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Bone’s Interpretation of Local Mechanical Signals
The osteocyte lacunar network within bone is responsible for sensing mechanical signals, transducing them to biological signals, and orchestrating bone remodeling. Yet, the details of mechanical sensation are not understood and we must approach this topic computationally on the microscopic level.
Keywords: data analysis, bone remodeling, mechanical loading, computational modeling
Bone responds and adapts to its environment continuously. This adaptation is due to the mechanical and chemical signals in which the organ is constantly immersed. At the cell level, the osteocytes are responsible for the orchestration of adaptation and sit inside individual cave-like structures within the bone matrix called lacunae. These lacunae are distributed throughout the bone matrix and collectively create an intricate network that is connected both to the bone surface and to the nearby lacunar structures via biological wires called dendrites. Therefore, understanding the mechanical signals both within and surrounding each lacunar structure (mechanical sensor) is paramount to learning how this network coordinates bone adaptation.
We have recently begun to investigate this question by performing micro finite element analyses (micro-FE) on small regions of human bone that were imaged using ultra-high resolution micro computed tomography (micro-CT) with a nominal voxel resolution of 1.2 micrometers. Each image volume consists of a full human bone biopsy with thousands of lacunae present. Initial findings suggest that the lacunae experience and respond best to the mechanical metric known as strain energy density (SED). Yet, while it is clear that lacunar shape and size are important for the mechanics of every lacuna, it is not very well understood how this network works as a whole. For example, how does the size and orientation of one lacuna influence its immediate and distant neighbors? Can that then be linked with tissue parameters such as bone volume and trabecular thickness? These and other similar exciting scientific questions are what the student can expect to explore in this project.
Bone responds and adapts to its environment continuously. This adaptation is due to the mechanical and chemical signals in which the organ is constantly immersed. At the cell level, the osteocytes are responsible for the orchestration of adaptation and sit inside individual cave-like structures within the bone matrix called lacunae. These lacunae are distributed throughout the bone matrix and collectively create an intricate network that is connected both to the bone surface and to the nearby lacunar structures via biological wires called dendrites. Therefore, understanding the mechanical signals both within and surrounding each lacunar structure (mechanical sensor) is paramount to learning how this network coordinates bone adaptation.
We have recently begun to investigate this question by performing micro finite element analyses (micro-FE) on small regions of human bone that were imaged using ultra-high resolution micro computed tomography (micro-CT) with a nominal voxel resolution of 1.2 micrometers. Each image volume consists of a full human bone biopsy with thousands of lacunae present. Initial findings suggest that the lacunae experience and respond best to the mechanical metric known as strain energy density (SED). Yet, while it is clear that lacunar shape and size are important for the mechanics of every lacuna, it is not very well understood how this network works as a whole. For example, how does the size and orientation of one lacuna influence its immediate and distant neighbors? Can that then be linked with tissue parameters such as bone volume and trabecular thickness? These and other similar exciting scientific questions are what the student can expect to explore in this project.
To answer these questions and improve our understanding on the topic the student must use computational and imaging skills to manipulate image volumes, hone in on individual lacunae, capture the local mechanics, and then compare with the surrounding lacunae and the entire network of lacunae. This will be done by developing a pipeline in which a single lacuna location can be chosen and then linked to the surrounding mechanical signals and its neighbors via the corresponding micro-FE output. Ultimately, a database of many individual lacunae that includes location and mechanics information relative to its neighbors will need to be created. Our lab uses a programming framework built in Python and hence the student’s project will also be centered around Python. Previous programming experience (in Python, Matlab, or other related language) is preferable.
To answer these questions and improve our understanding on the topic the student must use computational and imaging skills to manipulate image volumes, hone in on individual lacunae, capture the local mechanics, and then compare with the surrounding lacunae and the entire network of lacunae. This will be done by developing a pipeline in which a single lacuna location can be chosen and then linked to the surrounding mechanical signals and its neighbors via the corresponding micro-FE output. Ultimately, a database of many individual lacunae that includes location and mechanics information relative to its neighbors will need to be created. Our lab uses a programming framework built in Python and hence the student’s project will also be centered around Python. Previous programming experience (in Python, Matlab, or other related language) is preferable.
Elliott Goff (elliott.goff@hest.ethz.ch), Institute for Biomechanics, ETH Zurich Professorship Ralph Müller (Department of Health Sciences and Technology)
Elliott Goff (elliott.goff@hest.ethz.ch), Institute for Biomechanics, ETH Zurich Professorship Ralph Müller (Department of Health Sciences and Technology)