Mammalian cell cultures are intrinsically heterogeneous at different scales (from the molecular to bioreactor level). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, productivity and product quality of mammalian cell cultures. Attempts to model the cell cycle heterogeneity are not new and have proved to be challenging both experimentally and computationally. Most current cell cycle models rely on biological variables (mass/volume/age) that are non-mechanistic and difficult to experimentally quantify to describe cell cycle transition and to capture the culture heterogeneity.
In this thesis, the development of integrated modelling and experimental approaches that facilitates the study of cell cycle subpopulations in cell cultures is presented. The recently proposed closing the loop framework (Kiparissides et al., 2011) is employed to facilitate the development of cell cycle models with biological relevance and applicable to real life problems (industrial and clinical). The work herein presents a novel experimental-modelling platform whereby experimental quantification of key cell cycle metrics (cell cycle timings, cell cycle fractions, and cyclin expression determined by flow cytometry) is used to develop a cyclin and DNA distributed model for the industrially relevant GS-NS0 cell line. The cell cycle model captured the population heterogeneity, which further enables in silico studies of the complex system. It is envisaged that this modelling approach will pave the way for model-based developments of industrial cell lines and clinical studies. A second cell cycle model was developed to assist the industrially relevant selection of temperature profiles in mammalian cell cultures. The combined experimental-mathematical approach avoided unnecessary experimentation and guided the model development for the temperature selection. The model was successfully validated by predicting different temperature profile scenarios. The presented contributions assist the development of meaningful mathematical models with predictive capabilities accounting for the cell cycle heterogeneity in bioprocesses.