@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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