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Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

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Related Experiment Video

Updated: May 5, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

Published on: July 18, 2013

Reconstructing gene network structure and dynamics from single cell data.

Feng Chen^1,2, Chunhe Li^1,2,3

¹Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.

Bioinformatics (Oxford, England)

|November 3, 2025

Summary

GGANO, a novel framework, accurately infers gene regulatory networks (GRNs) from noisy single-cell data using Gaussian Graphical Models and Neural Ordinary Differential Equations. It reveals crucial insights into cell fate decisions and disease progression.

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Last Updated: May 5, 2026

Acquiring Fluorescence Time-lapse Movies of Budding Yeast and Analyzing Single-cell Dynamics using GRAFTS

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Published on: July 18, 2013

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Systems Biology
Computational Biology
Genomics

Background:

Gene regulatory networks (GRNs) govern biological functions.
Inferring GRNs from high-dimensional, noisy single-cell data is challenging.
Existing methods lack robustness and interpretability for complex biological processes.

Purpose of the Study:

To develop a robust and interpretable framework for GRN inference.
To enable dynamic modeling and inference from single-cell data.
To uncover regulatory mechanisms in cell fate decisions and diseases.

Main Methods:

Proposed GGANO, a hybrid framework integrating Gaussian Graphical Models (GGMs) and Neural Ordinary Differential Equations (NODEs).
GGMs for conditional independence learning.
NODEs for dynamic modeling and inference.

Main Results:

GGANO demonstrated superior accuracy and stability over existing methods, especially under high-noise conditions.
Enabled inference of stochastic dynamics from single-cell data.
Identified intermediate cellular states and key regulatory genes in Epithelial-Mesenchymal Transition (EMT).

Conclusions:

GGANO offers a powerful new approach for GRN inference from single-cell data.
The framework enhances understanding of complex biological processes like cell fate decisions and diseases.
GGANO provides a valuable tool for systems biology research.