A novel method for signal transduction network inference from indirect experimental evidence. Albert, R., DasGupta, B., Dondi, R., Kachalo, S., Sontag, E., Zelikovsky, A., & Westbrooks, K. *Journal of Computational Biology*, 14:927-949, 2007. abstract bibtex This paper introduces a new method of combined synthesis and inference of biological signal transduction networks. The main idea 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. The paper formalizes the approach, studies its computational complexity, proves new results for exact and approximate solutions of the computationally hard transitive reduction substep of the approach, validates the biological applicability by applying it to a previously published signal transduction network by Li et al., and shows that the algorithm for the transitive reduction substep performs well on graphs with a structure similar to those observed in transcriptional regulatory and signal transduction networks.

@ARTICLE{dasgupta-albert2,
AUTHOR = {R. Albert and B. DasGupta and R. Dondi and S. Kachalo and
E.D. Sontag and A. Zelikovsky and K. Westbrooks},
JOURNAL = {Journal of Computational Biology},
TITLE = {A novel method for signal transduction network inference
from indirect experimental evidence},
YEAR = {2007},
OPTMONTH = {},
OPTNOTE = {},
OPTNUMBER = {},
PAGES = {927-949},
VOLUME = {14},
KEYWORDS = {systems biology, biochemical networks, algorithms,
signal transduction networks, graph algorithms},
PDF = {../../FTPDIR/albert_dasgupta_et_all_jcb07_galleys.pdf},
ABSTRACT = {This paper introduces a new method of combined synthesis
and inference of biological signal transduction networks. The main
idea 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. The
paper formalizes the approach, studies its computational complexity,
proves new results for exact and approximate solutions of the
computationally hard transitive reduction substep of the approach,
validates the biological applicability by applying it to a previously
published signal transduction network by Li et al., and shows that
the algorithm for the transitive reduction substep performs well on
graphs with a structure similar to those observed in transcriptional
regulatory and signal transduction networks.}
}

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