Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

中文翻译：

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恶臭假单胞菌 KT2440 的大规模动力学代谢模型，用于代谢工程策略的一致设计


恶臭假单胞菌是生物燃料和生物化学品工业生产的有前途的候选者，因为它对有毒化合物具有高耐受性并且能够在多种基质上生长。改造这种生物体以提高性能并预测遗传扰动时的代谢反应需要以化学计量和动力学模型的形式对其代谢进行可靠的描述。在这项工作中，我们开发了恶臭假单胞菌的动力学模型来预测代谢表型并设计用于生化产品生产的代谢工程干预措施。开发的动力学模型包含 775 个反应和 245 个代谢物。此外，我们在这里引入了基于热力学的通量分析中的一组新颖的约束，这些约束允许考虑作为单独实体存在于多个隔室中的代谢物的浓度。我们首先对 iJN1411（恶臭假单胞菌 KT2440 的基因组规模模型）进行间隙填充和热力学管理。然后，我们系统地简化了精心设计的 iJN1411 模型，并创建了三个不同复杂度的核心化学计量模型，用于描述恶臭假单胞菌的中心碳代谢。使用中等复杂性核心模型作为支架，我们为两项研究生成了大规模动力学模型群体。在第一项研究中，开发的动力学模型成功地捕获了通过实验观察到的对以葡萄糖生长的恶臭假单胞菌 KT2440 野生型菌株的几个单基因敲除的代谢反应。在第二项研究中，我们使用开发的模型提出代谢工程干预措施，以提高该生物体对 ATP 需求增加的应激条件的鲁棒性。 该研究证明了动力学模型的潜力和预测能力，可以合理设计和优化重组恶臭假单胞菌菌株，以提高生物燃料和生物化学品的生产。精心策划的恶臭假单胞菌基因组规模模型以及开发的大规模化学计量和动力学模型为工业界和学术界的研究人员提供了重要资源。