Dynamic modeling of hybridoma cell cultures using maximum likelihood principal component analysis

Abstract

RésuméHybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually available experimental data. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. A simple macroscopic reaction scheme is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to identify the kinetic parameters. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct validation tests. However, following a parametric sensitivity analysis, this model appears over-parameterized. A simplified model is therefore proposed, offering comparable direct validation results and very good cross-validation results. The procedure proves systematic and could be applied to other case studies.