von der Heyde Silvia, Sonntag Johanna, Kramer Frank, Bender Christian, Korf Ulrike, Beißbarth Tim
Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
IndivuTest GmbH, Falkenried 88, 20251, Hamburg, Germany.
Methods Mol Biol. 2016;1362:227-46. doi: 10.1007/978-1-4939-3106-4_15.
In this chapter, we describe an approach to reconstruct cellular signaling networks based on measurements of protein activation after different stimulation experiments. As experimental platform reverse-phase protein arrays (RPPA) are used. RPPA allow the measurement of proteins and phosphoproteins across many samples in parallel with minimal sample consumption using a panel of highly target protein-specific antibodies. Functional interactions of proteins are modeled using a Boolean network. We describe the Boolean network reconstruction approach ddepn (dynamic deterministic effects propagation networks), which uses time course data to derive protein interactions based on perturbation experiments. We explain how the method works, give a practical application example, and describe how the results can be interpreted. Furthermore prior knowledge on signaling pathways is essential for network reconstruction. Here we describe the use of our software rBiopaxParser to integrate prior knowledge on protein signaling available in public databases. All applied methods are freely available as open-source R software packages. We describe the preparation of RPPA data as well as all relevant programming steps to format the RPPA data, to infer the prior knowledge, and to reconstruct and analyze the protein signaling networks.
在本章中，我们描述了一种基于不同刺激实验后蛋白质激活测量来重建细胞信号网络的方法。作为实验平台，使用了反向蛋白质阵列（RPPA）。RPPA允许使用一组高度靶向蛋白质特异性抗体，以最少的样本消耗并行测量多个样本中的蛋白质和磷酸化蛋白质。蛋白质的功能相互作用使用布尔网络进行建模。我们描述了布尔网络重建方法ddepn（动态确定性效应传播网络），该方法使用时间进程数据基于扰动实验推导蛋白质相互作用。我们解释了该方法的工作原理，给出了一个实际应用示例，并描述了如何解释结果。此外，信号通路的先验知识对于网络重建至关重要。在这里，我们描述了如何使用我们的软件rBiopaxParser来整合公共数据库中可用的蛋白质信号先验知识。所有应用的方法都作为开源R软件包免费提供。我们描述了RPPA数据的准备以及所有相关的编程步骤，以格式化RPPA数据、推断先验知识以及重建和分析蛋白质信号网络。
