# Integrated visualization, control, and analysis of GEF – GTPase networks in living cells

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $499,156

## Abstract

A small number of Rho family GTPases participate in a broad array of fundamental cellular behaviors.
Specificity is possible due to spatial and temporal control of GTPase “activation”; Guanine exchange factors
(GEFs) generate activated, GTP-bound GTPases with precise timing and localization, while specialized
interactions with adhesion molecules, membrane domains and other localized structures specify GEF-GTPase
interactions. GEF/GTPase circuits are complex, with localized feedbacks, multiple GEFs controlling one
GTPase, and vice versa. To dissect this spatiotemporally regulated circuitry requires imaging, and new
analytical techniques that can dissect causal relationships from imaging data. Following the intentions of PAR-
19-158 (Bioengineering Research Grants), we propose a multidisciplinary collaboration leveraging organic
chemistry, protein engineering, imaging, and computer science to fudnamentally advance signal transduction
imaging and analysis. As a biological testbed we will explore the role of GEF-GTPase interactions in cell
protrusion, single cell migration and collective migration. We will develop a generalizable approach to GEF
biosensors, and adapt our proven GTPase biosensors to image GEF and GTPase activities in the same cell.
Because GEF-GTPase interactions are heterogeneous and complex, multiplexed imaging is necessary to
quantify their relative dynamics. However, perturbation of cell behavior is especially problematic when using
two biosensors in the same cell. We will therefore develop new biosensor designs that greatly reduce cell
perturbation. Even the most precise imaging of overlapping molecular activations has not revealed causal
relationships. We will therefore adopt the framework of Granger Causality inference, which was originally
devised for financial market analysis, to extract causal connections and feedback interactions from imaging
data. Numerous steps will be necessary to translate the existing concepts of Granger causality to the analysis
of spatially and temporally distributed molecular processes. Most importantly, we will implement a schema for
Granger causality inference in multivariate time series models that will capture spatial relations, and we will
combine principles of high-dimensional statistical regression with approaches from control theory to estimate
information flows between variables that are coupled by strong feedbacks. We will also develop a novel
clustering approach that preserves the neighborhood topology of data in a high-dimensional feature space and
in the Euclidian space of the cell outline to identify signaling microdomains. Finally, to test and confirm our
hypotheses, we will use new photo-activatable and photo-inhibitable analogs of GEFs together with GTPase
biosensors to control one protein while observing another. This research plan will produce biosensors with
reduced perturbation, biosensor/optogenetic multiplexing capabilities, and image analysis/modeling
approaches necessary to ...

## Key facts

- **NIH application ID:** 10830296
- **Project number:** 5R01CA252826-04
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Gaudenz Danuser
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $499,156
- **Award type:** 5
- **Project period:** 2021-05-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10830296

## Citation

> US National Institutes of Health, RePORTER application 10830296, Integrated visualization, control, and analysis of GEF – GTPase networks in living cells (5R01CA252826-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10830296. Licensed CC0.

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