SignalNet: Visualization of Signal Network Responses by Quantitative Proteome Data

  • Christoph Gernert
  • Frank Klawonn
  • Lothar Jänsch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)

Abstract

Interactome databases summarize our present knowledge of how proteins can interact at the molecular level under variable conditions. Signal networks, in which proteins and their interactions are represented by nodes and edges, constitute an essential part in these interactomes. The subset of nodes and edges, which become involved under certain biological conditions, necessitate the integration of further experimental information. Mass spectrometry used in proteomics can provide such data describing the expression and responses of nodes in signal networks.

SignalNet is a program that connects mass spectrometry (MS) data with a protein interaction database to recognize most likely utilized or affected signaling pathways. Regulatory information derived from quantitative MS analyses is used to calculate and visualize which nodes feature altered expression or response levels. Since signals naturally propagate from node to node, SignalNet also emphasizes edges, which are over-connected to several regulated nodes. Both the regulation factor and the robustness of the underlying MS data are statistically evaluated and assigned to the nodes and edges. Thus SignalNet can filter highly complex interactome data to extract information about signal networks coordinating certain biological conditions. Through this filtering ordinarily densely connected interaction networks get purged from irrelevant interactions. By the presentation of a reduced network with respect to the MS data, the actual observed state of the cell can be resolved.

Keywords

Human Protein Reference Database Time Series Experiment Protein Node iTRAQ Label Quantitative Mass Spectrometry 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christoph Gernert
    • 1
  • Frank Klawonn
    • 1
  • Lothar Jänsch
    • 1
  1. 1.Helmholtz Centre for Infection ResearchGermany

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