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Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Karen Sachs,1*
Omar Perez,2*
Dana Pe'er,3*
Douglas A. Lauffenburger,1
Garry P. Nolan2
Abstract:
Machine learning was applied for the automated derivation ofcausal influences in cellular signaling networks. This derivationrelied on the simultaneous measurement of multiple phosphorylatedprotein and phospholipid components in thousands of individualprimary human immune system cells. Perturbing these cells withmolecular interventions drove the ordering of connections betweenpathway components, wherein Bayesian network computational methodsautomatically elucidated most of the traditionally reportedsignaling relationships and predicted novel interpathway networkcausalities, which we verified experimentally. Reconstructionof network models from physiologically relevant primary singlecells might be applied to understanding native-state tissuesignaling biology, complex drug actions, and dysfunctional signalingin 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.
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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Roger Brent and Larry Lok (22 April 2005) Science308 (5721), 504.
[DOI: 10.1126/science.1110535] |Summary »|Full Text »|PDF »
Dana Pe'er (26 April 2005) Sci. STKE2005 (281), pl4.
[DOI: 10.1126/stke.2812005pl4] |Abstract »|Full Text »|PDF »
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