title: Learning gene network structure from time laps cell imaging in RNAi Knock downs.
abstract: BACKGROUND
  As RNA interference is becoming a standard method for targeted gene perturbation, computational approaches to reverse engineer parts of biological networks based on measurable effects of RNAi become increasingly relevant. The vast majority of these methods use gene expression data, but little attention has been paid so far to other data types.
  
  
  RESULTS
  Here we present a method, which can infer gene networks from high-dimensional phenotypic perturbation effects on single cells recorded by time-lapse microscopy. We use data from the Mitocheck project to extract multiple shape, intensity and texture features at each frame. Features from different cells and movies are then aligned along the cell cycle time. Subsequently we use Dynamic Nested Effects Models (dynoNEMs) to estimate parts of the network structure between perturbed genes via a Markov Chain Monte Carlo approach. Our simulation results indicate a high reconstruction quality of this method. A reconstruction based on 22 gene knock downs yielded a network, where all edges could be explained via the biological literature.
  
  
  BACKGROUND
  The implementation of dynoNEMs is part of the Bioconductor R-package nem.
authors.affiliation: Computational Biology and Regulatory Networks, Max-Planck Institute for Plant Breeding Research, Carl-von-Linne-Weg 10, 50829 Cologne, Germany.
authors.forename: Henrik
authors.initials: H
authors.lastname: Failmezger
authors.forename: Paurush
authors.initials: P
authors.lastname: Praveen
authors.forename: Achim
authors.initials: A
authors.lastname: Tresch
authors.forename: Holger
authors.initials: H
authors.lastname: Fröhlich
mesh_terms: D002453:Cell Cycle; D055785:Gene Knockdown Techniques; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008853:Microscopy / Q000379:methods; D009010:Monte Carlo Method; D034622:RNA Interference; D059008:Time-Lapse Imaging