Year: 2019
Type: article
Abstract: Advances in fluorescence microscopy have introduced new assays to quantify live-cell translation dynamics at single-RNA resolution. We introduce a detailed, yet efficient sequence-based stochastic model that generates realistic synthetic data for several such assays, including Fluorescence Correlation Spectroscopy (FCS), ribosome Run-Off Assays (ROA) after Harringtonine application, and Fluorescence Recovery After Photobleaching (FRAP). We simulate these experiments under multiple imaging conditions and for thousands of human genes, and we evaluate through simulations which experiments are most likely to provide accurate estimates of elongation kinetics. Finding that FCS analyses are optimal for both short and long length genes, we integrate our model with experimental FCS data to capture the nascent protein statistics and temporal dynamics for three human genes: KDM5B, β-actin, and H2B. Finally, we introduce a new open-source software package, RNA Sequence to NAscent Protein Simulator (rSNAPsim), to easily simulate the single-molecule translation dynamics of any gene sequence for any of these assays and for different assumptions regarding synonymous codon usage, tRNA level modifications, or ribosome pauses. rSNAPsim is implemented in Python and is available at: https://github.com/MunskyGroup/rSNAPsim.git.
Source: PLoS Computational Biology
Institutions: Colorado State University, Tokyo Institute of Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,350
Awards: G4122075540, NRT-DESE: Generating, Analyzing, and Understanding Sensory and Sequencing Information--A Trans-Disciplinary Graduate Training Program in Biosensing and Computational Biology, Integrating Multi-Scale Imaging, Reaction-Diffusion Simulation, and Markov Model Inference to Enhance Predictive Design and Interpretation of Single-Molecule Gene Regulation Experiments
OpenAlex ID: W2980752160