Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, California, United States of America.
PLoS One. 2009 Sep 4;4(9):e6903. doi: 10.1371/journal.pone.0006903.
Ensemble Modeling (EM) is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.
集合建模（EM）是一种最近开发的代谢建模方法，特别是用于利用酶调谐数据对特定化合物的产生的影响来改进模型。在这里，我们使用这种方法来研究大肠杆菌中芳香产物的产生。与使用动态代谢物数据拟合模型不同，EM 方法使用表型数据（酶过表达或敲除对稳态产生速率的影响）来筛选可能的模型。这些数据通常在菌株设计过程中生成。构建了一个集合模型，这些模型都达到相同的稳态，并基于基本反应水平上相同的机械框架。模型的行为跨越了热力学允许的动力学范围。然后，通过使用文献中关于过表达编码转酮醇酶（Tkt）、转醛醇酶（Tal）和磷酸烯醇丙酮酸合酶（Pps）的基因的现有数据来筛选集合，我们得到了一组能够正确描述已知酶过表达表型的模型。随着更多数据的使用来改进模型，这个模型子集变得更具预测性。最终的模型集合展示了细胞的特征，即 Tkt 是第一个限速步骤，并正确预测只有在 Tkt 过表达后，Pps 的增加才会增加芳香族产物的产率。这项工作表明，EM 能够通过成功利用常规生成的酶调谐数据来指导模型学习，从而捕获酶过表达对芳香族产生菌的影响。
