Signaling Analysis and Visualization 2.5 Description:

The discovery process of cellular signal transduction pathways has reached a stage where pathways are combined to form large networks of interactions.

Viewing protein-protein and ligand-protein interactions as graphs (networks) where bio-molecules are represented as vertices (nodes) and interactions are represented as edges (links) is a useful method for consolidating and organizing experimental evidence from multiple sources to achieve system-level understanding of how pathways control cellular phenotypes.

The emergence of datasets of large-scale networks made of cellular components and interactions provides the opportunity for statistical topological analysis and the challenge of network visualization. Graph-theory methods applied to complex systems in other fields is also applied to analyze bio-molecular cellular networks.

SAVI is a desktop program that implements standard methods to compute the clustering, diameter, connectivity distribution and detecting and visualizing network motifs.

Additionally, SAVI generates a complete website from network datasets in text format. SAVI contains a tool called PathwayGenerator. This tool creates signaling connection maps as web pages from large datasets of interactions. SAVI can also generate a network of interactions when given a list of human gene names.

Signaling Analysis and Visualization 2.5 Requirements:

· 256 MB of RAM
· 1 GB of free hard disk space

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