Yu Shiuan Huang

Document Type

Honors Thesis


Nanopore biosensors have played an essential role in bio-analyte characterization, but traditional biosensors are limited by short analyte translocation times. Dr. Alexandrakis has invented the Self-induced Back Action Nanopore Electrophoresis (SANE) to slow analytes down by optically trapping them as they travel through the nanopore. By collecting optical and electrical data, SANE enables comprehensive bio-analyte characterization. However, the manual analysis of bimodal data is work-intensive, highlighting the need for automated data analysis. The longer-term aims of this project are to develop a MATLAB algorithm that automatically identifies positive and negative signals being created when a molecule enters and escapes the sensor’s optical trap. Specifically, the code should identify (1) positive and negative polarity current spikes detected by the v sensor and (2) concurrently occurring positive and negative step changes in the optical signal. Additional code will calculate features of the optical and electrical signals, including each event's duration, beginning time, current change, and more. This thesis work is a first step towards the above-described longer-term goal. Specifically, this work focuses on the creation of an algorithm that detects single-step changes in detected optical signal when 20 nm silica beads are trapped by the sensor (positive signal change – upward step) and when they escape the sensor (negative signal change – downward step). Furthermore, once this algorithm has detected all step-changes, it saves their time of occurrence as a concatenated list for further analyses and builds histograms of the step-change amplitudes and their durations and fits Gaussian curves to help resolve sub-populations. The significance of this work is the creation of an open-source algorithm that researchers can use to speed up protein-ligand interaction analyses in the field of nanopore sensing.

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