TY - GEN
T1 - A novel method for signal transduction network inference from indirect experimental Evidence
AU - Albert, Reka Z.
AU - DasGupta, Bhaskar
AU - Dondi, Riccardo
AU - Kachalo, Sema
AU - Sontag, Eduardo
AU - Zelikovsky, Alexander
AU - Westbrooks, Kelly
PY - 2007/12/24
Y1 - 2007/12/24
N2 - 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: on the theoretical and algorithmic side, 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. On the application side, we validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. [20] 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.
AB - 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: on the theoretical and algorithmic side, 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. On the application side, we validate the biological usability of our approach by successfully applying it to a previously published signal transduction network by Li et al. [20] 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.
UR - http://www.scopus.com/inward/record.url?scp=37249042663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=37249042663&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:37249042663
SN - 9783540741251
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 407
EP - 419
BT - Algorithms in Bioinformatics - 7th International Workshop, WABI 2007, Proceedings
T2 - 7th International Workshop on Algorithms in Bioinformatics, WABI 2007
Y2 - 8 September 2007 through 9 September 2007
ER -