LAUSR

Abstract This study aims at providing a methodology based on both data- and knowledge-driven approaches to build dynamic macroscopic models of cell cultures. This methodology proceeds in three steps. A principal component analysis is first applied in order to determine the minimum number of macroreactions necessary to faithfully describe the available data. These reactions are then selected among the elementary flux modes associated with a chosen metabolic network through the definition of an original linear programming problem. Kinetic laws are finally identified so as to reproduce the measurement data. The proposed methodology is illustrated using four different perfusion cultures of hybridoma cells and demonstrates a good capacity to select macroreactions capable of reproducing well the complex experimental data, even with the use of simple kinetic laws and without re-identifying the stoichiometry.