Causal Protein-Signaling Networks Derived from Multiparame Single-Cell Data. Xanya Sofra Weiss

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

Xanya Sofra Weiss

Xanya Sofra Weiss