An integer linear programming approach for finding deregulated subgraphs in regulatory networks.
An integer linear programming approach for finding deregulated subgraphs in regulatory networks.
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DOI：
10.1093/nar/gkr1227
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发表时间：
2012-03
影响因子：
14.9
通讯作者：
Lenhof HP
中科院分区：
文献类型：
--
作者：
Backes C;Rurainski A;Klau GW;Müller O;Stöckel D;Gerasch A;Küntzer J;Maisel D;Ludwig N;Hein M;Keller A;Burtscher H;Kaufmann M;Meese E;Lenhof HP
细胞信号通路的失控在肿瘤的发生发展中起着至关重要的作用。识别这种途径需要有效的分析工具，以促进对表达差异的解释。在这里，我们提出了一种新的、高效的方法来识别监管网络中解除监管的子网络。给出衡量相应基因或蛋白质放松管制程度的每个节点的分数，该算法计算出从指定根节点可到达的指定大小的最重连接的子网络。这个根节点可以被解释为对观察到的放松管制负责的分子关键角色。为了展示我们方法的潜力，我们分析了三个基因表达数据集。在一种方案中，我们比较了来自BRCA1突变携带者的非恶性原代乳腺上皮细胞和没有BRCA1突变的上皮细胞的表达谱。我们的结果表明，氧化应激在BRCA1突变携带者的上皮细胞中起着重要的作用，应激蛋白的激活可能导致避免细胞凋亡，从而提高具有基因改变的细胞的总体存活率。总之，我们的方法为阐明致病机制和检测分子关键角色开辟了新的途径。
Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players.
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影响因子：
3
作者：
Küntzer J;Backes C;Blum T;Gerasch A;Kaufmann M;Kohlbacher O;Lenhof HP
通讯作者：
Lenhof HP
影响因子：
11.2
作者：
Burga LN;Tung NM;Troyan SL;Bostina M;Konstantinopoulos PA;Fountzilas H;Spentzos D;Miron A;Yassin YA;Lee BT;Wulf GM
通讯作者：
Wulf GM
DOI：
10.1093/bioinformatics/btr245
发表时间：
2011-07-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Dao P;Wang K;Collins C;Ester M;Lapuk A;Sahinalp SC
通讯作者：
Sahinalp SC
影响因子：
2
作者：
Hwang, SL;Chang, JH;Hong, YR
通讯作者：
Hong, YR
影响因子：
12.3
作者：
Fortney, Kristen;Kotlyar, Max;Jurisica, Igor
通讯作者：
Jurisica, Igor