Holger Fröhlich1, Paurush Praveen, Achim Tresch. 1. Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany. frohlich@bit.uni-bonn.de
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: The implementation (R and C) is part of the Supplement to this article.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: The implementation (R and C) is part of the Supplement to this article.