PAPET

PAPET models biological pathways using Petri nets to quantify pathway perturbations and prioritize altered signaling processes in disease-related gene expression data.

Key Features:

• Petri-net-based pathway modeling: Uses Petri nets to represent dynamic interactions and dependencies within signaling pathways, addressing limitations of simpler graph-based models.
• Perturbation measurement algorithm: Implements a specific algorithm to measure pathway perturbations under varying conditions and identify alterations linked to disease mechanisms.
• Comprehensive evaluation metrics: Assesses performance using sensitivity, prioritization, mean reciprocal rank, and false-positive rate.

Scientific Applications:

• Gene expression analysis: Analyzes gene expression profiles from normal and disease states, including Pancreatic Ductal Adenocarcinoma (PDAC).
• Benchmarking and comparative studies: Applied to 36 real datasets and compared against FoPA, PADOG, GSEA, CePa, and SPIA, demonstrating superior balance across the evaluation metrics.

Methodology:

Modeling pathways with Petri nets to capture complex interactions within signaling pathways; algorithmic perturbation analysis to quantify pathway perturbations and identify critical changes associated with disease conditions.