Vizan2013 - TGF pathway long term signaling

  public model
Model Identifier
BIOMD0000000499
Short description
Format
SBML (L2V4)
Related Publication
  • Controlling long-term signaling: receptor dynamics determine attenuation and refractory behavior of the TGF-β pathway.
  • Vizán P, Miller DS, Gori I, Das D, Schmierer B, Hill CS
  • Science signaling , 12/ 2013 , Volume 6 , pages: ra106 , PubMed ID: 24327760
  • 1Developmental Signalling Laboratory, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK.
  • Understanding the complex dynamics of growth factor signaling requires both mechanistic and kinetic information. Although signaling dynamics have been studied for pathways downstream of receptor tyrosine kinases and G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors, they have not been investigated for the transforming growth factor-β (TGF-β) superfamily pathways. Using an integrative experimental and mathematical modeling approach, we dissected the dynamic behavior of the TGF-β to Smad pathway, which is mediated by type I and type II receptor serine/threonine kinases, in response to acute, chronic, and repeated ligand stimulations. TGF-β exposure produced a transient response that attenuated over time, resulting in desensitized cells that were refractory to further acute stimulation. This loss of signaling competence depended on ligand binding, but not on receptor activity, and was restored only after the ligand had been depleted. Furthermore, TGF-β binding triggered the rapid depletion of signaling-competent receptors from the cell surface, with the type I and type II receptors exhibiting different degradation and trafficking kinetics. A computational model of TGF-β signal transduction from the membrane to the nucleus that incorporates our experimental findings predicts that autocrine signaling, such as that associated with tumorigenesis, severely compromises the TGF-β response, which we confirmed experimentally. Thus, we have shown that the long-term signaling behavior of the TGF-β pathway is determined by receptor dynamics, does not require TGF-β-induced gene expression, and influences context-dependent responses in vivo.
Contributors
Bernhard Schmierer

Metadata information

is
BioModels Database MODEL1203120000
BioModels Database BIOMD0000000499
isDerivedFrom
BioModels Database BIOMD0000000173
isDescribedBy
PubMed 24327760
hasTaxon
Taxonomy Homo sapiens

Curation status
Curated

Tags
Name Description Size Actions

Model files

BIOMD0000000499_url.xml SBML L2V4 representation of Vizan2013 - TGF pathway long term signaling 94.87 KB Preview | Download

Additional files

BIOMD0000000499.vcml Auto-generated VCML file 900.00 Bytes Preview | Download
BIOMD0000000499_urn.xml Auto-generated SBML file with URNs 97.56 KB Preview | Download
BIOMD0000000499.png Auto-generated Reaction graph (PNG) 5.04 KB Preview | Download
BIOMD0000000499.svg Auto-generated Reaction graph (SVG) 851.00 Bytes Preview | Download
BIOMD0000000499.xpp Auto-generated XPP file 8.49 KB Preview | Download
BIOMD0000000499.pdf Auto-generated PDF file 191.10 KB Preview | Download
BIOMD0000000499.sci Auto-generated Scilab file 154.00 Bytes Preview | Download
BIOMD0000000499-biopax3.owl Auto-generated BioPAX (Level 3) 14.40 KB Preview | Download
BIOMD0000000499-biopax2.owl Auto-generated BioPAX (Level 2) 11.85 KB Preview | Download
BIOMD0000000499.m Auto-generated Octave file 11.74 KB Preview | Download

  • Model originally submitted by : Bernhard Schmierer
  • Submitted: Mar 12, 2012 4:12:51 PM
  • Last Modified: Feb 24, 2014 9:47:15 AM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Feb 24, 2014 9:47:15 AM
    • Submitted by: Bernhard Schmierer
    • With comment: Current version of Vizan2013 - TGF pathway long term signaling
  • Version: 1 public model Download this version
    • Submitted on: Mar 12, 2012 4:12:51 PM
    • Submitted by: Bernhard Schmierer
    • With comment: Original import of New Model

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

Legends
: Variable used inside SBML models


Species
Reactions
Reactions Rate Parameters
species_1 = 1/(1+parameter_13)*(parameter_9*(parameter_13*species_20^2+species_15^2)-parameter_8*(parameter_13*species_13+species_11)) 1/(1+parameter_13)*(parameter_9*(parameter_13*species_20^2+species_15^2)-parameter_8*(parameter_13*species_13+species_11)) parameter_9 = 350.877192982456; parameter_8 = 60.0; parameter_13 = 2.27
species_4 = parameter_1*((parameter_27+parameter_26*species_16)-(parameter_18*species_9+parameter_25)*species_4) parameter_1*((parameter_27+parameter_26*species_16)-(parameter_18*species_9+parameter_25)*species_4) parameter_25 = 0.35; parameter_26 = 0.0; parameter_1 = 0.32; parameter_27 = 0.0; parameter_18 = 100.0
species_6 = parameter_2*species_10-(parameter_3+parameter_6*species_26*parameter_17/(parameter_17+species_22))*species_6 parameter_2*species_10-(parameter_3+parameter_6*species_26*parameter_17/(parameter_17+species_22))*species_6 parameter_2 = 20.0; parameter_3 = 9.36; parameter_6 = 21.3715; parameter_17 = 0.196565
species_7 = parameter_1*((parameter_4*species_5-(1-parameter_28)*species_25)-parameter_18*species_4*species_9) parameter_1*((parameter_4*species_5-(1-parameter_28)*species_25)-parameter_18*species_4*species_9) parameter_28 = 0.0; parameter_1 = 0.32; parameter_18 = 100.0; parameter_4 = 0.08
species_8 = (parameter_6*species_26*parameter_17/(parameter_17+species_22)*species_6+parameter_2*species_15)-parameter_3*(species_20+parameter_11*(species_17+2*species_13)) (parameter_6*species_26*parameter_17/(parameter_17+species_22)*species_6+parameter_2*species_15)-parameter_3*(species_20+parameter_11*(species_17+2*species_13)) parameter_2 = 20.0; parameter_3 = 9.36; parameter_11 = 5.7; parameter_6 = 21.3715; parameter_17 = 0.196565
species_9 = species_7*parameter_21/(1+parameter_21) [] parameter_21 = 0.710526315789474
species_11 = (parameter_13+1)*species_1-parameter_13*species_13 [] parameter_13 = 2.27
species_12 = (parameter_13+1)*parameter_15-parameter_13*species_19 [] parameter_13 = 2.27; parameter_15 = 1.0
species_14 = (parameter_13+1)*species_3-parameter_13*species_8 [] parameter_13 = 2.27
species_15 = (species_14-2*species_11)-species_16 [] []
species_16 = (parameter_13+1)*species_2-parameter_13*species_17 [] parameter_13 = 2.27
species_17 = parameter_9*species_18*species_20-(parameter_8+parameter_3*parameter_11)*species_17 parameter_9*species_18*species_20-(parameter_8+parameter_3*parameter_11)*species_17 parameter_9 = 350.877192982456; parameter_8 = 60.0; parameter_3 = 9.36; parameter_11 = 5.7
species_18 = species_19-species_17 [] []
species_19 = parameter_19*species_21-parameter_3*(species_18+parameter_11*species_17) parameter_19*species_21-parameter_3*(species_18+parameter_11*species_17) parameter_19 = 9.36; parameter_3 = 9.36; parameter_11 = 5.7
species_20 = (species_8-2*species_13)-species_17 [] []
Curator's comment:
(added: 15 Jan 2014, 11:32:18, updated: 15 Jan 2014, 11:32:18)
The curation figure reproduces the results shown in Figure 4B of the reference publication, and was generated using SBMLsimulator 1.1.The names given to the physical entity names in the model do not correspond with those in the reference publication. The curation figure provides the latter in parentheses.