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