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Related Experiment Videos

Continuous measurements of a binding reaction using a capacitive biosensor.

Martin Hedström1, Igor Yu Galaev, Bo Mattiasson

  • 1Department of Biotechnology, Center for Chemistry and Chemical Engineering, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden.

Biosensors & Bioelectronics
|June 22, 2005
PubMed
Summary

This study presents a novel capacitive biosensor for continuous human serum albumin (HSA) measurement. A refined mathematical model allows accurate retrieval of HSA concentration profiles from normalized integral binding curves.

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A Sensitive Capacitive Biosensor for Protein a Detection Using Human IgG Immobilized on an Electrode Using Layer-by-Layer Applied Gold Nanoparticles.

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Area of Science:

  • Biosensing
  • Analytical Chemistry
  • Biophysics

Background:

  • Capacitive biosensors offer label-free detection.
  • Continuous monitoring of biomarkers like human serum albumin (HSA) is crucial for diagnostics.
  • Immobilized antibodies on transducers are key for specific biomolecule capture.

Purpose of the Study:

  • To develop a capacitive biosensor for continuous, real-time measurement of human serum albumin (HSA).
  • To refine a mathematical model for describing and deconvoluting HSA binding curves.
  • To demonstrate the retrieval of analyte concentration profiles from normalized integral binding (NIB) curves.

Main Methods:

  • Development of a capacitive biosensor using polyclonal antibodies against HSA immobilized on a gold transducer.

Related Experiment Videos

  • Refinement of a mathematical model incorporating irreversible binding, signal scaling, and binding site dynamics.
  • Application of the model to deconvolve normalized integral binding (NIB) curves.

    • Main Results:

    • The biosensor successfully measured human serum albumin (HSA) in the muM-range.
    • The refined mathematical model accurately described HSA-binding curves.
    • Deconvolution of NIB curves enabled retrieval of various HSA concentration profiles (isocratic, linear, exponential gradients).

    Conclusions:

    • The developed biosensor and mathematical model provide a foundation for on-line detection of analyte concentration profiles.
    • This approach allows for continuous monitoring with potentially infrequent regeneration steps.
    • The system shows promise for developing advanced analytical systems for real-time biomarker analysis.