Abstract:
Surface Plasmon Resonance (SPR) assays are a standard approach for quantifying kinetic parameters in antibody-antigen binding reactions. Classical SPR approaches ignore the bivalent structure of antibodies, and use simplified ODE models to estimate effective reaction rates for such interactions. In this work we develop a new SPR protocol, coupling a model that explicitly accounts for the bivalent nature of such interactions and the limited spatial distance over which such interactions can occur, to a SPR assay that provides more features in the generated data. Our approach allows the estimation of bivalent binding kinetics and the spatial extent over which antibodies and antigens can interact, while also providing substantially more robust fits to experimental data compared to classical ODE models. I will present our new modeling and parameter estimation approach, and demonstrate how it is being used to study interactions between antibodies and spike protein. I will also explain how we make the overall parameter estimation problem computationally feasible via the construction of a surrogate approximation to the (computationally-expensive) particle model. The latter enables fitting of model parameters via standard optimization approaches.
Time-permitting, I will also give an introduction to our Catalyst.jl symbolic chemical reaction modeling library, which we have recently demonstrated outperforms a number of popular systems biology simulation packages in solving ODE and stochastic reaction models. A distinguishing feature of Catalyst is the ease with which it integrates with other Julia libraries to enable sensitivity analysis, parameter estimation studies, structural identifiability analysis, bifurcation analysis, solution of the chemical master equation, and a variety of higher-level functionality.
