279067 Mapping the Cell Cycle in GS-NS0: Developing a Cyclin Blueprint As a Tool for Optimizing Productivity

Wednesday, October 31, 2012: 10:18 AM
Westmoreland East (Westin )
D. G. Garcia Münzer, Biological Systems Engineering Laboratory - Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College, Imperial College London, London, United Kingdom, A. Mantalaris, Biological Systems Engineering Laboratory, Department of Chemical Engineering, Imperial College London, London, United Kingdom and E. N. Pistikopoulos, Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, United Kingdom


Mapping the Cell Cycle in GS-NS0: Developing a cyclin blueprint as a tool for optimizing productivity


D.G. García Münzer *, **, A. Mantalaris*, E.N.  Pistikopoulos**

*Biological Systems Engineering Laboratory, Department of Chemical Engineering, Imperial College, London SW7 2AZ, UK

**Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College, London SW7 2BY, UK

The use of mammalian cells for the production of high value bio-pharmaceuticals (biologics), such as monoclonal antibodies, has growth rapidly. It is expected to reach a market value of $239 billion by 2015 [1]. Mammalian cell factories are complex physical and chemical structures whose productivity (and product quality) is under the control of a large number of coordinated chemical reactions (metabolism) influenced by culture parameters. The cell cycle is at the centre of growth, productivity and cell death, which vary during the different phases of the cell cycle. Specifically, cell productivity is cell cycle, cell-line and promoter dependant [2]. Consequently, knowledge of the cell cycle-associated production profile will assist in determining the optimization strategy towards improving productivity [3].

Cyclins are key regulators of the cell cycle. They activate their partner cyclin-dependent kinases (CDKs) and target specific proteins to drive the cell through particular processes, check points and phases. Although a number of studies have dealt with cell cycle regulation in various human cell lines [4-6], to our knowledge, there is no information on cyclin phase-dependant expression profiles and thresholds of industrial relevant mammalian cells. Therefore, there exists a need to identify and quantify major landmarks of the cell cycle that will allow for the systematic study of cell productivity.

We have studied the timing of expression of the three cyclins, under both perturbed and unperturbed growth using the GS-NS0 cell line by flow cytometry. The perturbed systems involved arresting the cell using two different DNA synthesis inhibitors, thymidine and dimethyl sulfoxide (DMSO). This approach allows establishment of characteristic cyclin profiles, thresholds and unscheduled production. In particular, we looked at two G1 class cyclins, namely cyclin D and E, and one G2 cyclin, namely cyclin B. The observed patterns (including thresholds and unscheduled production) provide a blueprint of the cell line's cell cycle, which can be used for cell cycle modelling. Briefly, expression of cyclin B showed a clear cell cycle phase specific pattern whereas Cyclin D expression was fairly invariable throughout the cycle (our data indicate an unrelated entrance to the S phase with respect to cyclin D). Similarly, cyclin E was expressed during all phases, in progressively decreasing manner from G1 towards G2, suggesting that cyclin E is being degraded.


Cyclin B





Cyclin D





Cyclin E



A key feature of cell cycle modelling in cell culture systems is the ability to account for important differences between the populations. There is a need for developing cell cycle models that can capture accurately the complexity of the system while being computationally tractable. Cyclins represent excellent cell cycle modelling variables as they not only provide information regarding the proliferating potential of the cell population, but also serve as landmarks throughout the cycle. Therefore, cyclin distributed models represent the next step in cell cycle modelling. The use of cyclins as distributed variables can be experimentally validated (quantitatively) and will avoid the use of weakly supported variables such as age, volume or mass. The development of a biological relevant cell cycle model, while keeping it tractable, is possible via the model building framework [7]. Ultimately, the development of these models will pave the way for the systematic study of the cell culture system, the improvement of productivity and product quality.


[1] Bbc Research. http://www.bccresearch.com/report/biologic-therapeutic-drugs-bio079a.html

[2] Alrubeai, M. and A. N. Emery (1990). "Mechanisms and Kinetics of Monoclonal-Antibody Synthesis and Secretion in Synchronous and Asynchronous Hybridoma Cell-Cultures." Journal of Biotechnology 16(1-2): 67-86.

[3] Dutton, R. L., J. M. Scharer, et al. (1998). "Descriptive parameter evaluation in mammalian cell culture." Cytotechnology 26(2): 139-152.

[4] Darzynkiewicz, Z., J. P. Gong, et al. (1996). "Cytometry of cyclin proteins." Cytometry 25(1): 1-13.

[5] Tomasoni, D., M. Lupi, et al. (2003). "Timing the changes of cyclin E cell content in G(1) in exponentially growing cells." Experimental Cell Research 288(1): 158-167.

[6] Frisa, P. S. and J. W. Jacobberger (2009). "Cell Cycle-Related Cyclin B1 Quantification." Plos One 4(9).

[7] Kiparissides, A., M. Koutinas, et al. (2011). "'Closing the loop' in biological systems modeling - From the in silico to the in vitro." Automatica 47(6): 1147-1155.

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