Wegner2012_TGFbetaSignalling_FeedbackLoops

View the 2012-11 Model of the Month entry for this model
  public model
Model Identifier
BIOMD0000000410
Short description

This model is from the article:
Dynamics and feedback loops in the transforming growth factor β signaling pathway.
Wegner K, Bachmann A, Schad JU, Lucarelli P, Sahle S, Nickel P, Meyer C, Klingmüller U, Dooley S, Kummer U. Biophys Chem. 2012 Jan 5. 22284904 ,
Abstract:
Transforming growth factor β (TGF-β) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-β target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
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.

Format
SBML (L2V4)
Related Publication
  • Dynamics and feedback loops in the transforming growth factor β signaling pathway.
  • Wegner K, Bachmann A, Schad JU, Lucarelli P, Sahle S, Nickel P, Meyer C, Klingmüller U, Dooley S, Kummer U
  • Biophysical chemistry , 3/ 2012 , Volume 162 , pages: 22-34 , PubMed ID: 22284904
  • Biological and Neural Computation Group, Science and Technology Research Institute, University of Hertfordshire, College Lane, Hatfield, United Kingdom. wegner@dhbw-karlsruhe.de
  • Transforming growth factor β (TGF-β) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-β target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.
Contributors
Katja Wengler (neé Wegner)

Metadata information

is
BioModels Database MODEL1202090000
BioModels Database BIOMD0000000410
isDescribedBy
PubMed 22284904
isVersionOf
Gene Ontology GO:0007179
hasTaxon
Brenda Tissue Ontology BTO:0000575
Taxonomy Mus musculus

Curation status
Curated

Tags
Name Description Size Actions

Model files

BIOMD0000000410_url.xml SBML L2V4 representation of Wegner2012_TGFbetaSignalling_FeedbackLoops 205.56 KB Preview | Download

Additional files

BIOMD0000000410.png Auto-generated Reaction graph (PNG) 1.04 MB Preview | Download
BIOMD0000000410-biopax3.owl Auto-generated BioPAX (Level 3) 195.47 KB Preview | Download
BIOMD0000000410-biopax2.owl Auto-generated BioPAX (Level 2) 113.13 KB Preview | Download
BIOMD0000000410.vcml Auto-generated VCML file 897.00 Bytes Preview | Download
BIOMD0000000410.sci Auto-generated Scilab file 28.59 KB Preview | Download
BIOMD0000000410_urn.xml Auto-generated SBML file with URNs 215.88 KB Preview | Download
BIOMD0000000410.pdf Auto-generated PDF file 590.83 KB Preview | Download
BIOMD0000000410.xpp Auto-generated XPP file 29.75 KB Preview | Download
BIOMD0000000410.m Auto-generated Octave file 36.15 KB Preview | Download
BIOMD0000000410.svg Auto-generated Reaction graph (SVG) 213.06 KB Preview | Download

  • Model originally submitted by : Katja Wengler (neé Wegner)
  • Submitted: Feb 9, 2012 7:56:00 AM
  • Last Modified: Mar 27, 2012 2:41:12 PM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Mar 27, 2012 2:41:12 PM
    • Submitted by: Katja Wengler (neé Wegner)
    • With comment: Current version of Wegner2012_TGFbetaSignalling_FeedbackLoops
  • Version: 1 public model Download this version
    • Submitted on: Feb 9, 2012 7:56:00 AM
    • Submitted by: Katja Wengler (neé Wegner)
    • With comment: Original import of TGFbeta signalling and feedback loops

(*) 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
=> _101; species_30 _1*(k+k1*species_30) k1=0.031; k=1.0E-4
_101 => _1*k1*_101 k1=0.065
_101 + species_3 => species_5 _1*(k1*_101*species_3-k2*species_5) k1=1.0; k2=0.1
_105 => _129 + _101; _96 _1*k*_96*_105/(km+_105) k=3.51; km=0.53
_147 + _129 => _153 _1*k1*_147*_129^2 k1=1000.0
=> _147 _1*v v=0.01183
_147 => _1*k1*_147 k1=0.1266
_174 + _96 => _198 _1*(k1*_174*_96-k2*_198) k2=0.01; k1=8.69
_21 + _15 => _3*k1*_21*_15 k1=0.2
_5 + _19 => _9 _3*k1*_5*_19^2 k1=255.068
species_25 => species_10 k1*species_25-k2*species_10 k1=1.0; k2=0.01
species_26 + species_28 => species_29 _3*(k1*species_26*species_28-k2*species_29) k1=0.2; k2=0.2
species_27 => _5 + species_17 _3*k1*species_27 k1=0.0492
species_18 => species_27 _3*V*species_18/(Km+species_18) V=2.34; Km=40.0
Curator's comment:
(added: 10 Feb 2012, 11:44:09, updated: 10 Feb 2012, 11:44:09)
Figure 2 of the reference publication has been reproduced here. The data were obtained by simulating the model using SBMLodeSolver and plotted using Gnuplot.