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Characterizing steady states of genome-scale metabolic networks in continuous cell cultures.
- Source :
-
PLoS Computational Biology . 11/13/2017, Vol. 13 Issue 11, p1-25. 25p. - Publication Year :
- 2017
-
Abstract
- In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. [ABSTRACT FROM AUTHOR]
- Subjects :
- *CELL culture
*METABOLITES
*CELL metabolism
*BIOREACTORS
*CELL lines
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 13
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 126199313
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1005835