Investigations of human biology and disease have been hindered by the use of animal models. The information obtained from such studies often results in clinically irrelevant results and drug trial failures. Additionally, several governing bodies have been formulating legislation to move away from animal models and toward more ethical and efficient testing platforms for drug discovery and cosmetic research. As an answer to these issues, "body-on-a-chip" systems have been a rapidly developing field which easily recapitulates in vivo functionality, providing a more relevant, repeatable, and ethical testing platform to better predict biology. These systems can be used as human-based testing platforms to evaluate human physiology, disease progression, and drug responsiveness for specific cell types and multi-organ systems. Diseases such as amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA) have significant research challenges, specifically with translating research findings into treatment plans. The complexity of the neuromuscular reflex arc, the biological system affected by these diseases, is difficult to study with traditional molecular techniques, namely because the many components of this disease system interact with each other using complex pathways. This work pushes the existing platform to a more complete human model of neuromuscular disease with the incorporation of gamma motoneurons, development of the first human induced pluripotent cell (iPSC) derived intrafusal fibers, and proposals to incorporate nociceptive neurons all on a functionally interrogative platform. The incorporation of these components will allow for a more complete, clinically relevant model to study neuromuscular disorders and for preclinical dug discovery.
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Doctor of Philosophy (Ph.D.)
College of Sciences
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Colon, Alisha, "Development of a Functional Testing Platform for the Sensory Segment of the Neuromuscular Reflex Arc" (2019). Electronic Theses and Dissertations. 6821.
Restricted to the UCF community until February 2020; it will then be open access.