iVUN: interactive Visualization of Uncertain biochemical reaction Networks

Area of Science:

• Systems Biology
• Computational Biology
• Biochemistry

Background:

• Mathematical models are essential for understanding complex biochemical reaction networks.
• Parameter estimation integrates diverse data, but measurement noise necessitates uncertainty consideration.
• Uncertainties in model parameters impact the interpretation of reaction fluxes and species dynamics.

Purpose of the Study:

• To develop a visual analytics system (iVUN) for uncertainty-aware analysis of biochemical reaction networks.
• To support the investigation of static and dynamic attributes within these models.
• To facilitate a holistic understanding of biological processes by accounting for parameter uncertainties.

Main Methods:

• Developed the iVUN visual analytics system for ordinary differential equation models.
• Visualized multivariate graphs using node-link diagrams with color-mapped attribute statistics.
• Integrated linked views including line plots, scatter plots, and correlation matrices for uncertainty analysis.

Main Results:

• iVUN enables quantitative analysis of attribute dynamics and uncertainties in biochemical networks.
• The system effectively visualizes uncertainties and their time dependencies.
• Demonstrated application on a model for Epo-induced JAK2/STAT5 signaling.

Conclusions:

• iVUN facilitates in-depth studies of biochemical networks, including attribute uncertainties and correlations.
• Linking diverse visualization options is highly beneficial for complex biological system analysis.
• The system aids in drawing more robust conclusions from model-based biological research.