Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information.
Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information.
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DOI：
10.1093/nar/gkt147
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发表时间：
2013-04
影响因子：
14.9
通讯作者：
Wu R
中科院分区：
文献类型：
--
作者：
Wang J;Chen B;Wang Y;Wang N;Garbey M;Tran-Son-Tay R;Berceli SA;Wu R
环境诱导的基因和蛋白质表达的改变，即表达的可塑性，促进了生物体对环境的反应能力。基于表达可塑性的基因调控网络的重建不仅可以对转录和细胞过程的因果关系有新的见解，而且可以获得支撑生物功能和适应的复杂调控机制。我们描述了一种将表达式可塑性集成到Shannon互信息中的网络推理方法。除了皮尔逊相关性之外，互信息还可以捕获非线性依赖关系和拓扑稀疏性。该方法测量在不同环境中表达的基因的依赖性网络，允许以前所未有的细节测试环境诱导的基因依赖性的可塑性。该方法还能够表征相同的基因在多大程度上触发了不同数量的表达，以应对环境变化。我们通过分析兔静脉移植研究的基因表达数据证明了这种方法的有效性，该研究包括两种不同的血液流动环境。所提出的方法为使用来自不同环境的基因表达数据来建模和分析动态调控网络提供了强大的工具。
The capacity of an organism to respond to its environment is facilitated by the environmentally induced alteration of gene and protein expression, i.e. expression plasticity. The reconstruction of gene regulatory networks based on expression plasticity can gain not only new insights into the causality of transcriptional and cellular processes but also the complex regulatory mechanisms that underlie biological function and adaptation. We describe an approach for network inference by integrating expression plasticity into Shannon’s mutual information. Beyond Pearson correlation, mutual information can capture non-linear dependencies and topology sparseness. The approach measures the network of dependencies of genes expressed in different environments, allowing the environment-induced plasticity of gene dependencies to be tested in unprecedented details. The approach is also able to characterize the extent to which the same genes trigger different amounts of expression in response to environmental changes. We demonstrated the usefulness of this approach through analysing gene expression data from a rabbit vein graft study that includes two distinct blood flow environments. The proposed approach provides a powerful tool for the modelling and analysis of dynamic regulatory networks using gene expression data from distinct environments.
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影响因子：
3
作者：
Meyer, Patrick E.;Lafitte, Frederic;Bontempi, Gianluca
通讯作者：
Bontempi, Gianluca
影响因子：
14.9
作者：
Huang W;Umbach DM;Vincent Jordan N;Abell AN;Johnson GL;Li L
通讯作者：
Li L
影响因子：
--
作者：
SHANNON, CE
通讯作者：
SHANNON, CE
影响因子：
3.3
作者：
Chen, DR;Toone, WM;Bähler, J
通讯作者：
Bähler, J
DOI：
10.1152/ajpheart.00760.2003
发表时间：
2004-01-01
影响因子：
4.8
作者：
Jiang, ZH;Wu, LZ;Berceli, SA
通讯作者：
Berceli, SA