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Science 308 (5721): 523-529

Copyright © 2005 by the American Association for the Advancement of Science

Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data

Karen Sachs,1* Omar Perez,2* Dana Pe'er,3* Douglas A. Lauffenburger,1{dagger} Garry P. Nolan2{dagger}

Abstract: Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.

1 Biological Engineering Division, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
2 Stanford University School of Medicine, The Baxter Laboratory of Genetic Pharmacology, Department of Microbiology and Immunology, Stanford, CA 94305, USA.
3 Harvard Medical School, Department of Genetics, Boston, MA 02115, USA.

* These authors contributed equally to this work.

{dagger} To whom correspondence should be addressed. E-mail: lauffen{at}mit.edu (D.A.L.); gnolan{at}stanford.edu (G.P.N.)


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