Inferring functional communities from partially observed biological networks exploiting geometric topology and side...

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Cellular biological networks are crucial for cell function but are challenging to fully map due to data incompleteness.
• Detecting functional communities within these networks is vital for understanding biological processes.
• Existing methods struggle with incomplete network data and lack of ground truth for validation.

Purpose of the Study:

• To develop a novel framework for detecting communities in partially observed biological networks.
• To improve community detection by integrating network topology with gene function information (side information).
• To enhance the understanding of biological network organization and function.

Main Methods:

• Developed a geometric-based detection framework utilizing Ollivier-Ricci curvature to analyze network topology.
• Integrated gene function knowledge (side information) into the Ollivier-Ricci curvature algorithm.
• Applied the framework to partially observed Arabidopsis protein interaction datasets.

Main Results:

• The Ollivier-Ricci curvature approach with side information outperformed previous methods in identifying biological communities.
• Successfully identified conserved and varied biological communities in plant protein interaction networks.
• Discovered an expanded auxin community, incorporating the Cop9 signalosome, linking it to auxin response and root development.

Conclusions:

• Ollivier-Ricci curvature with side information is an effective method for community detection in incomplete biological networks.
• This approach can uncover novel network components and communities, offering new insights into complex biological systems.
• The findings provide a powerful tool for advancing systems biology research and understanding cellular organization.