A Cyclin Distributed Cell Cycle Model in GS-NS0
File(s)
Author(s)
Garcia Munzer, D
Kostoglou, M
Georgiadis, MC
Pistikopoulos, EN
Mantalaris, A
Type
Journal Article
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.
Date Issued
2015-02-27
Date Acceptance
2014-11-26
Citation
PLOS Computational Biology, 2015, 11 (2)
ISSN
1553-734X
Publisher
Public Library of Science
Journal / Book Title
PLOS Computational Biology
Volume
11
Issue
2
Copyright Statement
© 2015 García Münzer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
License URL
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Mathematical & Computational Biology
Biochemistry & Molecular Biology
POPULATION BALANCE MODELS
FED-BATCH CULTURE
GLOBAL SENSITIVITY-ANALYSIS
EXPRESSING CHO-CELLS
MONOCLONAL-ANTIBODY
NUMERICAL-SOLUTION
MAMMALIAN-CELLS
LACTATE CONSUMPTION
MATHEMATICAL-MODEL
FLOW-CYTOMETRY
Animals
Cell Cycle
Cell Line
Cell Line, Tumor
Cell Survival
Cyclins
DNA
Mice
Models, Biological
Recombinant Proteins
Bioinformatics
06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Publication Status
Published
Article Number
e1004062