Lee2003 - Roles of APC and Axin in Wnt Pathway (without regulatory loop)

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Model Identifier
BIOMD0000000658
Short description
Lee2003 - Roles of APC and Axin in Wnt Pathway (without regulatory loop)

This model is described in the article:

Lee E, Salic A, Krüger R, Heinrich R, Kirschner MW.
PLoS Biol. 2003 Oct; 1(1): E10

Abstract:

Wnt signaling plays an important role in both oncogenesis and development. Activation of the Wnt pathway results in stabilization of the transcriptional coactivator beta-catenin. Recent studies have demonstrated that axin, which coordinates beta-catenin degradation, is itself degraded. Although the key molecules required for transducing a Wnt signal have been identified, a quantitative understanding of this pathway has been lacking. We have developed a mathematical model for the canonical Wnt pathway that describes the interactions among the core components: Wnt, Frizzled, Dishevelled, GSK3beta, APC, axin, beta-catenin, and TCF. Using a system of differential equations, the model incorporates the kinetics of protein-protein interactions, protein synthesis/degradation, and phosphorylation/dephosphorylation. We initially defined a reference state of kinetic, thermodynamic, and flux data from experiments using Xenopus extracts. Predictions based on the analysis of the reference state were used iteratively to develop a more refined model from which we analyzed the effects of prolonged and transient Wnt stimulation on beta-catenin and axin turnover. We predict several unusual features of the Wnt pathway, some of which we tested experimentally. An insight from our model, which we confirmed experimentally, is that the two scaffold proteins axin and APC promote the formation of degradation complexes in very different ways. We can also explain the importance of axin degradation in amplifying and sharpening the Wnt signal, and we show that the dependence of axin degradation on APC is an essential part of an unappreciated regulatory loop that prevents the accumulation of beta-catenin at decreased APC concentrations. By applying control analysis to our mathematical model, we demonstrate the modular design, sensitivity, and robustness of the Wnt pathway and derive an explicit expression for tumor suppression and oncogenicity.

This model is hosted on BioModels Database and identified by: BIOMD0000000658.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Format
SBML (L2V4)
Related Publication
  • The roles of APC and Axin derived from experimental and theoretical analysis of the Wnt pathway.
  • Lee E, Salic A, Krüger R, Heinrich R, Kirschner MW
  • PLoS Biology , 10/ 2003 , Volume 1 , Issue 1 , pages: E10 , PubMed ID: 14551908
  • Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA.
  • Wnt signaling plays an important role in both oncogenesis and development. Activation of the Wnt pathway results in stabilization of the transcriptional coactivator beta-catenin. Recent studies have demonstrated that axin, which coordinates beta-catenin degradation, is itself degraded. Although the key molecules required for transducing a Wnt signal have been identified, a quantitative understanding of this pathway has been lacking. We have developed a mathematical model for the canonical Wnt pathway that describes the interactions among the core components: Wnt, Frizzled, Dishevelled, GSK3beta, APC, axin, beta-catenin, and TCF. Using a system of differential equations, the model incorporates the kinetics of protein-protein interactions, protein synthesis/degradation, and phosphorylation/dephosphorylation. We initially defined a reference state of kinetic, thermodynamic, and flux data from experiments using Xenopus extracts. Predictions based on the analysis of the reference state were used iteratively to develop a more refined model from which we analyzed the effects of prolonged and transient Wnt stimulation on beta-catenin and axin turnover. We predict several unusual features of the Wnt pathway, some of which we tested experimentally. An insight from our model, which we confirmed experimentally, is that the two scaffold proteins axin and APC promote the formation of degradation complexes in very different ways. We can also explain the importance of axin degradation in amplifying and sharpening the Wnt signal, and we show that the dependence of axin degradation on APC is an essential part of an unappreciated regulatory loop that prevents the accumulation of beta-catenin at decreased APC concentrations. By applying control analysis to our mathematical model, we demonstrate the modular design, sensitivity, and robustness of the Wnt pathway and derive an explicit expression for tumor suppression and oncogenicity.
Contributors
Submitter of the first revision: Emma Fairbanks
Submitter of this revision: administrator
Modellers: administrator, Emma Fairbanks

Metadata information

is (2 statements)
BioModels Database MODEL1708310000
BioModels Database BIOMD0000000658

isDescribedBy (1 statement)
PubMed 14551908

hasTaxon (1 statement)
Taxonomy Xenopus

hasProperty (2 statements)

Curation status
Curated


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Model files

BIOMD0000000658_url.xml SBML L2V4 representation of Lee2003 - Roles of APC and Axin in Wnt Pathway (without regulatory loop) 98.16 KB Preview | Download

Additional files

BIOMD0000000658-biopax2.owl Auto-generated BioPAX (Level 2) 25.92 KB Preview | Download
BIOMD0000000658-biopax3.owl Auto-generated BioPAX (Level 3) 41.63 KB Preview | Download
BIOMD0000000658.m Auto-generated Octave file 8.94 KB Preview | Download
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BIOMD0000000658_urn.xml Auto-generated SBML file with URNs 97.65 KB Preview | Download
MODEL1708310000_edited.cps Curated and annotated model COPASI file 114.49 KB Preview | Download
MODEL1708310000_edited.sedml SED-ML file for figure 6 2.33 KB Preview | Download

  • Model originally submitted by : Emma Fairbanks
  • Submitted: Aug 31, 2017 3:36:56 PM
  • Last Modified: Mar 21, 2018 11:03:19 AM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Mar 21, 2018 11:03:19 AM
    • Submitted by: administrator
    • With comment: Current version of Lee2003 - Roles of APC and Axin in Wnt Pathway (without regulatory loop)
  • Version: 2 public model Download this version
    • Submitted on: Aug 31, 2017 4:42:09 PM
    • Submitted by: Emma Fairbanks
    • With comment: Current version of Lee2003 - Wnt Pathway
  • Version: 1 public model Download this version
    • Submitted on: Aug 31, 2017 3:36:56 PM
    • Submitted by: Emma Fairbanks
    • With comment: Original import of Wnt Pathway

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Legends
: Variable used inside SBML models


Species
Reactions
Reactions Rate Parameters
APC__axin__GSK3 => APC_axin_GSK3 Cytoplasm*k5*APC__axin__GSK3 k5 = 0.133
GSK3 + APC_axin => APC_axin_GSK3 Cytoplasm*(k6*GSK3*APC_axin-k_6*APC_axin_GSK3) k6 = 0.0909; k_6 = 0.909
APC + B_catenin_0 => B_catenin_APC k17*APC*B_catenin_0-k_17*B_catenin_APC k_17 = 600000.0; k17 = 500.0
APC_axin_GSK3 => APC__axin__GSK3 Cytoplasm*k4*APC_axin_GSK3 k4 = 0.267
B_catenin_APC__axin__GSK3 => B_catenin__APC__axin__GSK3 Cytoplasm*k9*B_catenin_APC__axin__GSK3 k9 = 206.0
Dsh_a => Dsh_i Cytoplasm*k2*Dsh_a k2 = 0.0182
APC + Axin => APC_axin Cytoplasm*(k7*APC*Axin-k_7*APC_axin) k7 = 500.0; k_7 = 25000.0
APC__axin__GSK3 + B_catenin_0 => B_catenin_APC__axin__GSK3 k8*APC__axin__GSK3*B_catenin_0-k_8*B_catenin_APC__axin__GSK3 k_8 = 60000.0; k8 = 500.0
B_catenin__APC__axin__GSK3 => B_catenin + APC__axin__GSK3 Cytoplasm*k10*B_catenin__APC__axin__GSK3 k10 = 206.0
Curator's comment:
(added: 21 Mar 2018, 11:31:47, updated: 21 Mar 2018, 11:31:47)
Figure 6 of the publication has been reproduced. The initial conditions were given in table 2 for all species except GSK3 and APC/axin which were set to 50 and 0.01 respectively. The reverse reaction rate constants (k_7, k_8, k_16, k_17) for reactions v7, v8, v16 and v17 were defined as k_x = kx*Kx where kx is the forward reaction rate constant and Kx is the relative dissociation constant. k7, k8, k16 and k17 were set to 500. This model is without the regulatory loop, meaning reactions v11, v13 and v15 are not dependent on APC and follow mass action kinetics and not Michaelis-Menten kinetics, as explained in the supplementary files. The simulations were performed in COPASI 4.22 (Build 170) and the figures were generated in MATLAB R2014b.