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

Content Provider	IEEE Xplore Digital Library
Author	Monfared, S.M. Krishnamurthy, V. Cornell, B.
Copyright Year	2009
Description	Author affiliation: Surgical Diagnostics Ltd., Unit 2/12 Frederick St, St Leonards NSW 2065 Australia (Cornell, B.) \|\| Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, V6T 1Z4, Canada (Monfared, S.M.; Krishnamurthy, V.)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	2725947
Page Count	6
File Format	PDF
ISBN	9781424447619
DOI	10.1109/GENSIPS.2009.5174353
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Algorithm design and analysis Linear systems Biological system modeling Stochastic processes Signal processing algorithms Nonlinear dynamical systems Biosensors Nanobioscience Biomedical signal processing
Content Type	Text
Resource Type	Article

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