SGABU computational platform for multiscale modeling: Bridging the gap between education and research

Area of Science:

• Bioengineering and Computational Biology
• Multiscale Modeling and Data Integration

Background:

• Growing need for a unified computational platform in bioengineering.
• Existing research requires integration of diverse datasets and multiscale models for bone, cancer, cardiovascular diseases, and tissue engineering.

Purpose of the Study:

• To develop a web-accessible cloud platform (SGABU) for integrating biological datasets and multiscale models.
• To create a powerful information system for research and education, facilitating knowledge exchange and computational pipeline development.
• To provide accurate biological information from molecular to organ levels.

Main Methods:

• Integration of experimental and clinical datasets (tabular, image formats with metadata).
• Implementation of multiscale models using differential equations solved via the finite element method.
• Utilization of Common Workflow Language (CWL) for simulation pipelines, Docker for containerization, and standard web technologies for user interfaces.

Main Results:

• The SGABU platform features a dashboard with dedicated sections for datasets and multiscale models across research fields.
• Interactive data visualization using Plotly.js (2D) and Kitware Paraview Glance (3D).
• CWL orchestration for input validation and output visualization, including interactive diagrams and animations.

Conclusions:

• The SGABU platform's workflow structure ensures compatibility with other bioengineering platforms.
• Key advantage lies in its versatility, modularity, and extensible architecture.
• Facilitates creation of accurate and comprehensive biological information through integrated computational pipelines.