Fernandez-de-Cossio-Diaz Jorge, Leon Kalet, Mulet Roberto
Systems Biology Department, Center of Molecular Immunlogy, Havana, Cuba.
Group of Complex Systems and Statistical Physics. Department of Theoretical Physics, Physics Faculty, University of Havana, Cuba.
PLoS Comput Biol. 2017 Nov 13;13(11):e1005835. doi: 10.1371/journal.pcbi.1005835. eCollection 2017 Nov.
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.
在细胞培养的连续模式中，携带新鲜培养基的恒定流体会替代培养液、细胞、营养物质和分泌的代谢产物。在此，我们提出了一个连续细胞培养模型，该模型将细胞内代谢与描述生物反应器状态的细胞外变量相耦合，同时考虑了细胞的生长能力以及有毒副产物积累的影响。我们提供了一种确定该系统稳态的方法，该方法对于任意复杂程度的代谢网络都是易于处理的。我们首先在一个简化模型中展示我们的方法，然后在中国仓鼠卵巢细胞系的基因组规模代谢网络中进行展示，所获得的结果与实验观察结果在定性上一致。我们从该模型中推导出了许多与参数值无关的结论。细胞密度与稀释率之间的比率是固定具有所需代谢特性的稳态的理想控制参数。即使存在多重稳定性，这一结论仍然稳健，在我们的模型中，多重稳定性是由有毒副产物积累导致的负反馈回路所解释的。我们的模拟结果呈现出一个复杂的稳态格局，包括多个代谢开关，这也解释了为什么在分批培养中进行的细胞系和培养基基准测试不能外推到灌注培养。另一方面，我们预测了具有不同参数的连续细胞培养之间的不变性规律。一个实际的结果是，恒化器是大规模高密度灌注培养的理想实验模型，在该模型中可以如实地再现复杂的代谢转变格局。
