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An Integrated Bayesian Framework for Identifying Phosphorylation Networks in Stimulated Cells

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Advances in Systems Biology

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 736))

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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.

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Authors and Affiliations

  1. Systems Biology Ireland, Conway Institute, University College Dublin (UCD), Belfield, Dublin 4, Ireland

    Tapesh Santra, Boris Kholodenko & Walter Kolch

Authors
  1. Tapesh Santra
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  2. Boris Kholodenko
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  3. Walter Kolch
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Correspondence to Tapesh Santra .

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Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Crichton Street 10, Edinburgh, EH89AB, UK

    Igor I. Goryanin

  2. School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh, EH93JR, UK

    Andrew B. Goryachev

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Santra, T., Kholodenko, B., Kolch, W. (2012). An Integrated Bayesian Framework for Identifying Phosphorylation Networks in Stimulated Cells. In: Goryanin, I.I., Goryachev, A.B. (eds) Advances in Systems Biology. Advances in Experimental Medicine and Biology, vol 736. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7210-1_3

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