Baumuratova Tatiana, Surdez Didier, Delyon Bernard, Stoll Gautier, Delattre Olivier, Radulescu Ovidiu, Siegel Anne
Systems Biology Group, Life Science Research Unit, University of Luxembourg,162A Avenue de la Faiencerie, Luxembourg, L-1511, Luxembourg.
BMC Syst Biol. 2010 Nov 2;4:146. doi: 10.1186/1752-0509-4-146.
A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level.
We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation.
The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific.
目前有多种技术可用于分析调控网络。然而，这些技术大多无法在翻译后水平上解读大规模转录数据。
我们解决了利用对系统扰动的大规模转录组观察来分析包含多种相互作用（转录和翻译后）的调控网络这一问题。我们的方法包括对一个名为BioQuali的开源工具的输出进行后处理，BioQuali是一种基于约束的自动分析方法，可大规模模拟生物学家的局部推理。后处理依赖于网络中转录和翻译后水平行为的差异。作为一个案例研究，我们在尤因肉瘤的背景下分析了由一个癌基因控制的基因和蛋白质的网络表示。该分析使我们能够确定这种癌症特有的活跃相互作用。我们还确定了网络中不完整的部分，应提交进一步研究。
所提出的方法对癌症网络的定性分析是有效的。它允许综合使用各种类型的实验数据，以便在初始（可能非常大）的实验数据集中识别应优先考虑的特定信息。通过迭代，可以将新的数据集引入分析，以改进网络表示并使其更具特异性。
