Process parameter shifting: Part I. Effect of DOT, pH, and temperature on the performance of Epo‐Fc expressing CHO cells cultivated in controlled batch bioreactors

Trummer, Evelyn; Fauland, Katharina; Seidinger, Silke; Schriebl, Kornelia; Lattenmayer, Christine; Kunert, Renate; Vorauer-Uhl, Karola; McCarley, Robert W.; Borth, Nicole; Katinger, Hermann; Müller, Dethardt

doi:10.1002/bit.21013

Cited by 189 publications

(172 citation statements)

References 49 publications

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“…For example, the Sixfors multireactor system (Infors AG, Bottmingen, Switzerland) was used by Trummer et al [8] to investigate the effect of variation in DOT, pH, and temperature (T) on cell growth, metabolism, and cell cycle distribution. The authors observed that the reduction of T and pH exert the most significant effects on process performance mainly by reducing cell growth and metabolism.…”

Section: Introductionmentioning

confidence: 99%

Environmental Control in Flow Bioreactors

et al. 2017

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Abstract:The realization of physiologically-relevant advanced in vitro models is not just related to the reproduction of a three-dimensional multicellular architecture, but also to the maintenance of a cell culture environment in which parameters, such as temperature, pH, and hydrostatic pressure are finely controlled. Tunable and reproducible culture conditions are crucial for the study of environment-sensitive cells, and can also be used for mimicking pathophysiological conditions related with alterations of temperature, pressure and pH. Here, we present the SUITE (Supervising Unit for In Vitro Testing) system, a platform able to monitor and adjust local environmental variables in dynamic cell culture experiments. The physical core of the control system is a mixing chamber, which can be connected to different bioreactors and acts as a media reservoir equipped with a pH meter and pressure sensors. The chamber is heated by external resistive elements and the temperature is controlled using a thermistor. A purpose-built electronic control unit gathers all data from the sensors and controls the pH and hydrostatic pressure by regulating air and CO 2 overpressure and flux. The system's modularity and the possibility of imposing different pressure conditions were used to implement a model of portal hypertension with both endothelial and hepatic cells. The results show that the SUITE platform is able to control and maintain cell culture parameters at fixed values that represent either physiological or pathological conditions. Thus, it represents a fundamental tool for the design of biomimetic in vitro models, with applications in disease modelling or toxicity testing.

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Section: Introductionmentioning

confidence: 99%

Environmental Control in Flow Bioreactors

et al. 2017

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“…To confirm that growth arrest was not related to oxygen depletion, dissolved oxygen was measured during the experiments. The dissolved oxygen in all the cultures ranged between 40-80% (Figure 7.3), which is sufficient for the cell growth and not in the limiting range that can lead to conditions of hypoxia (Ozturk and Palsson, 1990b;Shu and Liao, 2002;Trummer et al, 2006). Figure 7.4A shows the time evolution of mAb concentration in each different volume.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Modeling of Apoptosis and its Interaction with Cell Growth in Mammalian Cell Culture

Meshram¹,

Naderi²,

Wei³

et al. 2011

IFAC Proceedings Volumes

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“…In addition, the model can be coupled to a glycosylation model [80] in order to capture product quality concerns. Several studies have reported changes [81][82][83][84], even improvements, on the glycosylation patterns linked to the cell culture environment (e.g. temperature, pH).…”

Section: Discussionmentioning

confidence: 99%

A Cyclin Distributed Cell Cycle Model in GS-NS0

Munzer

Kostoglou

Georgiadis

et al. 2014

Computer Aided Chemical Engineering

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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 transitionhave 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 cyclespecific 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.PLOS Computational Biology |

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Process parameter shifting: Part I. Effect of DOT, pH, and temperature on the performance of Epo‐Fc expressing CHO cells cultivated in controlled batch bioreactors

Cited by 189 publications

References 49 publications

Environmental Control in Flow Bioreactors

Environmental Control in Flow Bioreactors

Dynamic Modeling of Apoptosis and its Interaction with Cell Growth in Mammalian Cell Culture

A Cyclin Distributed Cell Cycle Model in GS-NS0

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