PathExpress

PathExpress identifies and visualizes metabolic pathways and enzyme sub-networks associated with microarray-derived gene expression data to link differentially expressed genes to metabolic function.

Key Features:

• Enzyme Neighborhood (EN) Concept: Defines an Enzyme Neighborhood as a sub-network of linked enzymes with a limited path length to pinpoint pertinent sub-networks affected in gene expression experiments.
• Pathway Identification and Visualization: Identifies the most relevant metabolic pathways for specific gene subsets and provides graphical representations showing the location of expressed or differentially expressed genes within pathways.
• Adaptability Across Organisms: Leverages the KEGG Ligand database to adapt pathway analysis across organisms and is implemented for seven Affymetrix genome arrays.
• Linkage to Enzyme Commission Numbers: Assigns Enzyme Commission (EC) numbers to probe sets (approximately 20% per array) based on homology relationships to connect genes to metabolic reactions and pathways.
• Support for Transcriptomic and Proteomic Interpretation: Maps gene and protein annotations onto metabolic networks to facilitate interpretation of transcriptomic and proteomic data.

Scientific Applications:

• Microarray gene expression analysis: Maps differentially expressed genes from microarray experiments to metabolic pathways and enzyme sub-networks.
• Pathway-centric functional interpretation: Identifies pathway-level and sub-network-level alterations to support elucidation of biological processes and disease mechanisms.
• Comparative genomics: Enables cross-organism comparisons of metabolic responses using KEGG Ligand mappings and supported Affymetrix arrays.
• Integrative omics analysis: Supports integration of transcriptomic and proteomic data with metabolic network context for functional interpretation.
• Personalized medicine research: Facilitates linking gene expression changes to metabolic function relevant to disease studies and biomarker discovery.

Methodology:

Integrates microarray gene expression data with a comprehensive metabolic network model, applies the Enzyme Neighborhood concept to focus on relevant enzyme sub-networks, assigns EC numbers to probe sets based on homology relationships, leverages the KEGG Ligand database, and generates graphical pathway representations.