title: Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions.
abstract: BACKGROUND
  Modern 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.
  
  
  RESULTS
  In 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.
  
  
  CONCLUSIONS
  DEPNs 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.
authors.affiliation: German Cancer Research Center, Molecular Genome Analysis, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany. h.froehlich@gmx.de
authors.forename: Holger
authors.initials: H
authors.lastname: Fröhlich
authors.forename: Ozgür
authors.initials: O
authors.lastname: Sahin
authors.forename: Dorit
authors.initials: D
authors.lastname: Arlt
authors.forename: Christian
authors.initials: C
authors.lastname: Bender
authors.forename: Tim
authors.initials: T
authors.lastname: Beissbarth
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D015398:Signal Transduction
chemical_list: D011506:Proteins