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

Abstract

The emergence of surface plasmon resonance-based optical biosensors has facilitated the identification of kinetic parameters for various macromolecular interactions. Normally, these parameters are determined from experiments with arbitrarily chosen periods of macromolecule and buffer injections, and varying macromolecule concentrations. Since the choice of these variables is arbitrary, such experiments may not provide the required confidence in identified kinetic parameters expressed in terms of standard errors. In this work, an iterative optimization approach is used to determine the above-mentioned variables so as to reduce the experimentation time, while treating the required standard errors as constraints. It is shown using multiple experimental and simulated data that the desired confidence can be reached with much shorter experiments than those generally performed by biosensor users.

Increasing throughput of surface plasmon resonance–based biosensors by multiple analyte injections

“…For the analysis of multiple analyte injections and optimized experiments (method by De Crescenzo et al . ()) and the multiple analyte optimized method described here), an in‐house software package was developed using the MATLAB 7.7.0.471 (R3008b) software platform (The Mathworks, Natick, USA) using the kinetic model described in Equation (2) and a Langmuirian model for the optimized single analyte experiments. The least‐square problem presented in Equation (3) was solved with the standard simplex program; the optimization problems were solved by sequential quadratic programming, both being available in the optimization Toolbox 4.1 of Matlab.…”

“…Confidence is a function of three important factors: the measurement noise, the number of measurements, and the experimental planning. The most common way of expressing the confidence ζ j in parameter j , in this domain, is the standard error (Ö'nell and Andersson, ; De Crescenzo et al ., ): where ρ is a proportionality constant based on the F ‐student distribution, the number of points used and the level of confidence that is requested, and σ is the standard deviation of the measurement noise a jj , the j th diagonal element of the inverse of the Hessian matrix H . The Hessian matrix is given by …”

“…As discussed in the Introduction section, it has been shown that time and material consumption can be reduced drastically when De Crenscenzo's () optimized method is applied. In this article, we made the hypothesis that the combination of multiple analyte injections and online optimization will take the improvement one step further and increase the throughput of SPR biosensors.…”

“…The online optimization algorithm that is presented here was inspired by De Crescenzo et al . (). These authors developed a method based on solving an optimization problem, where the objective function is the time of the experiment and the confidence on kinetic parameter acts as a constraint.…”

“…De Crescenzo et al . () also demonstrated that the information‐rich nature of real‐time data from SPR biosensors can be exploited, in combination with a careful, fully automated experimental design to obtain comparable accuracy and confidence on kinetic parameters as those derived from nonoptimized (i.e. time and material consuming) experimental data sets traditionally collected.…”