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

    Author(s)
    Krishnamurthy, Vikram
    Luk, Kai Yin
    Cornell, Bruce
    Prashar, Jog
    di Maio, Isabelle L
    Islam, Hedayetul
    Battle, Andrew R
    Valenzuela, Stella M
    Martin, Donald K
    Griffith University Author(s)
    Battle, Andrew
    Year published
    2007
    Metadata
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    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, ...
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    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.
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    Journal Title
    IEEE Sensors Journal
    Volume
    7
    Issue
    9
    DOI
    https://doi.org/10.1109/JSEN.2007.901254
    Subject
    Atomic, molecular and optical physics
    Other biological sciences not elsewhere classified
    Mechanical engineering
    Publication URI
    http://hdl.handle.net/10072/58089
    Collection
    • Journal articles

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