Mapping biological process relationships and disease perturbations within a pathway network.

Area of Science:

• Systems biology
• Network science
• Human cellular processes

Background:

• Molecular interaction networks are standard for mapping cellular functions but lack dynamic, context-specific information.
• Integrating contextual data into molecular networks faces challenges with completeness and relevance.
• Existing models struggle to capture the dynamic and context-dependent nature of cellular processes.

Purpose of the Study:

• To develop a novel approach for representing human cellular organization using pathways as network nodes.
• To create a high-level network that incorporates contextual information without relying on molecular interaction data.
• To analyze functional relationships and disease associations within this new pathway-based network.

Main Methods:

• Representing cellular organization by using pathways as nodes in a network.
• Linking pathways to form a high-level network that inherently includes spatial and temporal context.
• Analyzing network communities, disease pathway clustering, and the localization of cancer pathways.

Main Results:

• The pathway network revealed linked communities mirroring functional relationships found in molecular networks (e.g., metabolism, signaling, immunity).
• Diseases mapped onto the network showed functionally connected pathways, highlighting perturbed functions in disease phenotypes.
• Disease pathways, particularly cancer pathways, clustered within specific modules like signaling, DNA processes, and immunity.

Conclusions:

• The developed pathway network provides a high-confidence functional model of cellular organization, overcoming limitations of conventional molecular networks.
• This representation offers an intuitive functional interpretation of cellular processes using reliable pathway and Gene Ontology data.
• The novel network facilitates understanding of disease mechanisms and functional relationships within human cells.