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Modeling Signaling Networks Using High-throughput Phospho-proteomics

  • Camille Terfve
  • Julio Saez-Rodriguez
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)

Abstract

Cellular communication and information processing is performed by complex, dynamic, and context specific signaling networks. Mathematical modeling is a very useful tool to make sense of this complexity. Building a model relies on two main ingredients: data and an adequate model formalism. In the case of signaling networks, we build mainly upon data at the proteome level, in particular about the phosphorylation of proteins. In this chapter we review recent developments in both data acquisition and computational analysis. We describe two approaches, antibody based technologies and mass spectrometry (MS), along with their main features and limitations. We then go on to describe some model formalisms that have been applied to such high-throughput phospho-proteomics data sets. We consider a variety of formalisms from clustering and data mining approaches to differential equation-based mechanistic models, rule-based, and logic based models, and on through Bayesian network inference and linear regressions.

Keywords

Bayesian Network Signaling Network Partial Little Square Regression Multiple Input Multiple Output Phosphopeptide Enrichment 
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.European Bioinformatics Institute (EMBL–EBI), Wellcome Trust Genome CampusCambridgeUK
  2. 2.EMBL–EBI and European Molecular Biology Laboratory (EMBL), Genome Biology UnitHeidelbergGermany

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