Rehberg Markus, Ritter Joachim B, Reichl Udo
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany; Otto von Guericke University Magdeburg, Chair of Bioprocess Engineering, Magdeburg, Germany.
PLoS Comput Biol. 2014 Oct 16;10(10):e1003885. doi: 10.1371/journal.pcbi.1003885. eCollection 2014 Oct.
Due to its vital importance in the supply of cellular pathways with energy and precursors, glycolysis has been studied for several decades regarding its capacity and regulation. For a systems-level understanding of the Madin-Darby canine kidney (MDCK) cell metabolism, we couple a segregated cell growth model published earlier with a structured model of glycolysis, which is based on relatively simple kinetics for enzymatic reactions of glycolysis, to explain the pathway dynamics under various cultivation conditions. The structured model takes into account in vitro enzyme activities, and links glycolysis with pentose phosphate pathway and glycogenesis. Using a single parameterization, metabolite pool dynamics during cell cultivation, glucose limitation and glucose pulse experiments can be consistently reproduced by considering the cultivation history of the cells. Growth phase-dependent glucose uptake together with cell-specific volume changes generate high intracellular metabolite pools and flux rates to satisfy the cellular demand during growth. Under glucose limitation, the coordinated control of glycolytic enzymes re-adjusts the glycolytic flux to prevent the depletion of glycolytic intermediates. Finally, the model's predictive power supports the design of more efficient bioprocesses.
由于糖酵解在为细胞途径提供能量和前体方面至关重要,几十年来人们一直在研究其能力和调节机制。为了从系统层面理解犬肾上皮细胞(MDCK)的代谢,我们将之前发表的一个分离细胞生长模型与一个糖酵解结构化模型相结合,该糖酵解结构化模型基于糖酵解酶促反应相对简单的动力学,以解释各种培养条件下的途径动态。该结构化模型考虑了体外酶活性,并将糖酵解与磷酸戊糖途径和糖原生成联系起来。通过单一参数化,考虑细胞的培养历史,可以一致地再现细胞培养、葡萄糖限制和葡萄糖脉冲实验期间的代谢物池动态。生长阶段依赖性葡萄糖摄取以及细胞特异性体积变化会产生高细胞内代谢物池和通量率,以满足生长期间的细胞需求。在葡萄糖限制下,糖酵解酶的协同控制会重新调整糖酵解通量,以防止糖酵解中间体的消耗。最后,该模型的预测能力有助于设计更高效的生物过程。