Modeling the Insulin-Like Growth Factor System in Articular Cartilage
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Zhang-2013-Modeling the Insulin.pdf
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2.52 MB
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Author(s)
Zhang, Lihai
Smith, David W.
Gardiner, Bruce S.
Grodzinsky, Alan J.
Date Issued
June 2013
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Zhang, Lihai, David W. Smith, Bruce S. Gardiner, and Alan J. Grodzinsky. “Modeling the Insulin-Like Growth Factor System in Articular Cartilage.” Edited by Amina Ann Qutub. PLoS ONE 8, no. 6 (June 26, 2013): e66870.
Version
Final published version
Abstract
IGF signaling is involved in cell proliferation, differentiation and apoptosis in a wide range of tissues, both normal and diseased, and so IGF-IR has been the focus of intense interest as a promising drug target. In this computational study on cartilage, we focus on two questions: (i) what are the key factors influencing IGF-IR complex formation, and (ii) how might cells regulate IGF-IR complex formation? We develop a reaction-diffusion computational model of the IGF system involving twenty three parameters. A series of parametric and sensitivity studies are used to identify the key factors influencing IGF signaling. From the model we predict the free IGF and IGF-IR complex concentrations throughout the tissue. We estimate the degradation half-lives of free IGF-I and IGFBPs in normal cartilage to be 20 and 100 mins respectively, and conclude that regulation of the IGF half-life, either directly or indirectly via extracellular matrix IGF-BP protease concentrations, are two critical factors governing the IGF-IR complex formation in the cartilage. Further we find that cellular regulation of IGF-II production, the IGF-IIR concentration and its clearance rate, all significantly influence IGF signaling. It is likely that negative feedback processes via regulation of these factors tune IGF signaling within a tissue, which may help explain the recent failures of single target drug therapies aimed at modifying IGF signaling.
MIT Department
Massachusetts Institute of Technology. Center for Biomedical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0066870
