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Concentration gradient immunoassay. 2. Computational modeling for analysis and optimization.

Jennifer O Foley1, Kjell E Nelson, Afshin Mashadi-Hossein

  • 1Department of Bioengineering, and Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, USA.

Analytical Chemistry
|April 18, 2007
PubMed
Summary
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A novel concentration gradient immunoassay uses surface plasmon resonance imaging to detect small molecules. A computational model confirms that antibody binding location quantifies analyte concentration, aiding assay optimization.

Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Chemical Engineering

Background:

  • Microfluidic devices offer precise control over chemical and biological processes.
  • Surface plasmon resonance (SPR) imaging is a label-free technique for detecting molecular interactions.
  • Immunoassays are crucial for detecting and quantifying analytes in biological samples.

Purpose of the Study:

  • To develop and validate a three-dimensional finite element model for a microfluidic surface-based competition immunoassay.
  • To understand the dynamic binding processes within the concentration gradient immunoassay.
  • To correlate model predictions with experimental results for small molecule detection.

Main Methods:

  • Development of a complex three-dimensional finite element model.

Related Experiment Videos

  • Utilizing surface plasmon resonance (SPR) imaging for sensitive detection.
  • Introducing antibody and analyte into a microfluidic T-sensor.

    • Main Results:

    • The model showed strong qualitative agreement with experimental data for small molecule detection.
    • Confirmed that the antibody binding position within the microchannel quantifies analyte concentration.
    • Identified key parameters influencing assay sensitivity, including flow rate, microchannel dimensions, and antibody concentration.

    Conclusions:

    • The validated computational model provides insights into the concentration gradient immunoassay's dynamics.
    • The model can be used to optimize assay parameters for improved speed and accuracy.
    • This approach facilitates efficient exploration of factors affecting immunoassay sensitivity.