SBDyNetVis: a visualization tool of dynamic network for systems biology model

Area of Science:

• Systems biology
• Computational biology
• Bioinformatics

Background:

• Analyzing complex molecular interactions requires integrating network structure with dynamic simulation data.
• Traditional tools often present static networks or separate simulation outputs, hindering concurrent interpretation of temporal dynamics and network topology.
• A need exists for integrated platforms that visualize dynamic biological processes effectively.

Purpose of the Study:

• To introduce SBDyNetVis, a novel Python library for creating interactive JavaScript visualizations of biological networks and time-course data.
• To enable intuitive exploration of reaction kinetics, flux equations, and dynamic changes within biological systems.
• To facilitate collaboration and data sharing among researchers through a web-based interface.

Main Methods:

• Developed SBDyNetVis as a Python-based library.
• Integrated Cytoscape.js for interactive JavaScript visualizations.
• Incorporated multiple network layout algorithms and animated node/edge representations.
• Tested conversion of 913 models from BioModels and applied to the IKK-NFκB-IκB reaction case study.

Main Results:

• Successfully converted 913 models from BioModels using SBDyNetVis.
• Demonstrated the tool's utility in visualizing the dynamics of the IKK-NFκB-IκB reaction.
• Created an integrated platform for exploring network structure and temporal dynamics simultaneously.
• Enabled intuitive visualization of reaction kinetics and flux equations.

Conclusions:

• SBDyNetVis effectively bridges the gap between static network representations and dynamic simulation visualizations.
• The library enhances understanding of cellular processes and advances systems biology research.
• Its web-based nature promotes accessibility and collaboration for researchers of all technical levels.