Pathway logic: symbolic analysis of biological signaling.

Steven Eker, Merrill Knapp, Keith Laderoute, Patrick Lincoln, Jose Meseguer, Kemal Sonmez

Research output: Contribution to journalArticle

100 Scopus citations

Abstract

The genomic sequencing of hundreds of organisms including homo sapiens, and the exponential growth in gene expression and proteomic data for many species has revolutionized research in biology. However, the computational analysis of these burgeoning datasets has been hampered by the sparse successes in combinations of data sources, representations, and algorithms. Here we propose the application of symbolic toolsets from the formal methods community to problems of biological interest, particularly signaling pathways, and more specifically mammalian mitogenic and stress responsive pathways. The results of formal symbolic analysis with extremely efficient representations of biological networks provide insights with potential biological impact. In particular, novel hypotheses may be generated which could lead to wet lab validation of new signaling possibilities. We demonstrate the graphic representation of the results of formal analysis of pathways, including navigational abilities, and describe the logical underpinnings of the approach. In summary, we propose and provide an initial description of an algebra and logic of signaling pathways and biologically plausible abstractions that provide the foundation for the application of high-powered tools such as model checkers to problems of biological interest.

Original languageEnglish (US)
Pages (from-to)400-412
Number of pages13
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
StatePublished - 2002
Externally publishedYes

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ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

Cite this

Eker, S., Knapp, M., Laderoute, K., Lincoln, P., Meseguer, J., & Sonmez, K. (2002). Pathway logic: symbolic analysis of biological signaling. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 400-412.