Julius Wolff Institut (JWI) - Center for Musculoskeletal Biomechanics and Regeneration 

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Computational Mechanobiology

Tissues in the musculoskeletal system are exquisitely designed with superb mechanical properties. The tissues are also able to adapt to withstand changing mechanical conditions. The Computational Mechanobiology Group is focused on understanding these two exciting features. Using computer modeling techniques, we seek to understand the mechanical behavior of tissues and their adaptive and regenerative response to mechanical stimuli at the different time and length scales.

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Feaured Publications

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  • Borgiani E, Figge C, Kruck B, Willie BM, Duda GN, Checa S

    Age-related changes in the mechanical regulation of bone healing are explained by altered cellular mechanoresponse


    We used a combined in vivo/in silico approach to investigate age-related alterations in the mechanical regulation of bone healing and identified the relative impact of altered cellular function on tissue patterns during the regenerative cascade. To modulate the mechanical environment, femoral osteotomies in adult and elderly mice were stabilized using either a rigid or a semirigid external fixator and the course of healing was evaluated using histomorphometric and microCT analyses at 7, 14 and 21 days post-surgery. Computer models were developed to investigate the influence of the local mechanical environment within the callus on tissue formation patterns.

    J Bone Miner Res 2019; doi: 10.1002/jbmr.3801

  • Cilla M, Borgiani E, Martinez J, Duda GN, Checa S

    Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant


    The aim of this project was to investigate if the geometry of a commercial short stem hip prosthesis can be further optimized to reduce stress shielding effects and achieve better short-stemmed implant performance. To reach this aim, the potential of machine learning techniques combined with parametric Finite Element analysis was used. The selected implant geometrical parameters were: total stem length (L), thickness in the lateral (R1) and medial (R2) and the distance between the implant neck and the central stem surface (D). The results show that the total stem length was not the only parameter playing a role in stress shielding. An optimized implant should aim for a decreased stem length and a reduced length of the surface in contact with the bone.

    PLoS One 2017; 12(9):e0183755.


Publications

Results 1 to 10 of total 66


Results 1 to 10 of total 66