Context-Specific Nested Effects Models

  • Yuriy Sverchkov
  • Yi-Hsuan Ho
  • Audrey Gasch
  • Mark Craven
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10812)


Advances in systems biology have made clear the importance of network models for capturing knowledge about complex relationships in gene regulation, metabolism, and cellular signaling. A common approach to uncovering biological networks involves performing perturbations on elements of the network, such as gene knockdown experiments, and measuring how the perturbation affects some reporter of the process under study. In this paper, we develop context-specific nested effects models (CSNEMs), an approach to inferring such networks that generalizes nested effect models (NEMs). The main contribution of this work is that CSNEMs explicitly model the participation of a gene in multiple contexts, meaning that a gene can appear in multiple places in the network. Biologically, the representation of regulators in multiple contexts may indicate that these regulators have distinct roles in different cellular compartments or cell cycle phases. We present an evaluation of the method on simulated data as well as on data from a study of the sodium chloride stress response in Saccharomyces cerevisiae.



We thank anonymous reviewers for many constructive comments. This research was supported by NIH/NLM grant T15 LM0007359, NIH/NIAID grant U54 AI117954, and NIH/NIGMS grant R01 GM083989.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biostatistics and Medical InformaticsUniversity of Wisconsin–MadisonMadisonUSA
  2. 2.Department of GeneticsUniversity of Wisconsin–MadisonMadisonUSA

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