# Deciphering the network structure of signaling dynamics

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $343,875

## Abstract

Project Summary/Abstract
The signaling network involving Ras GTPases and their downstream effectors, particularly the
PI3K and MAPK/ERK pathways, plays important roles in diverse cellular processes including
proliferation, metabolism, migration, and survival. Derangements of the signaling network leads
to diseases such as developmental anomalies, metabolic disorders, and cancer. Despite its
clinical importance, targeting the Ras signaling network for disease treatment has been
challenging due to an incomplete understanding of its complex regulation. Recent studies of the
Ras signaling dynamics at the single-cell level revealed fascinating properties with important
functional implications. In particular, we demonstrated that the Ras signaling network displays
hallmarks of excitable systems such as stochastic activation, traveling waves, and all-or-none
activation. The excitability of the Ras-PI3K-ERK signaling network plays important roles in cell
motility and integration of chemical and mechanical stimuli that regulate cell proliferation.
However, the overall structure of the Ras signaling network that encodes the excitable dynamics
is not known. The purpose of this application is to analyze the structure of the Ras signaling
network by systematically perturbing individual nodes and studying the effects on the excitable
dynamics of the network. To this end we will develop a method based on fluorescent live cell
imaging to simultaneously track a large number of signaling activities. We will use this method
to monitor the excitable responses of ~30 signaling activities when each activity is
pharmacologically inhibited. The effects of perturbations will provide insight into the regulatory
relationship between the signaling activities. We will also carry out network analysis on different
cell types to understand the basis of their distinct responses to small molecule inhibitors. These
studies will pave the way for quantitative models containing sufficient details of the network to
make accurate predictions of cellular responses.

## Key facts

- **NIH application ID:** 10112925
- **Project number:** 5R01GM136711-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Chuan-Hsiang Huang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $343,875
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10112925

## Citation

> US National Institutes of Health, RePORTER application 10112925, Deciphering the network structure of signaling dynamics (5R01GM136711-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10112925. Licensed CC0.

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