SPECIAL ISSUE EDITORS: Christian Hellmich and Dinesh Katti
May 1, 2009

Integrated Model of IGF-I Mediated Biosynthesis in a Deformed Articular Cartilage

Publication: Journal of Engineering Mechanics
Volume 135, Issue 5

Abstract

Maintenance of articular cartilage’s functional mechanical properties ultimately depends on the balance between the extracellular matrix component biosynthesis, degradation, and loss. A variety of factors are known to modulate the rate of cartilage matrix synthesis (e.g., growth factors and stress/strain environment). In the present study, we develop an integrated mathematical model that quantifies biological processes within cartilage tissue modulated by insulin-like growth factors (IGFs). Specifically, the model includes IGF transport through a deforming porous media, competitive binding to binding proteins and cell receptors, and matrix macromolecule biosynthesis—particularly glycosaminoglycans (GAGs). These newly synthesized matrix molecules are then able to modify the material properties of cartilage. The model is used to investigate the effect of synovial fluid IGF-I concentration on cartilage homeostasis. The results presented here suggest that GAG production can be rapidly “switched on” when the concentration of IGF-I reaches a certain threshold, while it is predicted that high receptor concentration leads to heterogeneous matrix production. As for the combined effect of IGF-I and mechanical loading on biosynthesis, the current model predicts that a loading regime with high strain magnitude (e.g., 10%) can achieve a synergistic effect on matrix protein production. Furthermore, dynamic loading is seen to promote spatial homogeneous GAG production.

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Acknowledgments

The writers wish to thank the Australian Research Council (UNSPECIFIEDDP50192) and the University of Melbourne for their support.

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Go to Journal of Engineering Mechanics
Journal of Engineering Mechanics
Volume 135Issue 5May 2009
Pages: 439 - 449

History

Received: Jul 2, 2007
Accepted: Mar 3, 2008
Published online: May 1, 2009
Published in print: May 2009

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Authors

Affiliations

Lihai Zhang [email protected]
Dept. of Civil and Environmental Engineering, Univ. of Melbourne, VIC 3010, Australia. E-mail: [email protected]
Bruce Stuart Gardiner [email protected]
Engineering Computational Biology Group, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Crawley, WA 6009, Australia. E-mail: [email protected]
David Wamsley Smith [email protected]
Faculty of Engineering, Computing and Mathematics, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. E-mail: [email protected]
Peter Pivonka [email protected]
Engineering Computational Biology Group, School of Computer Science and Software Engineering, The University of Western Australia, Perth, Crawley, WA 6009, Australia. E-mail: [email protected]
Alan Jay Grodzinsky [email protected]
Center for Biomedical Engineering, Dept. of Electrical Engineering and Computer Science, Dept. of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139. E-mail: [email protected]

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