MINERVA-a platform for visualization and curation of molecular interaction networks

Area of Science:

• Systems Biology
• Bioinformatics
• Computational Biology

Background:

• Growing knowledge of molecular mechanisms requires structured, accessible data repositories.
• Existing tools lack comprehensive solutions for curating and exploring molecular interaction networks.
• Systems biology research necessitates advanced tools for network visualization and annotation.

Purpose of the Study:

• To develop a platform for creating, curating, and visualizing molecular interaction networks.
• To facilitate the integration of diverse biological data within a systems biology framework.
• To improve the accessibility and usability of network-encoded knowledge for biomedical research.

Main Methods:

• Developed MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone web service.
• Implemented Systems Biology Graphical Notation (SBGN)-compliant format for network representation.
• Integrated automated content annotation, verification, and user feedback mechanisms.

Main Results:

• MINERVA supports curation, annotation, and visualization of molecular interaction networks.
• The platform allows mapping drug targets and overlaying experimental data.
• Extensive export functions enable reuse of network models in SBGN-compliant format.

Conclusions:

• MINERVA provides a robust architecture for building accessible repositories of molecular interaction networks.
• The platform enhances data exploration and interpretation through multi-layered information overlay.
• MINERVA's workflows improve curation efficiency, fostering greater engagement with biomedical knowledge repositories.