Nested effects models for learning signaling networks from perturbation data
Nested effects models for learning signaling networks from perturbation data
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DOI：
10.1002/bimj.200800185
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发表时间：
2009-04-01
影响因子：
1.7
通讯作者：
Beissbarth, Tim
中科院分区：
文献类型：
--
作者：
Froehlich, Holger;Tresch, Achim;Beissbarth, Tim
靶向基因扰动已成为深入了解复杂细胞过程的主要工具。结合通过DNA微阵列测量下游效应，这种方法可以用来深入了解信号通路。嵌套效应模型首先由Markowetz等人引入，作为基于下游扰动效应的嵌套结构的逆向工程信号级联的概率方法。后来Frohlich等人对基本框架进行了大幅扩展，Markowetz等人，还有特雷施和马克维茨在本文中，我们提出了一个完整的方法与迄今为止提出的算法的定性和定量水平上的详细比较审查。作为一个应用，我们目前的结果估计13个基因之间的信号网络在ER-一个途径的人MCF-7乳腺癌细胞。与文献的比较显示了大量的重叠。
Targeted gene perturbations have become a major tool to gain insight into complex cellular processes. In combination with the measurement of downstream effects via DNA microarrays, this approach can be used to gain insight into signaling pathways. Nested Effects Models were first introduced by Markowetz et al. as a probabilistic method to reverse engineer signaling cascades based on the nested structure of downstream perturbation effects. The basic framework was substantially extended later on by Frohlich et al., Markowetz et al., and Tresch and Markowetz. In this paper, we present a review of the complete methodology with a detailed comparison of so far proposed algorithms on a qualitative and quantitative level. As an application, we present results on estimating the signaling network between 13 genes in the ER-a pathway of human MCF-7 breast cancer cells. Comparison with the literature shows a substantial overlap.