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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

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Related Experiment Video

Updated: Apr 6, 2026

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

DYVIPAC: an integrated analysis and visualisation framework to probe multi-dimensional biological networks.

Lan K Nguyen¹, Andrea Degasperi¹, Philip Cotter¹

¹Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.

Scientific Reports

|July 30, 2015

Summary

This study introduces DYVIPAC, a novel framework using parallel coordinates to visualize complex, high-dimensional biochemical network dynamics. It aids understanding of disease mechanisms by offering a multi-dimensional view of system behavior.

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Last Updated: Apr 6, 2026

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Published on: November 7, 2025

Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

Biochemical networks are complex, multi-dimensional systems crucial for cellular functions.
Disease, like cancer, often results from altered network dynamics due to genetic changes.
Current methods for analyzing network dynamics offer limited low-dimensional views, hindering comprehensive understanding.

Purpose of the Study:

To develop an integrated analysis and visualization framework for high-dimensional biochemical network behavior.
To overcome limitations of current methods in presenting multi-dimensional system dynamics.
To facilitate a better understanding of disease mechanisms and therapeutic strategies.

Main Methods:

Developed a novel framework named "Dynamics Visualisation based on Parallel Coordinates" (DYVIPAC).
Utilized parallel coordinates graphs to analyze and visualize high-dimensional network dynamics.
Applied the framework to various signaling networks with diverse topological and dynamic properties.

Main Results:

Demonstrated the applicability and utility of the DYVIPAC framework across different network types.
Showcased the framework's ability to provide an integrated understanding of complex systems behavior.
Successfully visualized multi-dimensional network dynamics that are often missed by conventional methods.

Conclusions:

DYVIPAC offers an efficient and reliable method for analyzing and visualizing high-dimensional network dynamics.
The framework enhances the understanding of system behavior in both health and disease states.
This approach is valuable for elucidating disease mechanisms and informing therapeutic strategy development.