A comprehensive statistical model for cell signaling.
A comprehensive statistical model for cell signaling.
复制标题
DOI：
10.1109/tcbb.2010.87
复制
发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
Younes L
中科院分区：
文献类型：
--
作者：
Yörük E;Ochs MF;Geman D;Younes L
蛋白质信号网络在转录调控和许多疾病的病因学中起着核心作用。统计方法，特别是贝叶斯网络，已被广泛用于模拟细胞信号，主要用于模型生物，重点是揭示连接性，而不是推断畸变。扩展到哺乳动物系统尚未产生令人信服的结果，可能是由于大大增加的复杂性和有限的体内蛋白质组学测量。在这项研究中，我们提出了一个全面的统计模型，锚定到一个预定义的核心拓扑结构，具有有限的复杂性，由于参数共享和使用微阵列数据的mRNA转录作为唯一可观察到的组件的信号。具体而言，我们考虑到细胞异质性和多层次的过程，在细胞水平上将信号表示为贝叶斯网络，在组织水平上将测量建模为整体平均值，并在群体水平上纳入患者间差异。出于识别个体蛋白质异常作为潜在治疗靶点的目标，我们将我们的方法应用于RAS-RAF网络，使用118例乳腺癌患者进行研究。我们证明了严格的统计推断，通过模拟建立了再现性和从可用的微阵列数据恢复受体状态的能力。
Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of signaling. Specifically, we account for cell heterogeneity and a multi-level process, representing signaling as a Bayesian network at the cell level, modeling measurements as ensemble averages at the tissue level and incorporating patient-to-patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover receptor status from available microarray data.