Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany.
Bioinformatics. 2011 Jan 15;27(2):238-44. doi: 10.1093/bioinformatics/btq631. Epub 2010 Nov 10.
Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades. Nested effect models (NEMs) have been introduced as a statistical approach to estimate the upstream signal flow from downstream nested subset structure of perturbation effects. The method was substantially extended later on by several authors and successfully applied to various datasets. The connection of NEMs to Bayesian Networks and factor graph models has been highlighted.
Here, we introduce a computationally attractive extension of NEMs that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops.
The implementation (R and C) is part of the Supplement to this article.
靶向干预与二次效应的测量相结合，可用于对上游非转录信号级联的特征进行计算反向工程。嵌套效应模型（NEMs）已被作为一种统计方法引入，以从下游扰动效应的嵌套子集结构中估计上游信号流。后来，几位作者对该方法进行了实质性扩展，并成功应用于各种数据集。已经强调了 NEMs 与贝叶斯网络和因子图模型的连接。
在这里，我们引入了 NEM 的一种计算上有吸引力的扩展，该扩展可用于分析扰动时间序列数据，从而能够区分直接和间接信号传递，并解决反馈回路。
该实现（R 和 C）是本文补充材料的一部分。
