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Published Articles >> Table of Contents >> Abstract
18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
pp. 133-142
On Data and Visualization Models for Signaling Pathways
Nattakarn Ratprasartporn, Case Western Reserve University
Ali Cakmak, Case Western Reserve University
Gultekin Ozsoyoglu, Case Western Reserve University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SSDBM.2006.36
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| Abstract |
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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.
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 Additional Information
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Citation:
Nattakarn Ratprasartporn, Ali Cakmak, Gultekin Ozsoyoglu,
"On Data and Visualization Models for Signaling Pathways,"
ssdbm,
pp. 133-142,
18th International Conference on Scientific and Statistical Database Management (SSDBM'06),
2006
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