public model
Short description

The model reproduces Fig 3a of the paper. Please note that the authors mention that they used a value of 2 for n, n being the power in the positive feedback function for kinase autocatalysis, however the model here has n=1.95 because this results in a simulation that is identical to Fig 3a. The model was successfully tested on MathSBML.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Related Publication
  • Feedback control of T-cell receptor activation.
  • Chan C, Stark J, George AJ
  • Proceedings. Biological sciences , 5/ 2004 , Volume 271 , pages: 931-939
  • Department of Immunology, Division of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 ONN, UK.
  • The specificity and sensitivity of T-cell recognition is vital to the immune response. Ligand engagement with the T-cell receptor (TCR) results in the activation of a complex sequence of signalling events, both on the cell membrane and intracellularly. Feedback is an integral part of these signalling pathways, yet is often ignored in standard accounts of T-cell signalling. Here we show, using a mathematical model, that these feedback loops can explain the ability of the TCR to discriminate between ligands with high specificity and sensitivity, as well as provide a mechanism for sustained signalling. The model also explains the recent counter-intuitive observation that endogenous 'null' ligands can significantly enhance T-cell signalling. Finally, the model may provide an archetype for receptor switching based on kinase-phosphatase switches, and thus be of interest to the wider signalling community.
Harish Dharuri

Metadata information

BioModels Database MODEL2514697386
BioModels Database BIOMD0000000120
PubMed 15255048
Taxonomy Homo sapiens
Human Disease Ontology bacterial infectious disease
Curation status
Name Description Size Actions

Model files

BIOMD0000000120_url.xml SBML L2V1 representation of Chan2004_TCell_receptor_activation 20.53 KB Preview | Download

Additional files

BIOMD0000000120.m Auto-generated Octave file 4.74 KB Preview | Download
BIOMD0000000120.pdf Auto-generated PDF file 168.67 KB Preview | Download
BIOMD0000000120_urn.xml Auto-generated SBML file with URNs 20.08 KB Preview | Download
BIOMD0000000120.svg Auto-generated Reaction graph (SVG) 20.27 KB Preview | Download
BIOMD0000000120.png Auto-generated Reaction graph (PNG) 52.70 KB Preview | Download
BIOMD0000000120-biopax2.owl Auto-generated BioPAX (Level 2) 15.72 KB Preview | Download
BIOMD0000000120.sci Auto-generated Scilab file 67.00 bytes Preview | Download
BIOMD0000000120.vcml Auto-generated VCML file 29.06 KB Preview | Download
BIOMD0000000120.xpp Auto-generated XPP file 3.09 KB Preview | Download
BIOMD0000000120-biopax3.owl Auto-generated BioPAX (Level 3) 23.14 KB Preview | Download

  • Model originally submitted by : Harish Dharuri
  • Submitted: Jun 22, 2007 2:02:10 PM
  • Last Modified: Apr 8, 2016 4:36:48 PM
  • Version: 2 public model Download this version
    • Submitted on: Apr 8, 2016 4:36:48 PM
    • Submitted by: Harish Dharuri
    • With comment: Current version of Chan2004_TCell_receptor_activation
  • Version: 1 public model Download this version
    • Submitted on: Jun 22, 2007 2:02:10 PM
    • Submitted by: Harish Dharuri
    • With comment: Original import of Chan2004_TCell_receptor_activation
Curator's comment:
(added: 22 Jun 2007, 13:20:12, updated: 22 Jun 2007, 13:20:12)
The plot corresponds to Fig 3a of the paper. Simulation results obtained from MathSBML.