# Using cellular fluctuations and computational analyses to probe biological mechanisms

> **NIH NIH R35** · COLORADO STATE UNIVERSITY · 2021 · $322,682

## Abstract

Project Summary
 New experimental approaches in single‐cell imaging and sequencing are producing an unprecedented
amount of data to quantify the intricate dynamics of biomedical processes. These processes are subject to the
intertwined issues of complexity and randomness, and it can be difficult for medical professionals to interpret,
understand or act on this data. In particular, spatial, temporal and stochastic fluctuations in cellular processes
introduce huge uncertainties that compromise responses, complicate modeling, and make predictive
understanding seemingly impossible. We hypothesize that the fluctuations and heterogeneities of single‐cell
dynamics can contain powerful information resources that can be unlocked with improved computational methods
and integrated experiment designs. This project will create these tools and use them to study the dynamics of
Mitogen–Activate Protein Kinase signaling and downstream regulation for multiple genes in multiple organisms.
We will integrate state‐of‐the‐art single‐cell‐single‐molecule super‐resolution microscopy experiments with novel
discrete stochastic analysis methods and seek to unlock the mysteries of (1) How do MAPK signals and
transcription factors interact in space and time to differentially control expression of multiple genes in response to
different external stresses and (2) How do mRNA sequences, protein regulators, and ribosomes interact to affect
the natural and aberrant dynamics of translation activation, initiation, elongation and termination?
 We will also create a set of advanced computational tools and build them into a user‐friendly software
package (the Stochastic System Identification Toolkit, SSIT), which will enable the systematic integration of
discrete stochastic modeling approaches with single‐cell experiment techniques. We will build the SSIT to
accomplish crucial tasks in the design, interpretation, prediction, and control of single‐cell experiments. To
guarantee the broadest possible impact, the SSIT will be validated in direct collaboration with at least four of the
nation’s top single‐cell experimental groups in bacteria, yeast, insect, and human research. Once validated, all SSIT
tools will be made publically available, and the theory, algorithms and techniques will be taught to scores of
graduate students, postdocs, and other young biomedical researchers at Colorado State University, Vanderbilt
University, UC Berkeley, and Los Alamos National laboratory as well as at the NIGMS‐funded q‐bio Summer School,
an internationally recognized program organized by the PI and held annually at the CSU. Our long‐term goal is to
make systematic and rigorous computational modeling an accessible and standard practice for biological and
biomedical research laboratories around the world. Successful completion of our goal will broadly support NIH
mission areas to seek predictive knowledge about the nature and behavior of living systems; to enable more rapid
and cost effective discove...

## Key facts

- **NIH application ID:** 10240469
- **Project number:** 5R35GM124747-05
- **Recipient organization:** COLORADO STATE UNIVERSITY
- **Principal Investigator:** Brian Munsky
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $322,682
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10240469

## Citation

> US National Institutes of Health, RePORTER application 10240469, Using cellular fluctuations and computational analyses to probe biological mechanisms (5R35GM124747-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10240469. Licensed CC0.

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