Adaptive Remodeling of Achilles Tendon: A Multi-scale Computational Model

Stuart R. Young, B. Gardiner, Arash Mehdizadeh, Jonas Rubenson, B. Umberger, David W. Smith

    Research output: Contribution to journalArticlepeer-review

    12 Citations (Scopus)
    10 Downloads (Pure)


    © 2016 Young et al. While it is known that musculotendon units adapt to their load environments, there is only a limited understanding of tendon adaptation in vivo. Here we develop a computational model of tendon remodeling based on the premise that mechanical damage and tenocyte-mediated tendon damage and repair processes modify the distribution of its collagen fiber lengths. We explain how these processes enable the tendon to geometrically adapt to its load conditions. Based on known biological processes, mechanical and strain-dependent proteolytic fiber damage are incorporated into our tendon model. Using a stochastic model of fiber repair, it is assumed that mechanically damaged fibers are repaired longer, whereas proteolytically damaged fibers are repaired shorter, relative to their pre-damage length. To study adaptation of tendon properties to applied load, our model musculotendon unit is a simplified three-component Hill-type model of the human Achilles-soleus unit. Our model results demonstrate that the geometric equilibrium state of the Achilles tendon can coincide with minimization of the total metabolic cost of muscle activation. The proposed tendon model independently predicts rates of collagen fiber turnover that are in general agreement with in vivo experimental measurements. While the computational model here only represents a first step in a new approach to understanding the complex process of tendon remodeling in vivo, given these findings, it appears likely that the proposed framework may itself provide a useful theoretical foundation for developing valuable qualitative and quantitative insights into tendon physiology and pathology.
    Original languageEnglish
    Article numbere1005106
    Pages (from-to)1-30
    JournalPLoS Computational Biology
    Issue number9
    Publication statusPublished - 29 Sep 2016


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