public model
Model Identifier
Short description

This sbml file describes the RECI model from:
"Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system" by Bernhard Schmierer, Alexander L. Tournier, Paul A. Bates and Caroline S. Hill, Proc Natl Acad Sci U S A. 2008 May 6;105(18):6608-13.
All parameter and species names are as in Figure S3 of the original publication. The original model was done in copasi.
SB-431542 addition to a concentration of 10000 nM is set at 2700 sec. The initial concentration of SB, the time point of addition and the final concentration can be set by altering the parameters SB_0, t_SB and SB_end.
This model file has been used to reproduce Figures 2D and 5A from the research paper using SBMLodesolver. To get the results for the figures, sum the corresponding concentrations:
fig 2D: nuclear EGFP-Smad2 = G_n + pG_n + G2_n + G4_n + 2* GG_n
fig 5A (either n or c for nucleus or cytosol):
monomeric Smad2 = S2_n/c + G_n/c
monomeric P-Smad2 = pS2_n/c + pG_n/c
Smad2/Smad4 complexes = S24_n/c + G4_n/c
Smad2/Smad2 complexes = S22_n/c + G2_n/c + GG_n/c

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Related Publication
  • Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system.
  • Schmierer B, Tournier AL, Bates PA, Hill CS
  • Proceedings of the National Academy of Sciences of the United States of America , 5/ 2008 , Volume 105 , pages: 6608-6613 , PubMed ID: 18443295
  • Developmental Signalling Laboratory and Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3PX, United Kingdom.
  • TGF-beta-induced Smad signal transduction from the membrane into the nucleus is not linear and unidirectional, but rather a dynamic network that couples Smad phosphorylation and dephosphorylation through continuous nucleocytoplasmic shuttling of Smads. To understand the quantitative behavior of this network, we have developed a tightly constrained computational model, exploiting the interplay between mathematical modeling and experimental strategies. The model simultaneously reproduces four distinct datasets with excellent accuracy and provides mechanistic insights into how the network operates. We use the model to make predictions about the outcome of fluorescence recovery after photobleaching experiments and the behavior of a functionally impaired Smad2 mutant, which we then verify experimentally. Successful model performance strongly supports the hypothesis of a dynamic maintenance of Smad nuclear accumulation during active signaling. The presented work establishes Smad nucleocytoplasmic shuttling as a dynamic network that flexibly transmits quantitative features of the extracellular TGF-beta signal, such as its duration and intensity, into the nucleus.
Bernhard Schmierer

Metadata information

BioModels Database MODEL0451870146
BioModels Database BIOMD0000000173
PubMed 18443295
Taxonomy Homo sapiens

Curation status

Name Description Size Actions

Model files

BIOMD0000000173_url.xml SBML L2V1 representation of Schmierer_2008_Smad_Tgfb 65.15 KB Preview | Download

Additional files

BIOMD0000000173.png Auto-generated Reaction graph (PNG) 197.39 KB Preview | Download
BIOMD0000000173.svg Auto-generated Reaction graph (SVG) 63.21 KB Preview | Download
BIOMD0000000173.m Auto-generated Octave file 12.70 KB Preview | Download
BIOMD0000000173.pdf Auto-generated PDF file 268.50 KB Preview | Download
BIOMD0000000173_urn.xml Auto-generated SBML file with URNs 63.20 KB Preview | Download
BIOMD0000000173.sci Auto-generated Scilab file 166.00 Bytes Preview | Download
BIOMD0000000173.xpp Auto-generated XPP file 9.29 KB Preview | Download
BIOMD0000000173-biopax2.owl Auto-generated BioPAX (Level 2) 49.04 KB Preview | Download
BIOMD0000000173-biopax3.owl Auto-generated BioPAX (Level 3) 78.89 KB Preview | Download
BIOMD0000000173.vcml Auto-generated VCML file 897.00 Bytes Preview | Download

  • Model originally submitted by : Bernhard Schmierer
  • Submitted: 04-Jun-2008 09:40:17
  • Last Modified: 08-Apr-2016 16:39:46
  • Version: 2 public model Download this version
    • Submitted on: 08-Apr-2016 16:39:46
    • Submitted by: Bernhard Schmierer
    • With comment: Current version of Schmierer_2008_Smad_Tgfb
  • Version: 1 public model Download this version
    • Submitted on: 04-Jun-2008 09:40:17
    • Submitted by: Bernhard Schmierer
    • With comment: Original import of SmadNucleocytoplasmicDynamics_Schmierer_PNAS_2008

(*) 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.

: Variable used inside SBML models

Reactions Rate Parameters
pS2_n + S4_n => S24_n nucleus*(kon*pS2_n*S4_n-koff*S24_n) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
S24_c => S24_n kin_CIF*S24_c kin_CIF = 3.36347821E-14 litrepersecond
S4_c => S4_n kin*S4_c-kin*S4_n kin = 5.93E-15 litrepersecond
pS2_n => S22_n nucleus*(kon*pS2_n*pS2_n-koff*S22_n) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
pS2_n + PPase => S2_n + PPase nucleus*kdephos*pS2_n*PPase kdephos = 0.00656639 pernMpersecond
pS2_n + pG_n => G2_n nucleus*(kon*pS2_n*pG_n-koff*G2_n) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
pS2_c + S4_c => S24_c cytosol*(kon*pS2_c*S4_c-koff*S24_c) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
pS2_c + pG_c => G2_c cytosol*(kon*pS2_c*pG_c-koff*G2_c) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
pG_c => pG_n kin*pG_c-kex*pG_n kex = 1.26E-14 litrepersecond; kin = 5.93E-15 litrepersecond
pG_c => GG_c cytosol*(kon*pG_c*pG_c-koff*GG_c) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
pG_c + S4_c => G4_c cytosol*(kon*pG_c*S4_c-koff*G4_c) kon = 0.00183925592901392 pernMpersecond; koff = 0.016 persecond
G2_c => G2_n kin_CIF*G2_c kin_CIF = 3.36347821E-14 litrepersecond
Curator's comment:
(added: 21 May 2008, 15:47:00, updated: 21 May 2008, 15:47:00)
reproduction of fig 5 A in the paper the time was rescaled to minutes and some concentrations needed to be summed up, as indicated in the model notes the integraton was done using SBMlodeSolver-20080507