Robust Reaction Rate Estimation with Application to Mammalian Cell Cultures

Macroscopic modeling of bioprocesses is a common approach to derive dynamic predictors suitable for process optimization and control. This study presents a data-driven methodology for inferring reaction rates without resorting to the classical numerical differentiation of experimental data, which is prone to errors in the presence of noise. The approach is based on the minimization of a nonlinear least square criterion, which parameterizes the rates in terms of the temporal values. The proposed method is appropriate for datasets with sparse measurements and few experimental replicates. A use case considering protein production by mammalian cells is used to validate the proposed approach.