Characterizing steady states of genome-scale metabolic networks in continuous cell cultures
Abstract
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. More importantly, we derive a number of consequences from the model that are independent of parameter values. First, that the ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties invariant across perfusion systems. This conclusion is robust even in the presence of multi-stability, which is explained in our model by the negative feedback loop on cell growth due to toxic byproduct accumulation. Moreover, a complex landscape of steady states in continuous cell culture 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. Thus, in order to actually reflect the expected behavior in perfusion, performance benchmarks of cell-lines and culture media should be carried out in a chemostat.
- Publication:
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PLoS Computational Biology
- Pub Date:
- November 2017
- DOI:
- 10.1371/journal.pcbi.1005835
- arXiv:
- arXiv:1705.09708
- Bibcode:
- 2017PLSCB..13E5835F
- Keywords:
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- Quantitative Biology - Molecular Networks;
- Quantitative Biology - Cell Behavior;
- Quantitative Biology - Populations and Evolution;
- Quantitative Biology - Subcellular Processes
- E-Print:
- PLOS Computational Biology. 13 (11): e1005835 (2017-11-13)