The mechanical stimuli generated by body exercise can be transmitted from cortical bone into the deep bone marrow. A mechanosensitive perivascular stem cell niche is recently identified within the bone marrow for osteogenesis and lymphopoiesis. However, the mechanopropagation from compact bone to deep bone marrow vasculature remains elusive in this fundamental mechanobiology field. No experimental system is available yet to directly understand such exercise‐induced mechanopropagation at the bone‐vessel interface. To this end, an integrated computational biomechanics framework to quantitatively evaluate the mechanopropagation capabilities of bone marrow arterioles, arteries, and sinusoids is devised. The 3D geometries of blood vessels are smoothly reconstructed in the presence of vessel wall thickness and intravascular pulse pressure, followed by finite element analysis to thoroughly investigate the mechanical effects of exercise‐induced intravascular vibratory stretching on bone marrow vasculature. The effects of blood pressure and cortical bone bending are also examined. It is concluded that arterioles and arteries are much more efficient in transducing mechanical force than sinusoids due to their higher stiffness. In the future, this in-silico approach could be combined with other clinical imaging modalities for subject/patient‐specific vascular reconstruction and biomechanical analysis, providing large‐scale phenotypic data for personalized mechanobiology discovery.
Skills and Experience
You will have:
· Academic knowledge in the discipline of biophysics, biomechanics, electrophysiology, cell biology and biochemistry;
· Experience of Linux/Unix commanding line (Unix shell)
· Capability of using two or more of ANSYS, COMSOL, Abaqus, LabVIEW, Python, AutoCAD, MATLAB and other software.
Preferred experience include:
· Solid basic knowledge of biology and hands-on experience in PC2 biological laboratory, using flow cytometer, ELISA, Western blots, protein-protein interaction assays, protein/antibody purification and functional characterizations;
· Experience in theoretical simulation using and MATLAB or COMSOL, or LabVIEW programming to control equipment and devices.
· Capability of independently output processing models and drawings, be capable of CNC programming, use other conventional processing platform equipment to manufacture mechanical parts, and use 3D printers for part manufacturing.
· Pre-doctoral track records with publications, conference papers, reports, professional or technical contributions with evidence of independent research ability.
· Excellent oral and written communication skills.