Gramicidin ion channel-based biosensors: Construction, stochastic dynamical models, and statistical detection algorithms

Krishnamurthy, Vikram, Luk, Kai Yiu, Cornell, Bruce, Prashar, Jog, di Maio, Isabelle L., Islam, Hedayetul, Battle, Andrew R., Valenzuela, Stella M. and Martin, Donald K. (2007) Gramicidin ion channel-based biosensors: Construction, stochastic dynamical models, and statistical detection algorithms. IEEE Sensors Journal, 7 9: 1281-1288. doi:10.1109/JSEN.2007.901254


Author Krishnamurthy, Vikram
Luk, Kai Yiu
Cornell, Bruce
Prashar, Jog
di Maio, Isabelle L.
Islam, Hedayetul
Battle, Andrew R.
Valenzuela, Stella M.
Martin, Donald K.
Title Gramicidin ion channel-based biosensors: Construction, stochastic dynamical models, and statistical detection algorithms
Journal name IEEE Sensors Journal   Check publisher's open access policy
ISSN 1530-437X
1558-1748
Publication date 2007-09-01
Sub-type Article (original research)
DOI 10.1109/JSEN.2007.901254
Volume 7
Issue 9
Start page 1281
End page 1288
Total pages 8
Place of publication New York, N.Y. U.S.A.
Publisher IEEE Sensors Council
Language eng
Subject 0906 Electrical and Electronic Engineering
0913 Mechanical Engineering
Formatted 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.
© 2007 IEEE
Keyword Biosensor
Estimation
Gramicidin A
Lipid membrane
Maximum-likelihood detection
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Biological Sciences Publications
 
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