Analysis of binding kinetics and mass transport in SPR-based biosensor using the Generalized Integral Transform Technique and the Markov Chain Monte Carlo Method

The present work addresses biomolecular interactions in Surface Plasmon Resonance (SPR)-based biosensors, explicitly focusing on mass transport and binding kinetics. The Generalized Integral Transform Technique (GITT) is employed to solve the nonlinear system of partial differential equations describing mass transport, while the Markov Chain Monte Carlo (MCMC) method is adopted for accurately estimating the kinetic constants of the model. The outcomes were corroborated with simulated measurements and validated against experimental data related to the binding of the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2 bound to the cell receptor angiotensin-converting enzyme 2 (ACE2) in the Biacore system. Our findings demonstrate the efficacy of the GITT in describing the dynamics of average concentrations of the free analyte and of the bound analyte-receptor complex, aligning with results obtained in prior studies. Furthermore, our results demonstrate that the MCMC method is a robust tool for estimating model kinetic constants, with estimates closely approximating the exact values and falling within a 99 % confidence interval. The estimated average concentrations concurred with simulated measurements, even when accounting for Gaussian noise. The experimental validation results strengthen our conclusions, aligning the model parameter estimates with reference values from the literature. Therefore, this study suggests that the adopted mathematical model and numerical methodology hold significant potential for analyzing and comprehending biomolecule binding data, representing a valuable tool for studying complex biomolecular interactions.