Stochastic modeling and signal processing of nano-scale protein-based biosensors

This paper considers the dynamic modeling and signal processing of a biosensor incorporating gramicidin A (gA) ion channels. The gA ion channel based biosensor provides improved sensitivity in rapid detection of biological analytes and is easily adaptable to detect a wide range of analytes. In this paper, the electrical dynamics of the biosensor are modeled by an equivalent second order linear system. The chemical dynamics of the biosensor response to analyte concentration are modeled by a two-time scale nonlinear system of differential equations. An optimal input excitation is designed for the biosensor to minimize the covariance of the channel conductance estimate. By using the theory of singular perturbation, we show that the channel conductance varies according to one of three possible modes depending on the concentration of the analyte present. A multi-hypothesis testing algorithm is developed to classify the analyte concentration in the system as null, medium or high. Finally experimental data collected from the biosensor in response to various analyte concentrations are used to verify the modeling of the biosensor as well as the performance of the multi-hypothesis testing algorithm.