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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.. 5, 16.\n                            Department of Chemical Engineering, Biotechnology Process Engineering Center, and Division of Bioengineering & Environmental Health, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA."],"submitter_mail":["laibe@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"publicationId":["BIOMD0000000665"],"pubmed_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-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."],"pubmed_title":["Computational model for effects of ligand/receptor binding properties on interleukin-2 trafficking dynamics and T cell proliferation response."],"pubmed_authors":["Fallon E M EM, Lauffenburger D A DA"],"name_synonyms":["Thymocyte Stimulating Factor, Ro-23-6019, Interleukin II, aldesleukin, Ro 236019, TCGF, Mitogenic Factor, IL-2, T-cell growth factor, Ro 23 6019, IL2, Ro236019, T-Cell Stimulating Factor, RU49637, Ro-236019, T-Cell Growth Factor, T Cell Stimulating Factor, Interleukin 2, RU-49637, T Cell Growth Factor., Lymphocyte Mitogenic Factor, RU 49637, Lymphocyte, Interleukine 2"],"pubmed_abstract_synonyms":["biochemical pathways, Fermentors, T-lymphocyte proliferation, MGC130048, PS, Derived, achi/vis, 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TAF1"],"pubmed_title_synonyms":["T-lymphocyte proliferation, reactivity, Thymocyte Stimulating Factor, Ligand, aldesleukin, Ro 236019, Mitogenic Factor, Effects, Computing, Computed, ligand, RU49637, Ro-236019, T-Cell Growth Factor, Interleukin 2, responsivity., Lymphocyte Mitogenic Factor, Computational, T Cell Growth Factor, T-cell proliferation, Ro-23-6019, Interleukin II, TCGF, IL-2, T-cell growth factor, Computational Technique, Ro 23 6019, IL2, Ro236019, T-Cell Stimulating Factor, T Cell Stimulating Factor, Computation, response, receptor ligand, receptor-associated protein activity, RU-49637, Effect, T lymphocyte proliferation, RU 49637, Lymphocyte, Interleukine 2"],"additional_accession":[]},"is_claimable":false,"name":"Fallon2000 - Interleukin-2 dynamics","description":"\n      \n        This a model from the article:      \n        Computational model for effects of ligand/receptor binding properties on\ninterleukin-2 trafficking dynamics and T cell proliferation response.\n        \n          Fallon EM, Lauffenburger DA.      Biotechnol Prog\n          2000 Sep-Oct;16(5):905-16      11027188\n          ,      \n        Abstract:\n        \n          Multisubunit cytokine receptors such as the heterotrimeric receptor for\ninterleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance in\nbiotechnology and are crucial regulators of cell proliferation and\ndifferentiation behavior. Dynamics of cytokine/receptor endocytic trafficking\ncan significantly impact cell responses through effects of receptor\ndown-regulation and ligand depletion, and in turn are governed by\nligand/receptor binding properties. We describe here a computational model for\ntrafficking dynamics of the IL-2 receptor (IL-2R) system, which is able to\npredict T cell proliferation responses to IL-2. This model comprises kinetic\nequations describing binding, internalization, and postendocytic sorting of IL-2\nand IL-2R, including an experimentally derived dependence of cell proliferation\nrate on these properties. Computational results from this model predict that\nIL-2 depletion can be reduced by decreasing its binding affinity for the IL-2R\nbetagamma subunit relative to the alpha subunit at endosomal pH, as a result of\nenhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2\nanalogue with such altered binding properties should exhibit increased potency\nfor stimulating the T cell proliferation response. These results are in\nagreement with our recent experimental findings for the IL-2 analogue termed 2D1\n[Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type of\nmodel may enable prediction of beneficial cytokine/receptor binding properties\nto aid development of molecular design criteria for improvements in applications\nsuch as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.      \n        This model was taken from the      CellML repository\n          and automatically converted to SBML.      \n          The original model was:      \n          Fallon EM, Lauffenburger DA. (2000) - version=1.0\n        \n        \n          The original CellML model was created by:      \n        Catherine Lloyd\n        \n          c.lloyd@auckland.ac.nz      \n          The University of Auckland      \n        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.      \n          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\n          for more information.      \n        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..      \n        \n          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.\n      \n    \n  ","dates":{"last_modification":"2024-08-22","publication":"2024-09-02","submission":"2010-06-23"},"accession":"BIOMD0000000665","cross_references":{"sbo":["SBO:0000179","SBO:0000180"],"reactome":["R-HSA-449836"],"pubmed":["11027188"],"ncit":["C104679","C126395"],"biomodels__db":["MODEL1006230001","BIOMD0000000665"],"go":["GO:0032800","GO:0006897","GO:0005886","GO:0005893","GO:0044424","GO:0005615","GO:0005134","GO:0031623","GO:0032801","GO:0019976"],"bto":["BTO:0000782"],"uniprot":["P14784","P31785","P60568"]}}