Background: Biological signaling pathways that govern cellular physiology form an intricate web of tightly regulated interlocking processes. Data on these regulatory networks are accumulating at an unprecedented pace. The assimilation, visualization and interpretation of these data have become a major challenge in biological research, and once met, will greatly boost our ability to understand cell functioning on a systems level. Results: To cope with this challenge, we are developing the SPIKE knowledge-base of signaling pathways. SPIKE contains three main software components: 1) A database (DB) of biological signaling pathways. Carefully curated information from the literature and data from large public sources constitute distinct tiers of the DB. 2) A visualization package that allows interactive graphic representations of regulatory interactions stored in the DB and superposition of functional genomic and proteomic data on the maps. 3) An algorithmic inference engine that analyzes the networks for novel functional interplays between network components. SPIKE is designed and implemented as a community tool and therefore provides a user-friendly interface that allows registered users to upload data to SPIKE DB. Our vision is that the DB will be populated by a distributed and highly collaborative effort undertaken by multiple groups in the research community, where each group contributes data in its field of expertise. Conclusion: The integrated capabilities of SPIKE make it a powerful platform for the analysis of signaling networks and the integration of knowledge on such networks with omics data.
|Original language||English (US)|
|State||Published - Feb 20 2008|
Bibliographical noteFunding Information:
The development of SPIKE, formerly called SHARP, was supported by grants from the A-T Children's Project, by a Converging Technologies grant from the Israeli Science Foundation, and by ESBIC-D: European Systems Biology Initiative for combating Complex Diseases, a Coordinated Action under the EU Sixth Framework call for Life Sciences, Genomics and Biotechnology for Health. R. Elkon was supported by an Eshkol Fellowship from the Ministry of Science, Israel. I. Ulitsky and I. Zohar were supported by fellowships from the Edmond J. Safra Bioinformatics Program of Tel Aviv University. We thank Anna Pavtulov for her contribution to the development of the system.