Data-driven inference of bioprocess models: A low-rank matrix approximation approach.

Following the recent advent of Process Analytical Technologies, dataset production has undergone significant leverage. In this new abundance of data, isolating meaningful, informative content is critical for process dynamic modeling. This paper proposes a data-driven algorithm based on low-rank matrix approximation, the so-called successive projection algorithm, to retrieve a minimal set of macroscopic reactions, the corresponding stoichiometry, and a consistent kinetic model structure from the measurements of the trajectories of the species concentrations during cultures in a bioreactor. The proposed method is successfully validated in simulation, considering a case study related to monoclonal antibody (MAb) production with hybridoma cell cultures. • A data-driven tool for macroscopic modeling of bioprocesses is proposed. • The approach is based on a successive projection algorithm. • The algorithm retrieves the number of macroscopic reactions and computes stoichiometry. • The data-driven method infers a consistent kinetic structure for bioprocess modeling. [ABSTRACT FROM AUTHOR]