Fallon2000 - Interleukin-2 dynamics

  public model
Model Identifier
BIOMD0000000665
Short description

This a model from the article:
Computational model for effects of ligand/receptor binding properties on interleukin-2 trafficking dynamics and T cell proliferation response.
Fallon EM, Lauffenburger DA. Biotechnol Prog 2000 Sep-Oct;16(5):905-16 11027188 ,
Abstract:
Multisubunit cytokine receptors such as the heterotrimeric receptor for interleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance in biotechnology and are crucial regulators of cell proliferation and differentiation behavior. Dynamics of cytokine/receptor endocytic trafficking can significantly impact cell responses through effects of receptor down-regulation and ligand depletion, and in turn are governed by ligand/receptor binding properties. We describe here a computational model for trafficking dynamics of the IL-2 receptor (IL-2R) system, which is able to predict T cell proliferation responses to IL-2. This model comprises kinetic equations describing binding, internalization, and postendocytic sorting of IL-2 and IL-2R, including an experimentally derived dependence of cell proliferation rate on these properties. Computational results from this model predict that IL-2 depletion can be reduced by decreasing its binding affinity for the IL-2R betagamma subunit relative to the alpha subunit at endosomal pH, as a result of enhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2 analogue with such altered binding properties should exhibit increased potency for stimulating the T cell proliferation response. These results are in agreement with our recent experimental findings for the IL-2 analogue termed 2D1 [Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type of model may enable prediction of beneficial cytokine/receptor binding properties to aid development of molecular design criteria for improvements in applications such as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Fallon EM, Lauffenburger DA. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
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.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

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
  • Computational model for effects of ligand/receptor binding properties on interleukin-2 trafficking dynamics and T cell proliferation response.
  • Fallon EM, Lauffenburger DA
  • Biotechnology progress , 0/ 2000 , Volume 16 , Issue 5 , pages: 905-916 , PubMed ID: 11027188
  • Department of Chemical Engineering, Biotechnology Process Engineering Center, and Division of Bioengineering & Environmental Health, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
  • Multisubunit cytokine receptors such as the heterotrimeric receptor for interleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance in biotechnology and are crucial regulators of cell proliferation and differentiation behavior. Dynamics of cytokine/receptor endocytic trafficking can significantly impact cell responses through effects of receptor down-regulation and ligand depletion, and in turn are governed by ligand/receptor binding properties. We describe here a computational model for trafficking dynamics of the IL-2 receptor (IL-2R) system, which is able to predict T cell proliferation responses to IL-2. This model comprises kinetic equations describing binding, internalization, and postendocytic sorting of IL-2 and IL-2R, including an experimentally derived dependence of cell proliferation rate on these properties. Computational results from this model predict that IL-2 depletion can be reduced by decreasing its binding affinity for the IL-2R betagamma subunit relative to the alpha subunit at endosomal pH, as a result of enhanced ligand sorting to recycling vis-à-vis degradation, and that an IL-2 analogue with such altered binding properties should exhibit increased potency for stimulating the T cell proliferation response. These results are in agreement with our recent experimental findings for the IL-2 analogue termed 2D1 [Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type of model may enable prediction of beneficial cytokine/receptor binding properties to aid development of molecular design criteria for improvements in applications such as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.
Contributors
Submitter of the first revision: Camille Laibe
Submitter of this revision: administrator
Modellers: administrator, Camille Laibe

Metadata information

is (2 statements)
BioModels Database MODEL1006230001
BioModels Database BIOMD0000000665

isDescribedBy (1 statement)
PubMed 11027188

hasPart (1 statement)
Gene Ontology endocytosis

hasProperty (1 statement)
Mathematical Modelling Ontology Ordinary differential equation model

isVersionOf (1 statement)

Curation status
Curated


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Model files

BIOMD0000000665_url.xml SBML L2V4 representation of Fallon2000 - Interleukin-2 dynamics 73.66 KB Preview | Download

Additional files

BIOMD0000000665-biopax2.owl Auto-generated BioPAX (Level 2) 21.69 KB Preview | Download
BIOMD0000000665-biopax3.owl Auto-generated BioPAX (Level 3) 32.50 KB Preview | Download
BIOMD0000000665.m Auto-generated Octave file 7.92 KB Preview | Download
BIOMD0000000665.pdf Auto-generated PDF file 188.74 KB Preview | Download
BIOMD0000000665.png Auto-generated Reaction graph (PNG) 66.61 KB Preview | Download
BIOMD0000000665.sci Auto-generated Scilab file 186.00 Bytes Preview | Download
BIOMD0000000665.svg Auto-generated Reaction graph (SVG) 33.66 KB Preview | Download
BIOMD0000000665.vcml Auto-generated VCML file 900.00 Bytes Preview | Download
BIOMD0000000665.xpp Auto-generated XPP file 5.32 KB Preview | Download
BIOMD0000000665_urn.xml Auto-generated SBML file with URNs 73.68 KB Preview | Download
MODEL1006230001.cps COPASI File 92.10 KB Preview | Download
MODEL1006230001.sedml SED-ML file for figure 5b. 4.17 KB Preview | Download

  • Model originally submitted by : Camille Laibe
  • Submitted: Jun 23, 2010 10:11:49 AM
  • Last Modified: Jan 30, 2018 10:50:31 AM
Revisions
  • Version: 3 public model Download this version
    • Submitted on: Jan 30, 2018 10:50:31 AM
    • Submitted by: administrator
    • With comment: Model name updated using online editor.
  • Version: 2 public model Download this version
    • Submitted on: Jun 25, 2010 1:00:33 PM
    • Submitted by: Camille Laibe
    • With comment: Current version of Fallon2000_IL2dynamics
  • Version: 1 public model Download this version
    • Submitted on: Jun 23, 2010 10:11:49 AM
    • Submitted by: Camille Laibe
    • With comment: Original import of Fallon2000_IL2dynamics

(*) 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
Cs_0 => Ci_0 COMpartment*ke*Cs_0 ke = 0.04 1/(0.0166667*s)
Ri_0 => Ci_0; Li_0 COMpartment*kfe*Li_0*Ri_0 kfe = 1.104E-4 0.06*nl/(mol*s)
Li_0 => COMpartment*kx*Li_0 kx = 0.15 1/(0.0166667*s)
Ci_0 => Ld_0 COMpartment*kh*Ci_0 kh = 0.035 1/(0.0166667*s)
Ci_0 => Ri_0 COMpartment*kre*Ci_0 kre = 0.1104 1/(0.0166667*s)
=> Rs_0; Cs_0 COMpartment*ksyn*Cs_0 ksyn = 0.0011 1/(0.0166667*s)
Rs_0 => Ri_0 COMpartment*kt*Rs_0 kt = 0.007 1/(0.0166667*s)
Curator's comment:
(added: 30 Jan 2018, 10:48:33, updated: 30 Jan 2018, 10:48:33)
A figure similar to figure 5B of the reference publication was produced. The figure shows cell-surface complex (Cs) concentration for varied dissociation rate constants (kr) between 0.00138 and 0.138. The simulations were performed in COPASI 4.22 (Build 170) and the figure was generated in MATLAB R2014.