TY - JOUR T1 - Inferring modulators of genetic interactions with epistatic nested effects models A1 - Pirkl, Martin A1 - Diekmann, Madeline A1 - van der Wees, Marlies A1 - Beerenwinkel, Niko A1 - Fröhlich, Holger A1 - Markowetz, Florian Y1 - 2017/04/13 N2 - Author summary Genes do not act in isolation, but rather in tight interaction networks. Maps of genetic interactions between pairs of genes are a powerful way to dissect these relationships. Genetic interactions are mostly defined by quantifying individual phenotypes like growth or survival. However, when high-dimensional phenotypes are observed, genetic interactions can become very hard to interpret. Here we test the hypothesis that complex relationships between a gene pair can be explained by the action of a third gene that modulates the interaction. Our approach to test this hypothesis builds on Nested Effects Models (NEMs), a probabilistic model tailored to inferring networks from gene perturbation data. We have extended NEMs with logical functions to model gene interactions and show in simulations and case studies that our approach can successfully infer modulators of genetic interactions and thus lead to a better understanding of an important feature of cellular organisation. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 13 IS - 4 UR - https://doi.org/10.1371/journal.pcbi.1005496 SP - e1005496 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1005496 ER -