Literature DB >> 16339439

A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis.

Kevin A Janes1, John G Albeck, Suzanne Gaudet, Peter K Sorger, Douglas A Lauffenburger, Michael B Yaffe.   

Abstract

Signal transduction pathways control cellular responses to stimuli, but it is unclear how molecular information is processed as a network. We constructed a systems model of 7980 intracellular signaling events that directly links measurements to 1440 response outputs associated with apoptosis. The model accurately predicted multiple time-dependent apoptotic responses induced by a combination of the death-inducing cytokine tumor necrosis factor with the prosurvival factors epidermal growth factor and insulin. By capturing the role of unsuspected autocrine circuits activated by transforming growth factor-alpha and interleukin-1alpha, the model revealed new molecular mechanisms connecting signaling to apoptosis. The model derived two groupings of intracellular signals that constitute fundamental dimensions (molecular "basis axes") within the apoptotic signaling network. Projection along these axes captures the entire measured apoptotic network, suggesting that cell survival is determined by signaling through this canonical basis set.

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Year:  2005        PMID: 16339439     DOI: 10.1126/science.1116598

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  211 in total

1.  Cross-talk between receptor tyrosine kinase and tumor necrosis factor-α signaling networks regulates apoptosis but not proliferation.

Authors:  Elsa M Beyer; Gavin MacBeath
Journal:  Mol Cell Proteomics       Date:  2012-02-08       Impact factor: 5.911

2.  Dramatic reduction of dimensionality in large biochemical networks owing to strong pair correlations.

Authors:  Michael Dworkin; Sayak Mukherjee; Ciriyam Jayaprakash; Jayajit Das
Journal:  J R Soc Interface       Date:  2012-02-29       Impact factor: 4.118

3.  Sensitivity control through attenuation of signal transfer efficiency by negative regulation of cellular signalling.

Authors:  Yu Toyoshima; Hiroaki Kakuda; Kazuhiro A Fujita; Shinsuke Uda; Shinya Kuroda
Journal:  Nat Commun       Date:  2012-03-13       Impact factor: 14.919

4.  Data-driven modelling of receptor tyrosine kinase signalling networks quantifies receptor-specific potencies of PI3K- and Ras-dependent ERK activation.

Authors:  Murat Cirit; Jason M Haugh
Journal:  Biochem J       Date:  2012-01-01       Impact factor: 3.857

5.  Multivariate signal integration.

Authors:  Rune Linding
Journal:  Nat Rev Mol Cell Biol       Date:  2010-05-06       Impact factor: 94.444

6.  Predicting cytotoxic T-cell age from multivariate analysis of static and dynamic biomarkers.

Authors:  Catherine A Rivet; Abby S Hill; Hang Lu; Melissa L Kemp
Journal:  Mol Cell Proteomics       Date:  2010-12-30       Impact factor: 5.911

7.  Dasatinib synergizes with both cytotoxic and signal transduction inhibitors in heterogeneous breast cancer cell lines--lessons for design of combination targeted therapy.

Authors:  Brian J Park; Zakary L Whichard; Seth J Corey
Journal:  Cancer Lett       Date:  2012-02-02       Impact factor: 8.679

8.  A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib.

Authors:  Rexxi D Prasasya; Kang Z Vang; Pamela K Kreeger
Journal:  Biotechnol Bioeng       Date:  2011-08-23       Impact factor: 4.530

9.  A novel biological function for CD44 in axon growth of retinal ganglion cells identified by a bioinformatics approach.

Authors:  Albert Ries; Jeffrey L Goldberg; Barbara Grimpe
Journal:  J Neurochem       Date:  2007-08-30       Impact factor: 5.372

10.  Cytokine-induced signaling networks prioritize dynamic range over signal strength.

Authors:  Kevin A Janes; H Christian Reinhardt; Michael B Yaffe
Journal:  Cell       Date:  2008-10-17       Impact factor: 41.582

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