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Membrane Bound Molecular Machines for Sensing | OMICS International | Abstract
ISSN: 2155-9872

Journal of Analytical & Bioanalytical Techniques
Open Access

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Research Article

Membrane Bound Molecular Machines for Sensing

William Hoiles*, Vikram Krishnamurthy and Bruce Cornell

PhD Student, The University of British Columbia, Canada

*Corresponding Author:
William Hoiles
PhD Student
The University of British Columbia
Vancouver, British Columbia, Canada
Tel: 604-822-5949
E-mail: [email protected]

Received Date: March 26, 2014; Accepted Date: April 28, 2014; Published Date: April 30, 2014

Citation:Hoiles W, Krishnamurthy V, Cornell B (2014) Membrane Bound Molecular Machines for Sensing. J Anal Bioanal Tech S7:014. doi: 10.4172/2155-9872.S7-014

Copyright: © 2014 Hoiles W, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This paper reports on the construction and predictive models of the Ion Channel Switch (ICS) biosensor which is capable of detecting femto-molar concentrations of target species including proteins, hormones, polypeptides, microorganisms, oligonucleotides, DNA segments, and polymers in cluttered electrolyte environments. The ICS employs an engineered tethered membrane with embedded gramicidin (gA) monomers and tethered antibody receptors. The detection of target molecules using the ICS is performed by measuring changes in the membrane conductance which is dependent on the number of gA dimers. As target molecules bind with the antibody receptors on the membrane surface, the conductance of the membrane decreases as a result of the decrease in the number of conducting gA dimers. As we show, the membrane conductance can be predicted using continuum theories for electrodiffusive flow coupled with boundary conditions for modelling chemical reactions and electrical double layers present at the bioelectronic interface of the ICS. Using the predictive model allows the concentration of analyte and surface reaction rates to be estimated from the current response of the ICS. To validate the predictive accuracy of the dynamic models, experimental measurement of streptavidin and ferritin analyte concentrations are performed using the ICS. Streptavidin is a useful example as the binding of streptavidin to biotin is one the strongest non-covalent bonds known in nature, and detection of the change in the concentration of ferritin can be linked to pathogenic infections or the presence of cancer.

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