Literature DB >> 16406679

A biological approach to computational models of proteomic networks.

Kevin A Janes1, Douglas A Lauffenburger.   

Abstract

Computational modeling is useful as a means to assemble and test what we know about proteins and networks. Models can help address key questions about the measurement, definition and function of proteomic networks. Here, we place these biological questions at the forefront in reviewing the computational strategies that are available to analyze proteomic networks. Recent examples illustrate how models can extract more information from proteomic data, test possible interactions between network proteins and link networks to cellular behavior. No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach.

Mesh:

Year:  2006        PMID: 16406679     DOI: 10.1016/j.cbpa.2005.12.016

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  47 in total

1.  Bayesian Network Inference Modeling Identifies TRIB1 as a Novel Regulator of Cell-Cycle Progression and Survival in Cancer Cells.

Authors:  Rina Gendelman; Heming Xing; Olga K Mirzoeva; Preeti Sarde; Christina Curtis; Heidi S Feiler; Paul McDonagh; Joe W Gray; Iya Khalil; W Michael Korn
Journal:  Cancer Res       Date:  2017-01-13       Impact factor: 12.701

2.  Parameter sensitivity analysis in electrophysiological models using multivariable regression.

Authors:  Eric A Sobie
Journal:  Biophys J       Date:  2009-02-18       Impact factor: 4.033

Review 3.  Models of signalling networks - what cell biologists can gain from them and give to them.

Authors:  Kevin A Janes; Douglas A Lauffenburger
Journal:  J Cell Sci       Date:  2013-05-01       Impact factor: 5.285

4.  A multiscale computational approach to dissect early events in the Erb family receptor mediated activation, differential signaling, and relevance to oncogenic transformations.

Authors:  Yingting Liu; Jeremy Purvis; Andrew Shih; Joshua Weinstein; Neeraj Agrawal; Ravi Radhakrishnan
Journal:  Ann Biomed Eng       Date:  2007-02-02       Impact factor: 3.934

5.  Using Statistical Modeling to Understand and Predict Pediatric Stem Cell Function.

Authors:  Farnaz Shoja-Taheri; Alex George; Udit Agarwal; Manu O Platt; Greg Gibson; Michael E Davis
Journal:  Circ Genom Precis Med       Date:  2019-05-17

6.  Direct measurement of association and dissociation rates of DNA binding in live cells by fluorescence correlation spectroscopy.

Authors:  Ariel Michelman-Ribeiro; Davide Mazza; Tilman Rosales; Timothy J Stasevich; Hacene Boukari; Vikas Rishi; Charles Vinson; Jay R Knutson; James G McNally
Journal:  Biophys J       Date:  2009-07-08       Impact factor: 4.033

7.  Maximal entropy inference of oncogenicity from phosphorylation signaling.

Authors:  T G Graeber; J R Heath; B J Skaggs; M E Phelps; F Remacle; R D Levine
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-11       Impact factor: 11.205

8.  Osteoblast-induced EGFR/ERBB2 signaling in androgen-sensitive prostate carcinoma cells characterized by multiplex kinase activity profiling.

Authors:  Ase Bratland; Piet J Boender; Hanne K Høifødt; Ingrid H G Østensen; Rob Ruijtenbeek; Meng-Yu Wang; Jens P Berg; Wolfgang Lilleby; Øystein Fodstad; Anne Hansen Ree
Journal:  Clin Exp Metastasis       Date:  2009-03-18       Impact factor: 5.150

9.  Multipathway model enables prediction of kinase inhibitor cross-talk effects on migration of Her2-overexpressing mammary epithelial cells.

Authors:  Neil Kumar; Raffi Afeyan; Hyung-Do Kim; Douglas A Lauffenburger
Journal:  Mol Pharmacol       Date:  2008-03-18       Impact factor: 4.436

10.  The crosstalk between EGF, IGF, and Insulin cell signaling pathways--computational and experimental analysis.

Authors:  Rafal Zielinski; Pawel F Przytycki; Jie Zheng; David Zhang; Teresa M Przytycka; Jacek Capala
Journal:  BMC Syst Biol       Date:  2009-09-04
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