Causal protein-signaling networks derived from multiparameter single-cell data
- PMID: 15845847
- DOI: 10.1126/science.1105809
Causal protein-signaling networks derived from multiparameter single-cell data
Erratum in
- Science. 2005 Aug 19;309(5738):1187
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 VSports手机版. .
Comment in
-
Cell biology. A fishing buddy for hypothesis generators.Science. 2005 Apr 22;308(5721):504-6. doi: 10.1126/science.1110535. Science. 2005. PMID: 15845840 No abstract available.
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