Literature DB >> 20096559

Simplistic pathways or complex networks?

Claus Jørgensen1, Rune Linding.   

Abstract

Signaling events are frequently described in textbooks as linear cascades. However, in reality, input cues are processed by dynamic and context-specific networks, which are assembled from numerous signaling molecules. Diseases, such as cancer, are typically associated with multiple genomic alterations that likely change the structure and dynamics of cellular signaling networks. To assess the impact of such genomic alterations on the structure of signaling networks and on the ability of cells to accurately translate environmental cues into phenotypic changes, we argue studies must be conducted on a network level. Advances in technologies and computational approaches for data integration have permitted network studies of signaling events in both cancer and normal cells. Here we will review recent advances and how they have impacted our view on signaling networks with a specific angle on signal processing in cancer. Copyright 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20096559     DOI: 10.1016/j.gde.2009.12.003

Source DB:  PubMed          Journal:  Curr Opin Genet Dev        ISSN: 0959-437X            Impact factor:   5.578


  17 in total

1.  Spatial phosphoprotein profiling reveals a compartmentalized extracellular signal-regulated kinase switch governing neurite growth and retraction.

Authors:  Yingchun Wang; Feng Yang; Yi Fu; Xiahe Huang; Wei Wang; Xinning Jiang; Marina A Gritsenko; Rui Zhao; Matthew E Monore; Olivier C Pertz; Samuel O Purvine; Daniel J Orton; Jon M Jacobs; David G Camp; Richard D Smith; Richard L Klemke
Journal:  J Biol Chem       Date:  2011-03-28       Impact factor: 5.157

2.  Protein interaction switches coordinate Raf-1 and MST2/Hippo signalling.

Authors:  David Romano; Lan K Nguyen; David Matallanas; Melinda Halasz; Carolanne Doherty; Boris N Kholodenko; Walter Kolch
Journal:  Nat Cell Biol       Date:  2014-06-15       Impact factor: 28.824

3.  NMR analysis of a stress response metabolic signaling network.

Authors:  Bo Zhang; Steven Halouska; Charles E Schiaffo; Marat R Sadykov; Greg A Somerville; Robert Powers
Journal:  J Proteome Res       Date:  2011-07-08       Impact factor: 4.466

4.  The dawn of a new era in cell signalling research.

Authors:  Stephan M Feller
Journal:  Cell Commun Signal       Date:  2010-05-24       Impact factor: 5.712

5.  Empirical inference of circuitry and plasticity in a kinase signaling network.

Authors:  Edmund H Wilkes; Camille Terfve; John G Gribben; Julio Saez-Rodriguez; Pedro Rodriguez Cutillas
Journal:  Proc Natl Acad Sci U S A       Date:  2015-06-09       Impact factor: 11.205

6.  Mapping the human phosphatome on growth pathways.

Authors:  Francesca Sacco; Pier Federico Gherardini; Serena Paoluzi; Julio Saez-Rodriguez; Manuela Helmer-Citterich; Antonella Ragnini-Wilson; Luisa Castagnoli; Gianni Cesareni
Journal:  Mol Syst Biol       Date:  2012       Impact factor: 11.429

7.  Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli.

Authors:  Melody K Morris; Julio Saez-Rodriguez; David C Clarke; Peter K Sorger; Douglas A Lauffenburger
Journal:  PLoS Comput Biol       Date:  2011-03-03       Impact factor: 4.475

8.  Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability.

Authors:  Edmund H Wilkes; Pedro Casado; Vinothini Rajeeve; Pedro R Cutillas
Journal:  Mol Cell Proteomics       Date:  2017-07-03       Impact factor: 5.911

9.  CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

Authors:  Camille Terfve; Thomas Cokelaer; David Henriques; Aidan MacNamara; Emanuel Goncalves; Melody K Morris; Martijn van Iersel; Douglas A Lauffenburger; Julio Saez-Rodriguez
Journal:  BMC Syst Biol       Date:  2012-10-18

10.  Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties.

Authors:  Michael P Menden; Francesco Iorio; Mathew Garnett; Ultan McDermott; Cyril H Benes; Pedro J Ballester; Julio Saez-Rodriguez
Journal:  PLoS One       Date:  2013-04-30       Impact factor: 3.240

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