# Integrating Multi-Scale Imaging, Reaction-Diffusion Simulation, and Markov Model Inference to Enhance Predictive Design and Interpretation of Single-Molecule Gene Regulation Experiments > **NIH NIH R35** · COLORADO STATE UNIVERSITY · 2024 · $334,400 ## Abstract Project Summary Single-cell imaging can quantify intricate spatial and temporal dynamics of gene regulation that underly important biomedical process ranging from bacterial infections to cancer. This gene regulation is subject to complexities and randomness of biological processes, and its observation is further subject to measurement artifacts due to inefficiencies in biochemical labels and distortions in microscope imaging. Yet, despite these complications, preliminary work shows that it is possible to integrate data and computational models to predict gene regulation in myriad environmental and genetic conditions provided that: (1) models must be constrained by informative and reproducible data, (2) models must be rigorously verified to account for biological and technical variations, and (3) models must be systematically explored to quantify uncertainties. The overarching hypothesis of this project is that spatial and temporal fluctuations observed in subcellular dynamics contain unique information that can be unlocked with improved computational methods and model-guided experiments. To test this hypothesis, this project will create a new research platform to be known as the single-cell Graphical Utility to Interpret and Design Experiments. scGUIDE will combine experimental analysis (e.g., image processing and single-particle tracking to extract quantitative data from fluorescence microscopy experiments), spatial stochastic simulation (e.g., reaction-diffusion models to generate realistic videos to mimic cellular experiments), model abstraction and identification (e.g., parameter inference and uncertainty quantification to translate quantitative observations into predictive insight), and experiment design (e.g., statistical methods to pinpoint which specific experimental conditions are most likely to reveal new biological insight). To demonstrate its broad capabilities, scGUIDE will be used to analyze and design single-cell experiments for four different health-related processes. In yeast, the project will examine the coordination between stress-activated MAPK dynamics and Spt-Ada-Gcn5 Acetyltransferase (SAGA) subunits that control chromatin and RNA transcription/transport dynamics, and which have been implicated in carcinoma, skeletal dysplasia, and retinal degeneration. In human cells, the project will examine the spatiotemporal clustering and phosphorylation of RNAP Polymerase II as it engages in single-gene transcription under CDK-inhibitor cancer treatments. In osteosarcoma cells, the project will explore how competition for local tRNA resources affects translation of single-mRNA molecules in different sub-cellular regions and in human and viral contexts. Finally, the project will explore the effects that epigenetic memory and molecular competition have on the multi- generational activation or repression of the pap operon that allows E. coli to establish uropathogenic infections. Each project will build mechanistic and quantitatively p... ## Key facts - **NIH application ID:** 10933434 - **Project number:** 5R35GM124747-08 - **Recipient organization:** COLORADO STATE UNIVERSITY - **Principal Investigator:** Brian Munsky - **Activity code:** R35 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $334,400 - **Award type:** 5 - **Project period:** 2017-09-15 → 2027-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10933434 ## Citation > US National Institutes of Health, RePORTER application 10933434, Integrating Multi-Scale Imaging, Reaction-Diffusion Simulation, and Markov Model Inference to Enhance Predictive Design and Interpretation of Single-Molecule Gene Regulation Experiments (5R35GM124747-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10933434. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*