title: A novel method for signal transduction network inference from indirect experimental evidence.
abstract: 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.
authors.affiliation: Department of Physics, Pennsylvania State University, University Park, Pennsylvania, USA.
authors.forename: Réka
authors.initials: R
authors.lastname: Albert
authors.forename: Bhaskar
authors.initials: B
authors.lastname: DasGupta
authors.forename: Riccardo
authors.initials: R
authors.lastname: Dondi
authors.forename: Sema
authors.initials: S
authors.lastname: Kachalo
authors.forename: Eduardo
authors.initials: E
authors.lastname: Sontag
authors.forename: Alexander
authors.initials: A
authors.lastname: Zelikovsky
authors.forename: Kelly
authors.initials: K
authors.lastname: Westbrooks
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008433:Mathematics; D008954:Models, Biological; D025941:Protein Interaction Mapping; D015203:Reproducibility of Results; D015398:Signal Transduction