Cyclin and DNA Distributed Cell Cycle Model for GS-NS0 Cells

Abstract

Mammalian cell cultures are intrinsically heterogeneous at different scales (molecular to bioreactor). The cell cycle is at the centre of capturing heterogeneity since it plays a critical role in the growth, death, and productivity of mammalian cell cultures. Current cell cycle models use biological variables (mass/volume/age) that are non-mechanistic, and difficult to experimentally determine, to describe cell cycle transition and capture culture heterogeneity. To address this problem, cyclins—key molecules that regulate cell cycle transition—have been utilized. Herein, a novel integrated experimental-modelling platform is presented 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 cell line, GS-NS0. Cyclins/DNA synthesis rates were linked to stimulatory/inhibitory factors in the culture medium, which ultimately affect cell growth. Cell antibody productivity was characterized using cell cycle-specific production rates. The solution method delivered fast computational time that renders the model’s use suitable for model-based applications. Model structure was studied by global sensitivity analysis (GSA), which identified parameters with a significant effect on the model output, followed by re-estimation of its significant parameters from a control set of batch experiments. A good model fit to the experimental data, both at the cell cycle and viable cell density levels, was observed. The cell population heterogeneity of disturbed (after cell arrest) and undisturbed cell growth was captured proving the versatility of the modelling approach. Cell cycle models able to capture population heterogeneity facilitate in depth understanding of these complex systems and enable systematic formulation of culture strategies to improve growth and productivity. It is envisaged that this modelling approach will pave the model-based development of industrial cell lines and clinical studies.

A mathematical model of subpopulation kinetics for the deconvolution of leukaemia heterogeneity

“…DNA content (defined as DNA in the model) is used as in previous models [19] for the representation of progress in S phase. Each of the three phases is modelled by a PBM equation (equations (2.1) - (2.3); refer to electronic supplementary material, table S2, for variable definitions), and these equations are linked by the transfer of cells from phase to phase (figure 1) [22,23]:…”

A Predictive Mathematical Model of Cell Cycle, Metabolism, and Apoptosis of Monoclonal Antibody‐Producing GS–NS0 Cells

“…GS–NS0 cells producing an immunoglobulin‐4 (IgG‐4) mAb were obtained from Lonza and adapted to serum‐free culture conditions. Cell culture, flow cytometry analysis of viability, and measurements of glucose, lactate, AAs, adenosine triphosphate (ATP), and mAb concentrations have been conducted as previously described . Briefly, cells were cultured in CD Hybridoma medium (Life Technologies) supplemented with cholesterol (Life Technologies), GS‐supplement (Sigma‐Aldrich), serum replacement (Sigma‐Aldrich), 25 µ m methylsulfoximine (Sigma‐Aldrich), and sodium hydroxide (Sigma‐Aldrich) in 1 L vent shake flasks (Corning) under 5% CO 2 and orbitally shaken at 130 rpm.…”

“…GuavaSoft v3.1 (Merck) was used for data analysis. For the determination of the time spent in each cell cycle phase, the 5‐ethynyl‐2′‐deoxyuridine (EdU) method was used as previously described …”

“…Capturing the appropriate level of biological features is essential in developing high‐fidelity models. Specifically, culture heterogeneity, both at the cellular and bioreactor level, needs to be accounted for by the mathematical models . Another important characteristic of biological systems is that they are inherently dynamic, prompting for a choice of mathematical formulations able to describe changes over time rather than steady‐state or empirical input/output relationships, which are likely to compromise the predictive capabilities of the model .…”

Selecting a Differential Equation Cell Cycle Model for Simulating Leukemia Treatment

“…6,7 In contrast, descriptive models represent observable cause-effect phenomena ("one cell gives birth to another cell after 24h") as an explanation of the overall system behavior. [8][9][10][11] Some hybrid mechanistic/descriptive approaches have arisen, benefiting from the advantages of both (the in-depth insights in mechanistic models, and the fast computation of key variables in descriptive models). Due to the cell cycle specificity of most chemotherapeutic drugs, mathematical models are increasingly used to study and simulate cellular kinetics in response to cancer treatment.…”