Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models

Abstract

Motivation: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. Results: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. Availability and implementation: R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. Contact: martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de Supplementary information: Supplementary data are available at Bioinformatics online.

Refining Pathways: A Model Comparison Approach

“…Nested effects models (NEMs) implement this concept into an algorithm for inferring pathway topologies [ 1 – 3 ]. They have been successfully applied to analyse LPS-mediated signalling in Drosophila cells [ 1 ], B-cell receptor signalling in human BL2-cells [ 4 ], cellular decision making in early murin embryonic stem cells differentiation [ 3 ], the yeast mediator complex [ 5 ], rhinovirus infection mechanisms [ 6 ], or gene regulatory interaction networks in C. elegans [ 7 ].…”

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity

“…The current scoring implementation of DRUG-NEM assumes an OR relationship between drugs in alternative paths to integrate drug combination effects. Adaptation of the Boolean NEM ( 37 ) within the framework of DRUG-NEM, which requires drug combination data to incorporate logic combinations of desired effects when integrating other molecular data types, may be used to score Boolean drug combinations as well as refine the drug combination ranking distribution. Furthermore, it is possible on a longer time scale that one or more drugs may induce new subpopulations that were not present in the baseline condition.…”

Single cell network analysis with a mixture of Nested Effects Models

“…, 2017 ) systematically infer epistasis from double knock-down screens. Boolean Nested Effects Models ( Pirkl et al. , 2016 ) make use of arbitrary combinations of knock-downs and knock-ins per experiment to infer a full boolean network and additionally integrate literature knowledge.…”