On data and visualization models for signaling pathways

Signaling pathways are chains of interacting proteins, through which the cell converts a (usually) extracellular signal into a biological response. The number of known signaling pathways in the biological literature and on the web has been increasing at a very high rate, thus demanding a need for efficient ways of storing, visualizing, querying, and mining signaling pathways. In this paper, first we briefly compare the data modeling and visualization capabilities of existing signaling pathways systems. Then, we present a signaling pathway data model and its visualization that subsumes the existing models. Our model visualizes a signaling pathway (a) as a nested graph, (b) with explicit location information (e.g., cell, tissue, organelle, nucleus, etc.), and (c) in four abstraction levels, namely, the levels of molecule-tomolecule signaling steps, collapsed sub-pathways, molecule-to-pathway connections, and pathway-to-pathway connections. We model (1) the effects of specific signaling steps, (2) state changes of signaling molecules, (3) various (extensible) structural/physical changes of signaling molecules such as complex formation, dissociation, assembly, oligomerization, di-/trimerization, cleavage and degradation, (4) condensation/hydrolysis signaling steps, and (5) exchanges and translocations as signaling steps. The visualization model gracefully models incomplete information and hierarchical levels of signaling molecules. Finally, we introduce a completely new visualization dimension for pathways, namely, Gene Ontology (GO)-based functional visualizations of pathways. We believe that functional visualizations of pathways provides new opportunities in understanding, defining and comparing existing pathways, and in helping discover new ones.