigm for inferring dynamics from static data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.