A Novel Method for Signal Transduction Network Inference from Indirect Experimental Evidence View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

Réka Albert , Bhaskar DasGupta , Riccardo Dondi , Sema Kachalo , Eduardo Sontag , Alexander Zelikovsky , Kelly Westbrooks

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: 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. More... »

PAGES

407-419

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-74126-8_38

DOI

http://dx.doi.org/10.1007/978-3-540-74126-8_38

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006232572


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