Reconstructing Boolean Models of Signaling
Since the first emergence of protein-protein interaction networks, more than a decade ago, they have been viewed as static scaffolds of the signaling-regulatory events taking place in the cell and their analysis has been mainly confined to topological aspects. Recently, functional models of these networks have been suggested, ranging from Boolean to constraint-based ones. However, learning such models from large-scale data remains a formidable task and most modeling approaches rely on extensive human curation. Here we provide a generic approach to learning Boolean models automatically from data. We apply our approach to growth and inflammatory signaling systems in human and show how the learning phase can improve the fit of the model to experimental data, remove spurious interactions and lead to better understanding of the system at hand.
KeywordsSignaling network Boolean modeling Integer linear programming
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- 1.Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)Google Scholar
- 6.Ryll, A., Samaga, R., Schaper, F., Alexopoulos, L., Klamt, S.: Large-scale network models of il-1 and il-6 signalling and their hepatocellular specification. Molecular Biosystems (2011)Google Scholar
- 7.Saez-Rodriguez, J., Alexopoulos, L., Epperlein, J., Samaga, R., Lauffenburger, D., Klamt, S., Sorger, P.: Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol. Sys. Biol. 5, 331 (2009)Google Scholar
- 11.Yeger-Lotem, E., Riva, L., Su, L.J., Gitler, A.D., Cashikar, A.G., King, O.D., Auluck, P.K., Geddie, M.L., Valastyan, J.S., Karger, D.R., Lindquist, S., Fraenkel, E.: Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet. 41, 316–323 (2009)CrossRefGoogle Scholar