A molecular machine biosensor: Construction, predictive models and experimental studies

This paper describes the construction, operation and predictive modeling of a molecular machine, functioning as a high sensitivity biosensor. Embedded gramicidin A (gA) ionchannels in a self-assembled tethered lipid bilayer act as biological switches in response to target molecules and provide a signal amplification mechanism that results in high sensitivity molecular detection. The biosensor can be used as a rapid and sensitive point of care diagnostic device in different media such as human serum, plasma and whole blood without the need for pre and post processing steps required in an enzyme-linked immunosorbent assay. The electrical reader of the device provides the added advantage of objective measurement. Novel ideas in the construction of the molecular machine, including fabrication of biochip arrays, and experimental studies of its ability to detect analyte molecules over a wide range of concentrations are presented. Remarkably, despite the complexity of the device, it is shown that the response can be predicted by modeling the analyte fluid flow and surface chemical reactions. The derived predictive models for the sensing dynamics also facilitate determining important variables in the design of a molecular machine such as the ion channel lifetime and diffusion dynamics within the bilayer lipid membrane as well as the bio-molecular interaction rate constants.

► Operation and construction of a high resolution gramicidin A ion channel biosensor are presented.
► The biosensor achieves high sensitivity detection by modulating the flow of ions in response to target molecules.
► Mathematical models that accurately predict the response of the biosensor over a wide range of concentrations are derived.
► The predictive models are used to explore possible improvements in biosensor response when flow rate and binding site density are changed.
► Bio-molecular interaction and diffusion rate constants are discovered using the developed mathematical models.