Date of Award
Doctor of Philosophy (PhD)
Jeremy Mercuri, PhD
William Richardson, PhD
Jiro Nagatomi, PhD
Delphine Dean, PhD
Mechanical stimulation through physical activity has been shown to play an important role in treating and preventing several non-communicable diseases such as hypertension, lower back pain (LBP), type-2 diabetes mellitus, and several cancers. This is accomplished through the regulation of cellular behavior and tissue remodeling within the body at both the micro- and macro-scale levels. The goal of mechanobiology research is to gain in-depth knowledge and understanding of how cells sense physical forces in conjunction with other biochemical cues and translate those factors into important biological functions that either maintain tissue homeostasis or lead to pathological states. Understanding these processes can lead to the development of medical interventions that influence cells towards a desired outcome. While some of these processes can be observed naturally through thoughtful experimental design, most mechanobiological processes require the development of tools that can enable scientists and researchers to investigate a specific aspect of cell mechanobiology.
In this PhD dissertation, we investigated the role that mechanical stimulation plays in regulating cell behavior in different chemical environments by subjecting cells cultured in monolayer to levels of uniaxial tension similar to those experienced in the annulus fibrosus (AF) in the intervertebral disc (IVD). We then developed both an experimental and computational platform that could be used to study the effects of mechanical stimulation on physiological function at both the microscopic and macroscopic levels. The experimental platform was designed to enable long term application of mechanical forces onto cell-seeded tissue scaffolds with the intention of developing a bioreactor to enable in vitro studies focused on investigating how mechanical forces influence cell behavior in healthy and diseased IVDs. The computational platform employed machine learning algorithms and data science strategies to examine how clinical measurements such as blood pressure and blood serum analytes influence patient risk for masked hypertension in a young, apparently healthy population.
Miller, Brendyn, "Experimental and Computational Platforms for Studying Systems Mechanobiology" (2023). All Dissertations. 3516.