The SDREM Method for Reconstructing Signaling and Regulatory Response Networks: Applications for Studying Disease Progression
The SDREM Method for Reconstructing Signaling and Regulatory Response Networks: Applications for Studying Disease Progression
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DOI：
10.1007/978-1-4939-2627-5_30
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发表时间：
2016-01-01
期刊：
影响因子：
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通讯作者：
Bar-Joseph, Ziv
中科院分区：
文献类型：
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作者：
Gitter, Anthony;Bar-Joseph, Ziv
信号和动态调节事件挖掘(SDREM)是一种强大的计算方法，用于识别哪些信号通路和转录因子控制短暂细胞对刺激的反应。SDREM通过将与条件无关的蛋白质相互作用和转录因子结合数据与两种特定条件的数据相结合来构建端到端响应模型：检测刺激和基因表达随时间变化的源蛋白。在这里，我们描述如何应用SDREM来研究人类疾病，以影响神经发生和阿尔茨海默病的表皮生长因子(EGF)反应为例。
The Signaling and Dynamic Regulatory Events Miner (SDREM) is a powerful computational approach for identifying which signaling pathways and transcription factors control the temporal cellular response to a stimulus. SDREM builds end-to-end response models by combining condition-independent protein protein interactions and transcription factor binding data with two types of condition-specific data: source proteins that detect the stimulus and changes in gene expression over time. Here we describe how to apply SDREM to study human diseases, using epidermal growth factor (EGF) response impacting neurogenesis and Alzheimer's disease as an example.