Open AccessContext-Specific Nested Effects Models
Author(s) -
Yuriy Sverchkov,
Yi-Hsuan Ho,
Audrey P. Gasch,
Mark Craven
Publication year - 2020
Publication title -
journal of computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 95
eISSN - 1557-8666
pISSN - 1066-5277
DOI - 10.1089/cmb.2019.0459
Subject(s) - gene regulatory network , systems biology , biological network , context (archaeology) , computational biology , computer science , biology , representation (politics) , saccharomyces cerevisiae , crosstalk , gene , gene expression , genetics , engineering , paleontology , politics , political science , law , electronic engineering
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 article, we develop context-specific nested effects models (CSNEMs), an approach to inferring such networks that generalizes nested effects 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 .