Khodayari Ali, Zomorrodi Ali R, Liao James C, Maranas Costas D
Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA.
Department of Chemical and Biomolecular Engineering, University of California at Los Angeles, USA.
Metab Eng. 2014 Sep;25:50-62. doi: 10.1016/j.ymben.2014.05.014. Epub 2014 Jun 10.
In contrast to stoichiometric-based models, the development of large-scale kinetic models of metabolism has been hindered by the challenge of identifying kinetic parameter values and kinetic rate laws applicable to a wide range of environmental and/or genetic perturbations. The recently introduced ensemble modeling (EM) procedure provides a promising remedy to address these challenges by decomposing metabolic reactions into elementary reaction steps and incorporating all phenotypic observations, upon perturbation, in its model parameterization scheme. Here, we present a kinetic model of Escherichia coli core metabolism that satisfies the fluxomic data for wild-type and seven mutant strains by making use of the EM concepts. This model encompasses 138 reactions, 93 metabolites and 60 substrate-level regulatory interactions accounting for glycolysis/gluconeogenesis, pentose phosphate pathway, TCA cycle, major pyruvate metabolism, anaplerotic reactions and a number of reactions in other parts of the metabolism. Parameterization is performed using a formal optimization approach that minimizes the discrepancies between model predictions and flux measurements. The predicted fluxes by the model are within the uncertainty range of experimental flux data for 78% of the reactions (with measured fluxes) for both the wild-type and seven mutant strains. The remaining flux predictions are mostly within three standard deviations of reported ranges. Converting the EM-based parameters into a Michaelis-Menten equivalent formalism revealed that 35% of Km and 77% of kcat parameters are within uncertainty range of the literature-reported values. The predicted metabolite concentrations by the model are also within uncertainty ranges of metabolomic data for 68% of the metabolites. A leave-one-out cross-validation test to evaluate the flux prediction performance of the model showed that metabolic fluxes for the mutants located in the proximity of mutations used for training the model can be predicted more accurately. The constructed model and the parameterization procedure presented in this study pave the way for the construction of larger-scale kinetic models with more narrowly distributed parameter values as new metabolomic/fluxomic data sets are becoming available for E. coli and other organisms.
与基于化学计量学的模型不同，代谢大规模动力学模型的发展受到了识别适用于广泛环境和/或基因扰动的动力学参数值及动力学速率定律这一挑战的阻碍。最近引入的集成建模（EM）程序提供了一种有前景的解决方法，通过将代谢反应分解为基本反应步骤，并在其模型参数化方案中纳入所有扰动后的表型观察结果来应对这些挑战。在此，我们利用EM概念提出了一个大肠杆菌核心代谢的动力学模型，该模型满足野生型和七个突变株的通量组学数据。该模型包含138个反应、93种代谢物和60种底物水平调节相互作用，涵盖糖酵解/糖异生、磷酸戊糖途径、三羧酸循环、主要丙酮酸代谢、回补反应以及代谢其他部分的一些反应。参数化使用一种形式优化方法进行，该方法使模型预测与通量测量之间的差异最小化。对于野生型和七个突变株，该模型预测的通量在78%的反应（有测量通量）的实验通量数据的不确定范围内。其余通量预测大多在报告范围的三个标准差内。将基于EM的参数转换为米氏等效形式表明，35%的Km参数和77%的kcat参数在文献报道值的不确定范围内。该模型预测的代谢物浓度也在68%的代谢物的代谢组学数据的不确定范围内。一项留一法交叉验证测试评估了该模型的通量预测性能，结果表明位于用于训练模型的突变附近的突变株的代谢通量可以更准确地预测。随着新的代谢组学/通量组学数据集可用于大肠杆菌和其他生物体，本研究中构建的模型和参数化程序为构建具有更窄分布参数值的更大规模动力学模型铺平了道路。
