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MICRO-MULTI-PHYSICS AGENT-BASED MODELLING OF THE TRABECULAR BONE RESPONSE TO ESTROGEN DEPLETION
The proposed project will investigate the trabecular bone response to estrogen depletion and will be used to investigate the probability of the chosen mechanism of action for estrogen. The chosen mechanism of action will be validated using available experimental reference data.
Postmenopausal osteoporosis is a prevalent bone disease leading to decreased bone integrity and an increased risk of fractures. Bone as an organ is able to continuously change its internal trabecular microstructure to adapt to changing mechanical and metabolic demands [1]. Bone remodelling is executed by bone forming cells called osteoblasts and resorbing cells called osteoclasts. Bone sensing cells called osteocytes are believed to pass on the local micromechanical conditions to the executing cells. During postmenopausal osteoporosis, these cellular mechanisms are disturbed towards increased bone resorption [2]. The exact mechanism of action of the disease is not well understood to date.
Agent-based in silico models have the potential to investigate research areas, which are to date not accessible with experimental data. In silico models can simulate changes to bone structure allowing testing of the mechanisms of action of drug treatments and providing insight into individualised estimations of treatment effects [3].
This project is part of a wider effort to create a comprehensive in silico multi-physics agent-based model for the investigation of osteoporotic bone mechanophysiology and the bone response to treatments. The proposed work will implement and evaluate the bone response to estrogen depletion. This work will form the basis for subsequent in silico investigation of potential osteoporosis treatments. The chosen mechanism of action of estrogen will be validated against available experimental reference data. The validation criterion will be the resulting bone morphometry that the in silico model produced. Visual representations of the resulting three-dimensional bone structures will be generated. Finally, the project will be prepared and presented.
Literature:
[1] Burr DB. Targeted and nontargeted remodeling. Bone 2002;30:2–4.
[2] Riggs BL. et al., Overview of osteoporosis. Western Journal of Medicine 1991;154:63.
[3] Ruiz-Lozano R. et al., An in silico approach to elucidate the pathways leading to primary osteoporosis: age-related vs. postmenopausal. Biomech Model Mechanobiol. 2024.
Postmenopausal osteoporosis is a prevalent bone disease leading to decreased bone integrity and an increased risk of fractures. Bone as an organ is able to continuously change its internal trabecular microstructure to adapt to changing mechanical and metabolic demands [1]. Bone remodelling is executed by bone forming cells called osteoblasts and resorbing cells called osteoclasts. Bone sensing cells called osteocytes are believed to pass on the local micromechanical conditions to the executing cells. During postmenopausal osteoporosis, these cellular mechanisms are disturbed towards increased bone resorption [2]. The exact mechanism of action of the disease is not well understood to date.
Agent-based in silico models have the potential to investigate research areas, which are to date not accessible with experimental data. In silico models can simulate changes to bone structure allowing testing of the mechanisms of action of drug treatments and providing insight into individualised estimations of treatment effects [3].
This project is part of a wider effort to create a comprehensive in silico multi-physics agent-based model for the investigation of osteoporotic bone mechanophysiology and the bone response to treatments. The proposed work will implement and evaluate the bone response to estrogen depletion. This work will form the basis for subsequent in silico investigation of potential osteoporosis treatments. The chosen mechanism of action of estrogen will be validated against available experimental reference data. The validation criterion will be the resulting bone morphometry that the in silico model produced. Visual representations of the resulting three-dimensional bone structures will be generated. Finally, the project will be prepared and presented.
Literature: [1] Burr DB. Targeted and nontargeted remodeling. Bone 2002;30:2–4. [2] Riggs BL. et al., Overview of osteoporosis. Western Journal of Medicine 1991;154:63. [3] Ruiz-Lozano R. et al., An in silico approach to elucidate the pathways leading to primary osteoporosis: age-related vs. postmenopausal. Biomech Model Mechanobiol. 2024.
- Prepare a timetable of the work to be performed
- Perform a short literature review (3 papers) regarding the role of estrogen for the bone and the effects of estrogen depletion leading to postmenopausal osteoporosis
- Implement a potential way of action for estrogen depletion into an existing micro-MPA-model (Python)
- Assess the micro-MPA model accuracy (differences in bone morphometry from existing experimental validation data (Python, JupyterNotebook)
- Generate visual representations of the simulated bone response to estrogen depletion (ParaView, Python)
- Write a detailed report of the project • Prepare a presentation summarizing the project (MS PowerPoint)
- Prepare a timetable of the work to be performed - Perform a short literature review (3 papers) regarding the role of estrogen for the bone and the effects of estrogen depletion leading to postmenopausal osteoporosis - Implement a potential way of action for estrogen depletion into an existing micro-MPA-model (Python) - Assess the micro-MPA model accuracy (differences in bone morphometry from existing experimental validation data (Python, JupyterNotebook) - Generate visual representations of the simulated bone response to estrogen depletion (ParaView, Python) - Write a detailed report of the project • Prepare a presentation summarizing the project (MS PowerPoint)
Dr. Friederike Schulte, Laboratory for Bone Biomechanics, ETH Zurich, Switzerland, friederike.schulte@hest.ethz.ch
Dr. Friederike Schulte, Laboratory for Bone Biomechanics, ETH Zurich, Switzerland, friederike.schulte@hest.ethz.ch