Journal article
Predicting tenocyte expression profiles and average molecular concentrations in Achilles tendon ECM from tissue strain and fiber damage
Biomechanics and Modeling in Mechanobiology, Vol.16(4), pp.1329-1348
2017
Abstract
In this study, we propose a method for quantitative prediction of changes in concentrations of a number of key signaling, structural and effector molecules within the extracellular matrix of tendon. To achieve this, we introduce the notion of elementary cell responses (ECRs). An ECR defines a normal reference secretion profile of a molecule by a tenocyte in response to the tenocyte’s local strain. ECRs are then coupled with a model for mechanical damage of tendon collagen fibers at different straining conditions of tendon and then scaled up to the tendon tissue level for comparison with experimental observations. Specifically, our model predicts relative changes in ECM concentrations of transforming growth factor beta, interleukin 1 beta, collagen type I, glycosaminoglycan, matrix metalloproteinase 1 and a disintegrin and metalloproteinase with thrombospondin motifs 5, with respect to tendon straining conditions that are consistent with the observations in the literature. In good agreement with a number of in vivo and in vitro observations, the model provides a logical and parsimonious explanation for how excessive mechanical loading of tendon can lead to under-stimulation of tenocytes and a degenerative tissue profile, which may well have bearing on a better understanding of tendon homeostasis and the origin of some tendinopathies.
Details
- Title
- Predicting tenocyte expression profiles and average molecular concentrations in Achilles tendon ECM from tissue strain and fiber damage
- Authors/Creators
- A. Mehdizadeh (Author/Creator) - Australian University, KuwaitB.S. Gardiner (Author/Creator) - Murdoch UniversityM. Lavagnino (Author/Creator) - Michigan State UniversityD.W. Smith (Author/Creator) - The University of Western Australia
- Publication Details
- Biomechanics and Modeling in Mechanobiology, Vol.16(4), pp.1329-1348
- Publisher
- Springer Verlag
- Identifiers
- 991005540831307891
- Copyright
- © 2017 Springer-Verlag Berlin Heidelberg
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
20 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.34 Orthopedics
- 1.34.982 Tendon Therapies
- Web Of Science research areas
- Biophysics
- Engineering, Biomedical
- ESI research areas
- Molecular Biology & Genetics