ArticleBMC bioinformatics2009
Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data.Frontiers in genetics · 2021Article
- Single cell network analysis with a mixture of Nested Effects Models.Bioinformatics (Oxford, England) · 2018Article
- Linear effects models of signaling pathways from combinatorial perturbation data.Bioinformatics (Oxford, England) · 2016Article
- Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.PloS one · 2015Article
- Reconstruction of the temporal signaling network in Salmonella-infected human cells.Frontiers in microbiology · 2015Article
- Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.BMC bioinformatics · 2014Article
- Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines.BMC systems biology · 2014Article
- Article
- Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology.BMC systems biology · 2013Article
- Reconstruction of cellular signal transduction networks using perturbation assays and linear programming.PloS one · 2013Article
- Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'.BMC bioinformatics · 2011Article
- RNAi-based validation of antibodies for reverse phase protein arrays.Proteome science · 2010Article
- Dynamic deterministic effects propagation networks: learning signalling pathways from longitudinal protein array data.Bioinformatics (Oxford, England) · 2010Article
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5 authors.
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Abstract
backgroundModern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.
resultsIn this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the ERBB signaling network in de novo trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.
conclusionDEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.
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