Terfve2012 - Signalling in liver cancer - logical model

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  • CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.
  • Terfve C, Cokelaer T, Henriques D, MacNamara A, Goncalves E, Morris MK, van Iersel MP, Lauffenburger DA, Saez-Rodriguez J
  • BMC systems biology , 0/ 2012 , Volume 6 , pages: 133 , PubMed ID: 23079107
  • European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK.
  • BACKGROUND: Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. RESULTS: Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. CONCLUSIONS: Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.
Vijayalakshmi Chelliah

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BioModels Database MODEL1506260000
Mathematical Modelling Ontology Logical model

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Model files

MODEL1506260000_url.xml SBML L3V1 representation of Terfve2012 - Signalling in liver cancer - logical model 114.69 KB Preview | Download

Additional files

MODEL1506260000.vcml Auto-generated VCML file 947.00 bytes Preview | Download
MODEL1506260000-biopax2.owl Auto-generated BioPAX (Level 2) 806.00 bytes Preview | Download
MODEL1506260000.pdf Auto-generated PDF file 100.77 KB Preview | Download
MODEL1506260000.m Auto-generated Octave file 1.21 KB Preview | Download
MODEL1506260000_urn.xml Auto-generated SBML file with URNs 111.53 KB Preview | Download
MODEL1506260000.png Auto-generated Reaction graph (PNG) 5.04 KB Preview | Download
MODEL1506260000.svg Auto-generated Reaction graph (SVG) 851.00 bytes Preview | Download
MODEL1506260000.xpp Auto-generated XPP file 466.00 bytes Preview | Download
MODEL1506260000-biopax3.owl Auto-generated BioPAX (Level 3) 810.00 bytes Preview | Download
MODEL1506260000.sci Auto-generated Scilab file 380.00 bytes Preview | Download

  • Model originally submitted by : Vijayalakshmi Chelliah
  • Submitted: 26-Jun-2015 14:33:26
  • Last Modified: 26-Jun-2015 16:57:26
  • Version: 2 public model Download this version
    • Submitted on: 26-Jun-2015 16:57:26
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Current version of Terfve2012 - Signalling in liver cancer - logical model
  • Version: 1 public model Download this version
    • Submitted on: 26-Jun-2015 14:33:26
    • Submitted by: Vijayalakshmi Chelliah
    • With comment: Original import of Terfve2012 - Signalling in liver cancer - logical model
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