Causal protein-signaling networks derived from multiparameter single-cell data
Causal protein-signaling networks derived from multiparameter single-cell data
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DOI：
10.1126/science.1105809
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发表时间：
2005-04-22
期刊：
影响因子：
56.9
通讯作者：
Nolan, GP
中科院分区：
文献类型：
--
作者：
Sachs, K;Perez, O;Nolan, GP
机器学习应用于细胞信号网络中因果影响的自动推导。该推导依赖于同时测量数千个个体原代人类免疫系统细胞中的多种磷酸化蛋白质和磷脂组分。用分子干预扰乱这些细胞，驱动了通路组分之间连接的顺序，其中贝叶斯网络计算方法自动阐明了大多数传统报道的信号传导关系，并预测了新的通路间网络因果关系，我们通过实验验证了这一点。从生理学相关的原代单细胞重建网络模型可能适用于理解自然状态组织信号传导生物学，复杂的药物作用和患病细胞中的功能失调信号传导。
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.