BiologicalNetworks

BiologicalNetworks integrates heterogeneous genomic-scale interaction data to construct, visualize, and query biological networks for systems biology analyses.

Key Features:

• Dynamic Integration: Leverages PathSys to integrate protein-protein interactions, protein-DNA interactions, genetic networks, and other diverse biological datasets for genome-scale network construction.
• Visualization and Analysis Services: Provides computational visualization and network analysis services to construct and explore complex biological interaction networks.
• High-Throughput Data Mapping: Maps high-throughput expression data onto regulatory, metabolic, and cellular networks to contextualize gene and protein expression profiles.
• Graph-Based System: Employs a graph-based unified database via PathSys integrating over 14 curated data sources with emphasis on the budding yeast (S. cerevisiae) and Gene Ontology.
• Query Language and Data Import: Includes a graph manipulation and query language and a generic data-import mechanism using schema-mapping to support relational and graph-based queries.
• Hypothesis Generation: Enables hypothesis generation by revealing cross-dataset connections and interactions through integrated network analysis.

Scientific Applications:

• Systems Biology: Facilitates system-level analyses of interactions among genes, proteins, and pathways.
• Gene Regulation: Supports analysis of gene regulation by integrating protein-DNA interactions and expression data.
• Metabolic Pathways: Enables reconstruction and analysis of metabolic pathways via integrated interaction and annotation data.
• Cellular Interactions: Supports study of cellular interactions by combining interaction networks and expression mapping.
• Disease Modeling: Can be applied to model disease mechanisms by mapping dysregulated genes onto interaction networks.
• Drug Discovery: Supports drug discovery efforts by identifying network-contextualized targets and interactions.
• Personalized Medicine: Contributes to personalized medicine research by contextualizing individual expression profiles within interaction networks.

Methodology:

Integrates heterogeneous data sources into a cohesive graph-based database using PathSys; employs a graph manipulation and query language and a generic data-import mechanism via schema-mapping; maps high-throughput expression data onto regulatory, metabolic, and cellular networks.