Bone is a fascinating organ and material with the unique capability of scarless self-repair. Due to its hierarchical architecture, bone succeeds to be a very stiff yet tough material (Peterlik et al. 2006). However, fractures of bone occur not only due to excessive loads, but also in non-traumatic situations due to an impaired mechanical performance of bone caused by diseases. Clinical studies estimate the percentage of eventful (problematic) fracture healing cases between 5-20% including fractures due to bone diseases such as osteoporosis (Coles and Gross 2000; 2000; Lynch et al.
2008). As the average human life time increases, the percentage of people with bone diseases will increase. Therefore, the issues arising with regards to eventful bone healing are becoming more urgent (Gruber et al. 2006; Jakob et al. 2007). The number of fractures in the European Union due to osteoporosis was estimated at 3.8 million in the year 2000 with related costs for clinical treatment at 32 billion Euros (Reginster and Burlet 2006). Therefore, insights into the process of fracture healing as well as a better understanding of bone as a material and its organisation are desirable in order to improve the prevention and treatment of patients, reducing the resulting costs. The behaviour of cells is very difficult to measure in vivo. Computational modelling is an important tool to gain a better understanding of the cell behaviour during bone healing. As a long term goal, these insights into regenerative processes of the human body could be further used in other fields of medicine.
Bone has several tasks besides the “mechanical engineering” tasks of shaping our body (by preventing bending and buckling of our extremities, e.g.) and protection of other organs. The main tasks of bone as an organ are providing a calcium reservoir for the body and producing different types of blood cells (within the bone marrow).
The aim of this study was to understand better the biological or biochemical and mechanical influences on the progression of bone healing. To start with, the aim was to understand the “normal” progression of healing in healthy sheep. Accordingly, the influence of the bone`s organ tasks on the healing progression were neglected in this work. Bone healing is controlled by a myriad of influences (smokers, for example, face more probably difficulties during healing). The main task of this thesis was to filter the important influences on a normal healing progression. This task was tackled from a physical viewpoint by post-processing available experimental data, using mechanobiological computer simulations and representing the comprehensive computational results in phase diagrams.
Mechanobiology is a rather young research field which is closely related to the older research field of Biomechanics. This introduction aims to clarify the difference between Mechanobiology and Biomechanics and outlines the benefit and the inherent inter disciplinarity of these two akin research fields. The link between these two research fields is a process called “Mechanotransduction”.
Biological processes are generally very complex, specifically fracture healing.
Therefore, significant background knowledge is required in order to model bone healing. Chapter 2 aims to provide knowledge of bone fracture healing and the underlying processes followed by the formulation of specific aims of this thesis.
Chapter 3 gives a summary of the available experimental data which was used in this thesis for computational modelling. Chapters 4 to 9 comprise the work which has been carried out in the framework of this thesis. A glossary of biological and clinical terms can be found in the appendix.
Biomechanics
Biomechanics investigates the whole body or parts of the body and their functions from a mechanical viewpoint or in other words, “biomechanics seeks to understand the mechanics of living systems” (Fung 2004). Already in 17th century, Galileo Galilei compared the diameter of leg bones of different animal species and estimated the demanded ratio of the diameter depending on the weight of the animal (Galilei 1638). Recent achievements of applied biomechanics have helped to solve problems not only in orthopaedics but in various other areas of regenerative medicine.
Biomechanical research labs have designed and optimized artificial heart valves, stents and hip implants, just to name some examples. Biomechanical research labs also measure the material properties of different tissues, which is an important input for mechanobiological studies. The material properties together with the load determine the stresses, interstitial fluid flow and strains within the tissue which can be sensed by biological cells. For example, shear strains are often considered as mechanical stimulus which can stimulate or activate a cell to produce tissue.
Mechanotransduction
In general, the cellular process of response to an activating mechanical stimulus such as strain is called mechanotransduction, meaning the transcription of a mechanical signal into a biochemical signalling cascade (Morgan et al. 2008). A certain signalling cascade is the response of the cell to a stimuli which leads to a specific biological
eventually differentiate according to mechanical, biological, chemical, or electrical stimulation.
Mechanobiology
Tissues with cells are living materials which have the ability to sense their physical environment and react with an adaptive response. The rather young field of mechanobiology wants to “predict growth and differentiation [of tissue and cells – the author] in quantitative terms, based on a given force exerted on a given tissue matrix populated by cells” (van der Meulen and Huiskes 2002). Naturally,
“mechanobiological research” is based on a very interdisciplinary approach. One of the early mechanobiological studies was made by the medical doctor Julius Wolff in the late 1800s. Wolff investigated the architecture of trabecular bone by applying engineering methods and hypothesised what is now called functional adaptation of bone (Wolff 1892). Accordingly, bone trabeculae are built at locations where they are mechanically required and removed where they are not required (Robling et al.
2006). Hip implants can be optimized following Wolff’s law. The lifetime of hip implants were significantly increased by avoiding a stress-shielding of the bone surrounding the implant (Huiskes et al. 1992). Too low mechanical stimulation leads to bone resorption and ultimately to a loosening of the implant.
With increasing experimental data, theoretical mechanobiological theories were suggested for different endogeneous processes with the main aim to better understand the mechanical influences on the process and the cells by modelling the cellular behaviour in silico. Several studies showed the potential impact of mechanobiological simulations. For example, computational models on bone remodelling were able to predict the natural aging of trabecular bone and provided evidence for the existence of a threshold value above which bone deposition by the cells is activated (Weinkamer et al. 2004; Dunlop et al. 2009).
The main aim of this thesis is to examine to which extent a basic mechanobiological model can explain the local development of different tissue types in a fracture site during bone healing.