PATHiWAYS

PATHiWAYS analyzes gene expression data in the context of signaling pathway topology to identify and quantify activated or deactivated stimulus-response subpathways and link them to cellular functionalities.

Key Features:

• Probabilistic Modeling: Employs a probabilistic model to estimate signal transmission probabilities within pathways.
• Receptor-to-Effector Estimation: Estimates probabilities of signal transmission from receptors to final effector molecules using the pathway model.
• Topology- and Expression-Aware Inference: Uses pathway topology and gene expression values to infer the presence or absence of gene products for probability calculations.
• Stimulus-Response Subpathway Detection and Visualization: Identifies and visualizes stimulus-response subpathways that are significantly activated or deactivated.
• Case-Control Microarray Integration: Tailored for analysis of expression microarray data from case-control experiments to compare pathway dynamics between conditions.
• Functional Linkage: Links significant changes in signal transmission probabilities to specific cellular functionalities.

Scientific Applications:

• Cellular Signaling Studies: Interprets how gene expression alterations affect signaling pathway behavior and downstream cellular responses.
• Disease Mechanism Investigation: Maps changes in pathway activation to potential molecular mechanisms underlying disease states.
• Drug Response Analysis: Assesses how treatments alter signal transmission probabilities and associated cellular functions.

Methodology:

Collect gene expression data from microarray experiments.
Apply a probabilistic model to estimate signal transmission probabilities within the pathway using pathway topology and gene expression values to infer presence or absence of gene products.
Identify significant deviations in these probabilities that correlate with functional changes in cells.