A novel method for signal transduction network inference from indirect experimental evidence

Réka Albert, Bhaskar DasGupta, Riccardo Dondi, Sema Kachalo, Eduardo Sontag, Alexander Zelikovsky, Kelly Westbrooks

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


In this paper, we introduce a new method of combined synthesis and inference of biological signal transduction networks. A main idea of our method lies in representing observed causal relationships as network paths and using techniques from combinatorial optimization to find the sparsest graph consistent with all experimental observations. Our contributions are twofold: (a) We formalize our approach, study its computational complexity and prove new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach (Sections 2 and 5). (b) We validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. (2006) and show that our algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.

Original languageEnglish (US)
Pages (from-to)927-949
Number of pages23
JournalJournal of Computational Biology
Issue number7
StatePublished - Sep 2007

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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