title: Inference of signal transduction networks from double causal evidence.
abstract: Here, we present a novel computational method, and related software, to synthesize signal transduction networks from single and double causal evidences. This is a significant and topical problem because there are currently no high-throughput experimental methods for constructing signal transduction networks, and because the understanding of many signaling processes is limited to the knowledge of the signal(s) and of key mediators' positive or negative effects on the whole process. Our software NET-SYNTHESIS is freely downloadable from http://www.cs.uic.edu/∼dasgupta/network-synthesis/ .Our methodology serves as an important first step in formalizing the logical substrate of a signal transduction network, allowing biologists to simultaneously synthesize their knowledge and formalize their hypotheses regarding a signal transduction network. Therefore, we expect that our work will appeal to a broad audience of biologists. The novelty of our algorithmic methodology based on nontrivial combinatorial optimization techniques makes it appealing to computational biologists as well.
authors.affiliation: Department of Physics, Penn State University, University Park, PA, USA.
authors.forename: Réka
authors.initials: R
authors.lastname: Albert
authors.forename: Bhaskar
authors.initials: B
authors.lastname: Dasgupta
authors.forename: Eduardo
authors.initials: E
authors.lastname: Sontag
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D015398:Signal Transduction; D012984:Software