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Statistical modeling and application of gramicidin A ion channel Luk, Kai Yiu

Abstract

Ion channels are aqueous pores in the cell membrane for selected ions to flow down their electrochemical gradient. These channels play a prominent role in a variety of biological processes in the human body. Determining the structure and function of ion channels is of fundamental importance in biology. Also, the selective conductivity and specific gating mechanism of ion channels have attracted much interest in the area of artificial molecular detectors. Ion channel based biosensors are developed to detect molecular species of interest in medical diagnostics, environmental monitoring and general bio-hazard detection. This thesis is concerned with statistical techniques used to describe ion channel permeation and to develop ion channel based biosensors. Brownian dynamics is a popular technique to simulate ion channel permeation but is too computationally expensive to run when ionic concentration is high. By fitting binding site statistics of BD simulation to a semi-Markov chain, we obtain a simpler model with conduction properties that are statistically the same as the simulations. This approach enables the use of extrapolation techniques to predict channel conduction when performing the actual simulation is computationally infeasible. Numerical studies on the simulation of gramicidin A channels are presented. In a separate study, we show the use of statistical modeling and detection techniques as part of a sensitive biosensing platform. A nano-scale biosensor is built by incorporating dimeric gramicidin A channels into bilayer membranes of giant unilamellar liposomes. The presence of specific target molecules changes the statistics of the biosensor's conduction. By capturing the change in real time, we devise a maximum likelihood detector to detect the presence of target molecules. The performance of the biosensor is tested with the addition of various target molecules known to inhibit conduction of gramicidin A channels. Experimental results show that the detection performed well even when the conductance change was difficult to visualize. The detection algorithm provides a sensitive detection system for ongoing development of membrane-based biosensors.

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