From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline
From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline
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DOI：
10.1371/journal.pcbi.1007319
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发表时间：
2019-09-01
影响因子：
4.3
通讯作者：
Maranas, Costas D.
中科院分区：
文献类型：
--
作者：
Foster, Charles J.;Gopalakrishnan, Saratram;Maranas, Costas D.
代谢网络的动力学模型提供了定量表型预测的希望。酶催化反应的机理表征允许跟踪响应于超出化学计量模型范围的遗传和环境扰动的代谢物浓度和反应通量的扰动的影响。在这项研究中，我们开发了一个两步计算管道，用于快速参数化代谢网络的动力学模型，使用策划的代谢模型和可用的C-13标记分布在多个遗传和环境扰动下。第一步是阐明E.用C-13-MFA分析了大肠杆菌中74个反应和61个代谢产物。在这里，通量对应于中期指数生长期阐明7个单基因缺失突变体从上糖酵解，戊糖磷酸途径和恩特纳-杜道夫途径。然后使用计算的通量范围来参数化相同的通量范围（即，k-ecoli 74）核心动力学模型。大肠杆菌与55个底物水平的规定，使用新开发的K-FIT参数化算法。K-FIT算法采用方程分解和迭代求解技术相结合，以评估响应于遗传扰动的稳态通量。k-ecoli 74预测了在拟合期间使用的菌株的86%的通量值，其在C-13-MFA估计值的单个标准偏差内。通过使用相同的网络执行这两项任务，避免了与两个网络之间缺乏一致性相关的错误，从而实现了数据与模型构建的无缝集成。产物产率预测和与先前开发的动力学模型的比较表明，通量范围的变化以及从训练数据向感兴趣的途径递送通量的突变菌株的存在或不存在显著影响预测能力。使用这个工作流程，通量组数据集的完整性的影响和特定的遗传扰动的动力学参数估计的不确定性的重要性进行评估。
Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available C-13-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using C-13-Metabolic Flux Analysis (C-13-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of C-13-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.