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BioSensorLab

By Pradeep Ramachandran Nair1, Muhammad A. Alam1

1. Purdue University

BioSensorLab is a tool to evaluate and predict the performance parameters of Biosensors.

Launch Tool

This tool version is unpublished and cannot be run. If you would like to have this version staged, you can put a request through HUB Support.

Archive Version 1.1
Published on 28 Mar 2008, unpublished on 31 Mar 2008
Latest version: 2.1.1. All versions

doi:10.4231/D3707WN45 cite this

This tool is closed source.

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Tools

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Abstract

BioSensorLab is a tool to evaluate and predict the performance parameters of a label-free, electronic biosensor (see figure). The sensor basically consist of a field effect device, whose surface is functionalized with capture probe (receptor) molecules. Some of the target molecules, which are introduced to the system, diffuse through the solution and reach the field effect device and get captured by the receptors thereby binding them close to the surface. Many bio-molecules carry an electrostatic charge under normal physiological conditions. For example, DNA is negatively charged while the net charge of a protein molecule depends on the pH of the solution. The coulomb interaction between the charge of the target bio-molecule and the field effect device can result in a change in conductivity of the latter.

The response of a sensor is characterized in terms of its Settling time, Sensitivity and Selectivity. The time taken by the sensor to produce a stable signal change defines the settling time. It is determined by bio-molecule concentration, their diffusion coefficients, and their conjugation affinity to the receptor molecules. Sensitivity corresponds to the relative change in sensor characteristics upon attachment of target molecules on nanowire surface. This is determined mainly by the electrostatics of the system. Finally, Selectivity denotes the ability of receptors to bind with the desired target in the presence of various other (possibly similar) biomolecules and is entirely determined by the functionalizing schemes. For example, to detect DNA, Peptide Nucleic Acid (PNA) receptors are shown to be more selective than their DNA counterparts.

The performance parameters of nanobiosensors (Settling time, Sensitivity and Selectivity) can be estimated using this tool. The theoretical model is based on self-consistent solutions of Diffusion-Capture model (for the time response), Poisson-Boltzmann and Drift-Diffusion Equations (for electrolyte screening and conductance modulation) and the statistical properties of bio-molecule adsorption (Selectivity). However, ONLY the SETTLING TIME module is released for public domain right now and the rest will be made available shortly.

Prof. Alam's lecture on Geometry of Diffusion and the Performance Limits of Nanobiosensors provides an overview on the Diffusion-Capture model and its implications on sensor performance.

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