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On-line kinetic model discrimination for optimized surface plasmon resonance experiments.

Massinissa Si Mehand1, Gregory De Crescenzo, Bala Srinivasan

  • 1Department of Chemical Engineering, École Polytechnique de Montréal, PO Box 6079, Centre-ville Station, H3C 3A7, Montréal, Québec, Canada.

Journal of Molecular Recognition : JMR
|April 5, 2014
PubMed
Summary

This study introduces an improved algorithm for surface plasmon resonance (SPR) biosensors to optimize experimental efficiency. The new method enhances throughput by intelligently selecting models to avoid biased kinetic parameters and reduce experiment duration.

Keywords:
biosensorkineticsmass transfer limitationsmodel discriminationsurface plasmon resonance

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Biophysics

Background:

  • Surface plasmon resonance (SPR) biosensors are crucial for analyzing molecular interactions.
  • Current optimization algorithms may use simplified models, leading to inaccurate kinetic parameters for complex interactions.
  • Mass transfer limitations can affect fast kinetics, biasing results if not accounted for.

Purpose of the Study:

  • To enhance the throughput of SPR biosensors.
  • To develop an algorithm that optimizes experimental time and resource consumption.
  • To ensure reliable kinetic parameter determination regardless of interaction complexity.

Main Methods:

  • Development of an on-line model discrimination and optimization approach.
  • Integration of model selection based on interaction kinetics (e.g., Langmuirian vs. mass transfer-limited).
  • Iterative algorithm design to predict optimal experimental conditions.

Main Results:

  • The proposed approach allows for accurate kinetic analysis across different interaction types.
  • Demonstrated reduction in experimental time and material usage.
  • Avoidance of biased kinetic parameters by selecting appropriate interaction models.

Conclusions:

  • The presented on-line model discrimination and optimization strategy significantly improves SPR biosensor throughput.
  • This method ensures robust and efficient kinetic analysis, applicable to both simple and complex molecular interactions.
  • The algorithm enhances confidence in kinetic parameters while minimizing experimental costs.