Modeling Cell Signaling Networks with Prize-Collecting Subhypernetworks
Modeling Cell Signaling Networks with Prize-Collecting Subhypernetworks
复制标题
使用获奖子超网络对细胞信号网络进行建模
DOI：
10.1145/2975167.2985655
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Ritz, Anna
中科院分区：
文献类型：
--
作者：
Potter, Barney;Fix, James;Ritz, Anna
细胞信号通路是生物学家用来模拟信号如何通过细胞转导的重要工具。虽然信号通路可以（而且经常）使用图建模，但我们使用的是一种称为有向超图的泛化。我们发现有向超图是一个有用的替代标准图，因为超图给我们一种方法来表示复杂的生物反应，可能有一个以上的反应物或产品。我们开发了超灌木的概念，一个多源，多目标的超路径的推广。我们创建了一个公式，找到特定的，奖品密集的超灌木，可能是以前在相同的信号传导通路的标准图表示未被注意到的生物现象的代表。为了构建我们的图，我们引入了一个加权方案，使用差异表达数据分配权重的节点，代表蛋白质和蛋白质复合物，基于给定的节点在条件之间差异调节的置信度。这种方法是不可知的关于是否一个节点是上调或下调之间的条件，使我们能够找到途径，可能涉及积极和消极的调节。我们将此公式应用于人类Hedgehog信号通路，使用基底细胞癌（BCC）的数据，分析我们的算法对真实的数据的有效性。
Cell signaling pathways are important tools used by biologists to model how signals are transduced through cells. Though signaling pathways can be (and often are) modeled using graphs, we instead use a generalization known as directed hypergraphs. We find directed hypergraphs to be a useful alternative to standard graphs, because hypergraphs give us a way to represent complex biological reactions that may have more than one reactant or product.Prior work analyzing cell signaling as hypergraphs sought structures called hyperpaths. We develop the notion of a hypershrub, a multi-source, multi-target generalization of a hyperpath. We create a formulation that finds specific, prize-dense hypershrubs that may be representative of biological phenomena that went previously unnoticed in standard graph representations of the same signaling pathways.To build our graphs, we introduce a weighting scheme that uses differential expression data to assign weights to nodes, representative of proteins and protein complexes, based on confidence that a given node is differentially regulated between conditions. This method is agnostic about whether or not a node is up-regulated or down-regulated between conditions, allowing us to find pathways that may involve both positive and negative regulation. We apply this formulation to the human Hedgehog signaling pathway, using data from Basal Cell carcinoma (BCC), to analyze the effectiveness of our algorithm on real data.
影响因子：
10.5
作者：
Humke, Eric W.;Dorn, Karolin V.;Rohatgi, Rajat
通讯作者：
Rohatgi, Rajat