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Simulation of a Scalable Electrochemical Immunosignaturing Biosensor Array for Disease Diagnosis


The ability to detect diseases during early progression greatly impacts the effectiveness of treatments, especially for cancers. Current research focuses on discovering specific biomarkers associated with the disease, but these are difficult to discover and present in very low concentrations. Conversely, immunosignaturing leverages the immense amplification provide by the immune system to examine antibody patterns on a random array of peptides. This thesis explores a new electrochemical detection method instead of the traditional optical detection for use with the immunosignaturing chip. I developed a software simulation in order to investigate the parameters of the system. The first part of the simulation tracked the concentration changes of the molecules of interest. These concentrations of molecules drove the electrochemical discharges modeled in the next part of the simulation. The efficacy of the simulated discharges was determined by comparison with experimental discharge data. The first two parts of the simulation showed that crosstalk occurs with adjacent non-active sensors and the time delay before it happens largely depends on array geometry and sensor capacitance. The last part of the simulation explored the ability of this method to discern various diseases. Classification of a transformed optical data yielded similar classification accuracy compared to the original optical data. A mock end to end simulation demonstrated high accuracy as well. This thesis outlines a few approaches for implementation of the physical device, while laying out the framework to further explore parameter variations and disease classification

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