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Natasha Wilson, Ph.D.


Chemical, Biochemical and Environmental Engineering 2016

Area of Doctoral Study: Chemical and Biochemical Engineering

Undergraduate Institute: University of Maryland, Baltimore County

Research Advisors: Theresa Good, Ph.D and Mariajosé Castellanos, Ph.D

Current Position: Coordinator of Education and Outreach, Office of Respect Life, Archdiocese of Baltimore

Description of Research

β-amyloid(Aβ) is the primary protein component of senile plaques associated with Alzheimer’s disease (AD) histopathology. Because of Aβ’s longstanding implication in the initiating events leading to AD pathology in in vitro and in vivo experimental preparations, it is commonly considered the causative agent of AD. Yet, though well studied, there still exists a lack of consensus concerning Aβ’s interactions with neurons and resulting alterations in neuronal signaling. We hypothesize that this lack of consensus is due to an incomplete framework for interpreting experimental data and discriminating between different hypotheses for Aβ-neuron interactions. In our work, we propose two changes to this framework in order to design better experiments to elucidate Aβ’s interactions with neurons: 1) using computational models to discriminate between different hypotheses of Aβ-neuron interactions by making predictions and comparing them to experimental data, and 2) assume a complex intracellular signaling network model instead of a linear pathway hypothesis. By developing our models with these two changes in mind, our results demonstrate the ability of an electrophysiological neuron model to make discriminating predictions under experimentally testable conditions. Our results also show that a complex, intracellular signaling model reveals that 20 years of experimental data collected investigating Aβ-induced intracellular signaling are not self-consistent. With data that is more consistent internally and with a complex, intracellular network, the methodology we developed has the potential to discriminate between hypotheses of Aβ-neuron interactions. Finally, we demonstrate, using network analysis, the need to move away from a simple, linear pathway hypothesis toward a more complex system and how the inconsistencies in our dataset collected from the literature could have arisen. We make recommendations of discriminating experiments using path length analysis and our Signal Flow Method that we developed. With further development of these computational tools, we can move closer to designing experiments to identify Aβ-neuron interactions with greater discriminatory power. With this understanding of Aβ’s deleterious effects on neurons, better treatments can be designed. Furthermore, these methods could be applied to other protein-misfold (amyloid) diseases, such as Parkinson’s disease, or other diseases where a known agent interacts extracellularly via an unknown receptor in neurons to cause cellular dysfunction.