Date Thesis Awarded
Honors Thesis -- Access Restricted On-Campus Only
Bachelors of Science (BS)
One way to study disease is to model specific biological reactions or processes involved in the generation of the disease in terms of a system of differential equations. The equations, called kinetic rate laws, are often non-linear and high order, making them difficult to solve. By approximating equations in complex biological networks as linear first order reactions, we can solve large sets of equations using computational software, such as MATLAB, to determine general trends in the change of molecular concentrations over time. These trends can tell us details about the disease and direct us toward areas worthy of further investigation. We can gain additional information concerning the potential behavior of a disease by superimposing its signaling network over a spatial approximation. In our work, we were able to generate a representation of a small volume of the human neocortex by modeling neurons cylinders. Cylinders act as a reliable model to describe the approximate radial symmetry of neurons. We also derived probability density equations for the dendrites and axons of each neuros. The model system is flexible so that any set of differential equations can be superimposed onto it. We plan to run our own devised system of equations for prion disease on the spatial model to see how its results differ from those produced by the kinetic equations alone.
Stephens, Christina Alexandra, "Creating a Computational Model of Prion Disease in the Human Neocortex" (2016). Undergraduate Honors Theses. William & Mary. Paper 938.
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