Gramicidin Ion Channel-Based Biosensors: Construction, Stochastic Dynamical Models, and Statistical Detection Algorithms

Abstract

This paper deals with the experimental construction, stochastic modeling, and statistical signal processing of a novel, artificially constructed biosensor comprised of biological ion channels. Such nanoscale biosensors have been built by incorporating dimeric gramicidin A (bis-gA) ion channels into bilayer membranes of giant unilamellar liposomes, and then excising small patches of the membrane loaded with ion channels. We present a stochastic model for the response of the biosensor and present statistical model validation tests to verify the adequacy of the model. We show that in the presence of specific target molecules, the statistics of the gating mechanisms of the gA channels are altered. By capturing the change in real time, we devise a maximum-likelihood detector to detect the presence of target molecules. To test the sensitivity of this model, we conducted patch-clamp experiments with two compounds known to inhibit conduction of the gA channels. We found experimentally that the real-time detection algorithm was able to accurately identify the addition of the compounds even when the alterations in the patch-clamp recordings were very small. This algorithm provides the sensitive detection system for ongoing development of lipid-based nanosensors.