PathRNet

PathRNet reconstructs context-specific, time-varying regulatory networks by integrating microarray gene expression profiles with pathway and transcription factor knowledge to model dynamic interactions governing cellular responses.

Key Features:

• Context-Specific Modeling: Tailors network constituents and topology to specific phenotypic and experimental contexts, including tissue types, cell conditions (e.g., damage or stress), and macroenvironmental factors.
• Time-Varying Dynamics: Captures temporal changes in network elements and their roles across cellular states such as different stages of the cell cycle.
• Network Structure: Represents nodes as transcription factors (TFs) and pathways and edges as regulatory interactions to enable pathway-centric regulatory analysis.
• Robust Reconstruction Approach: Implements the PATTERN approach to reconstruct the temporal dynamics of pathways and TFs within the networks.

Scientific Applications:

• Kaposi's sarcoma-associated herpesvirus infection: Applied to study the infection of human endothelial cells by Kaposi's sarcoma-associated herpesvirus.
• System-level regulatory analysis: Reveals dynamic gene regulatory circuitry and pathway–TF interactions to provide a system-level view of regulatory mechanisms.

Methodology:

Integrates microarray gene expression data with existing knowledge of pathways and transcription factors and applies the PATTERN approach to reconstruct context-specific, time-varying regulatory network models.