Online optimization of surface plasmon resonance-based biosensor experiments for improved throughput and confidence.

Area of Science:

• Biophysics
• Analytical Chemistry
• Biochemistry

Background:

• Surface plasmon resonance (SPR) optical biosensors are widely used for characterizing macromolecular interactions.
• Kinetic parameters are typically determined using experiments with arbitrarily set injection times and concentrations.
• This arbitrary experimental design can lead to insufficient confidence in the identified kinetic parameters, often reflected in high standard errors.

Purpose of the Study:

• To develop an iterative optimization approach for SPR biosensor experiments.
• To reduce overall experimentation time while maintaining a desired level of confidence in kinetic parameter estimation.
• To treat standard errors of kinetic parameters as constraints within the optimization process.

Main Methods:

• An iterative optimization algorithm was employed to determine optimal experimental variables.
• Variables optimized include injection periods for macromolecules and buffers, and macromolecule concentrations.
• The optimization process incorporates desired standard errors as constraints.

Main Results:

• The iterative optimization approach was validated using both experimental and simulated data.
• Shorter experimental durations were achieved compared to conventional methods.
• The method successfully reached the desired confidence levels for identified kinetic parameters.

Conclusions:

• The proposed iterative optimization strategy offers a more efficient method for kinetic analysis using SPR biosensors.
• This approach allows researchers to obtain reliable kinetic data with significantly reduced experimental effort.
• The findings suggest a paradigm shift towards optimized experimental design in biosensing applications.