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

Krishnamurthy, V; Luk, KY; Cornell, B; Prashar, J; Di Maio, IL; Islam, H; Battle, AR; Valenzuela, SM; Martin, DK

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

Krishnamurthy, V Luk, KY Cornell, B

Prashar, J Di Maio, IL Islam, H Battle, AR Valenzuela, SM

Martin, DK

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Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2007, 7 (9), pp. 1281 - 1288
Issue Date:: 2007-09-01

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Field	Value	Language
dc.contributor.author	Krishnamurthy, V	en_US
dc.contributor.author	Luk, KY	en_US
dc.contributor.author	Cornell, B https://orcid.org/0000-0003-4166-3241	en_US
dc.contributor.author	Prashar, J	en_US
dc.contributor.author	Di Maio, IL	en_US
dc.contributor.author	Islam, H	en_US
dc.contributor.author	Battle, AR	en_US
dc.contributor.author	Valenzuela, SM https://orcid.org/0000-0001-5934-6047	en_US
dc.contributor.author	Martin, DK https://orcid.org/0000-0001-5913-2372	en_US
dc.date.issued	2007-09-01	en_US
dc.identifier.citation	IEEE Sensors Journal, 2007, 7 (9), pp. 1281 - 1288	en_US
dc.identifier.issn	1530-437X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/5850
dc.description.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.	en_US
dc.relation.ispartof	IEEE Sensors Journal	en_US
dc.relation.isbasedon	10.1109/JSEN.2007.901254	en_US
dc.subject.classification	Analytical Chemistry	en_US
dc.title	Gramicidin ion channel-based biosensors: Construction, stochastic dynamical models, and statistical detection algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	7	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0205 Optical Physics	en_US
dc.location.activity	ISI:000249742700010	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Medical and Molecular Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

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.

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http://hdl.handle.net/10453/5850