Inference of signal transduction networks from double causal evidence.

Réka Albert, Bhaskar Dasgupta, Eduardo Sontag

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Here, we present a novel computational method, and related software, to synthesize signal transduction networks from single and double causal evidences. This is a significant and topical problem because there are currently no high-throughput experimental methods for constructing signal transduction networks, and because the understanding of many signaling processes is limited to the knowledge of the signal(s) and of key mediators' positive or negative effects on the whole process. Our software NET-SYNTHESIS is freely downloadable from http://www.cs.uic.edu/∼dasgupta/network-synthesis/ .Our methodology serves as an important first step in formalizing the logical substrate of a signal transduction network, allowing biologists to simultaneously synthesize their knowledge and formalize their hypotheses regarding a signal transduction network. Therefore, we expect that our work will appeal to a broad audience of biologists. The novelty of our algorithmic methodology based on nontrivial combinatorial optimization techniques makes it appealing to computational biologists as well.

Original languageEnglish (US)
Pages (from-to)239-251
Number of pages13
JournalMethods in molecular biology (Clifton, N.J.)
Volume673
DOIs
StatePublished - 2010

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

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