Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction.
Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction.
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DOI：
10.1038/msb.2009.87
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发表时间：
2009
影响因子：
9.9
通讯作者：
Sorger, Peter K.
中科院分区：
文献类型：
--
作者：
Saez-Rodriguez, Julio;Alexopoulos, Leonidas G.;Epperlein, Jonathan;Samaga, Regina;Lauffenburger, Douglas A.;Klamt, Steffen;Sorger, Peter K.
大规模的蛋白质信号网络对于探索复杂的生物化学途径是有用的，但不能揭示途径如何响应特定的刺激。这种特异性对于理解疾病和设计药物至关重要。在这里，我们描述了一种计算方法，实现在免费的CNO软件，把信令网络的逻辑模型和校准模型对实验数据。当对一个由82种蛋白质组成的文献网络进行建模时，我们发现，尽管布尔近似法很粗糙，但针对实验数据的训练大大提高了预测能力，同时显着减少了相互作用的数量。因此，文献衍生网络中的许多相互作用似乎在我们收集数据的肝细胞中不起作用。与此同时，CNO确定了几个新的交互作用，提高了模型与数据的匹配。虽然从起始网络中缺失，但这些相互作用有文献支持。因此，我们的方法，代表了一种手段，以产生预测，细胞类型特异性模型的哺乳动物信号从通用蛋白质信号网络。
Large-scale protein signalling networks are useful for exploring complex biochemical pathways but do not reveal how pathways respond to specific stimuli. Such specificity is critical for understanding disease and designing drugs. Here we describe a computational approach—implemented in the free CNO software—for turning signalling networks into logical models and calibrating the models against experimental data. When a literature-derived network of 82 proteins covering the immediate-early responses of human cells to seven cytokines was modelled, we found that training against experimental data dramatically increased predictive power, despite the crudeness of Boolean approximations, while significantly reducing the number of interactions. Thus, many interactions in literature-derived networks do not appear to be functional in the liver cells from which we collected our data. At the same time, CNO identified several new interactions that improved the match of model to data. Although missing from the starting network, these interactions have literature support. Our approach, therefore, represents a means to generate predictive, cell-type-specific models of mammalian signalling from generic protein signalling networks.
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影响因子：
4.3
作者：
Aldridge BB;Saez-Rodriguez J;Muhlich JL;Sorger PK;Lauffenburger DA
通讯作者：
Lauffenburger DA
影响因子：
14.9
作者：
Harris, MA;Clark, J;White, R
通讯作者：
White, R
影响因子：
2.5
作者：
Barron, A;Rissanen, J;Yu, B
通讯作者：
Yu, B
影响因子：
14.9
作者：
Chatr-aryamontri, Andrew;Ceol, Arnaud;Palazzi, Luisa Montecchi;Nardelli, Giuliano;Schneider, Maria Victoria;Castagnoli, Luisa;Cesareni, Gianni
通讯作者：
Cesareni, Gianni
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H