(436g) Mapping the Cell Cycle in GS-NS0: Developing a Cyclin Blueprint As a Tool for Optimizing Productivity

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.

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.

References

[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.