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

@article{citeulike:1885082, 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.}, address = {Department of Physics, Pennsylvania State University, University Park, Pennsylvania, USA.}, author = {Albert, R. and DasGupta, B. and Dondi, R. and Kachalo, S. and Sontag, E. and Zelikovsky, A. and Westbrooks, K. }, citeulike-article-id = {1885082}, doi = {10.1089/cmb.2007.0015}, issn = {1066-5277}, journal = {J Comput Biol}, keywords = {reverse\_eng}, month = {September}, number = {7}, pages = {927--949}, posted-at = {2007-11-08 15:16:19}, priority = {2}, title = {A novel method for signal transduction network inference from indirect experimental evidence.}, url = {http://dx.doi.org/10.1089/cmb.2007.0015}, volume = {14}, year = {2007} }

TY - JOUR ID - citeulike:1885082 L3 - citeulike-article-id:1885082 TI - A novel method for signal transduction network inference from indirect experimental evidence. JF - J Comput Biol VL - 14 IS - 7 SP - 927 EP - 949 AD - Department of Physics, Pennsylvania State University, University Park, Pennsylvania, USA. SN - 1066-5277 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: (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. KW - reverse_eng AU - Albert, R AU - DasGupta, B AU - Dondi, R AU - Kachalo, S AU - Sontag, E AU - Zelikovsky, A AU - Westbrooks, K PY - 2007/09// UR - http://dx.doi.org/10.1089/cmb.2007.0015 ER -