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An Integrated Bayesian Framework for Identifying Phosphorylation Networks in Stimulated Cells

  • Tapesh Santra
  • Boris Kholodenko
  • Walter Kolch
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)

Abstract

One of the primary mechanisms of signal transduction in cells is protein phosphorylation. Upon ligand stimulation a series of phosphorylation events take place which eventually lead to transcription. Different sets of phosphorylation events take place due to different stimulating ligands in different types of cells. Knowledge of these phosphorylation events is essential to understand the underlying signaling mechanisms. We have developed a Bayesian framework to infer phosphorylation networks from time series measurements of phosphosite concentrations upon ligand stimulation. To increase the prediction accuracy we integrated different types of data, e.g., amino acid sequence data, genomic context data (gene fusion, gene neighborhood, and phylogentic profiles), primary experimental evidence (physical protein interactions and gene coexpression), manually curated pathway databases, and automatic literature mining with time series data in our inference framework. We compared our results with data available from public databases and report a high level of prediction accuracy.

Keywords

Phosphorylation Event Network Inference Kinase Substrate Context Network Epidermal Growth Factor Stimulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Systems Biology Ireland, Conway InstituteUniversity College Dublin (UCD)Dublin 4Ireland

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