Register now After registration you will be able to apply for this opportunity online.
This opportunity is not published. No applications will be accepted.
In silico bone adaptation simulations of mouse caudal vertebra
This project aims to investigate the incorporation and simulation of cyclic dynamic loading in a validated in silico load-adaptive bone adaptation algorithm.
Keywords: computational, mechanics, modelling, micro-CT, mouse, vertebra, python, in-vivo, medical engineering, biomedical engineering, image, processing, finite element analysis, programming, high-performance computing, image analysis, bone, in silico, simulation, adaptation
The precise effects of mechanical loading in the regulation of bone remodelling are still not well understood. When coupled with in vivo micro-Computed Tomography (micro-CT), the structural bone adaptation can be tracked longitudinally, from which static and dynamic morphometric parameters can be directly analysed. Using a previously established in vivo animal loading model, load-induced bone adaptation in mouse caudal vertebrae has been shown to logarithmically depend on loading frequency. Understanding how mechanical loading influences bone formation and resorption can help to define more effective treatment plans for degenerative conditions such as osteoporosis.
Aside from experimental studies, in silico bone simulations have been shown to realistically adapt bone structures obtained from micro-CT images to given dynamic loading conditions. The in silico model mentioned above is based on an implementation of the mechanostat theory and uses strain energy density (SED), determined from static micro Finite Element Analysis, as the mechanical signal driving bone adaptation. Remarkably, data generated with this model accurately reflected the effects of different treatment plans and static and cyclic loading conditions in mice seen in respective experimental data [see attached documents].
The current project aims to explore the integration of cyclic dynamic loading into this model and investigate whether the frequency-dependent bone adaptation observed in vivo can be reproduced with the above-mentioned load-adaptive bone adaptation algorithm.
In summary, with this combined experimental and computational approach, we seek to expand our ability to extract relevant parameters from in vivo experiments and explore the capabilities of the existing in silico model of bone adaptation to simulate diverse conditions of bone remodelling.
The precise effects of mechanical loading in the regulation of bone remodelling are still not well understood. When coupled with in vivo micro-Computed Tomography (micro-CT), the structural bone adaptation can be tracked longitudinally, from which static and dynamic morphometric parameters can be directly analysed. Using a previously established in vivo animal loading model, load-induced bone adaptation in mouse caudal vertebrae has been shown to logarithmically depend on loading frequency. Understanding how mechanical loading influences bone formation and resorption can help to define more effective treatment plans for degenerative conditions such as osteoporosis. Aside from experimental studies, in silico bone simulations have been shown to realistically adapt bone structures obtained from micro-CT images to given dynamic loading conditions. The in silico model mentioned above is based on an implementation of the mechanostat theory and uses strain energy density (SED), determined from static micro Finite Element Analysis, as the mechanical signal driving bone adaptation. Remarkably, data generated with this model accurately reflected the effects of different treatment plans and static and cyclic loading conditions in mice seen in respective experimental data [see attached documents]. The current project aims to explore the integration of cyclic dynamic loading into this model and investigate whether the frequency-dependent bone adaptation observed in vivo can be reproduced with the above-mentioned load-adaptive bone adaptation algorithm. In summary, with this combined experimental and computational approach, we seek to expand our ability to extract relevant parameters from in vivo experiments and explore the capabilities of the existing in silico model of bone adaptation to simulate diverse conditions of bone remodelling.
This project aims to investigate bone adaptation in silico in an attempt to reproduce phenomena observed in vivo, with a validated load-adaptive bone adaptation algorithm. The work will be done in Python, using a model of bone remodelling of grayscale images available in the group.
This project aims to investigate bone adaptation in silico in an attempt to reproduce phenomena observed in vivo, with a validated load-adaptive bone adaptation algorithm. The work will be done in Python, using a model of bone remodelling of grayscale images available in the group.
Francisco Correia Marques (francisco.correia@hest.ethz.ch), HCP H Leopold-Ruzicka-Weg 4, 8093 Zürich, Switzerland
Francisco Correia Marques (francisco.correia@hest.ethz.ch), HCP H Leopold-Ruzicka-Weg 4, 8093 Zürich, Switzerland