Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, 5531 Boelter Hall, Los Angeles, CA 90095-1592, USA.
Metab Eng. 2011 Jan;13(1):60-75. doi: 10.1016/j.ymben.2010.11.001. Epub 2010 Nov 12.
Dynamic models of metabolism are instrumental for gaining insight and predicting possible outcomes of perturbations. Current approaches start from the selection of lumped enzyme kinetics and determine the parameters within a large parametric space. However, kinetic parameters are often unknown and obtaining these parameters requires detailed characterization of enzyme kinetics. In many cases, only steady-state fluxes are measured or estimated, but these data have not been utilized to construct dynamic models. Here, we extend the previously developed Ensemble Modeling methodology by allowing various kinetic rate expressions and employing a more efficient solution method for steady states. We show that anchoring the dynamic models to the same flux reduces the allowable parameter space significantly such that sampling of high dimensional kinetic parameters becomes meaningful. The methodology enables examination of the properties of the model's structure, including multiple steady states. Screening of models based on limited steady-state fluxes or metabolite profiles reduces the parameter space further and the remaining models become increasingly predictive. We use both succinate overproduction and central carbon metabolism in Escherichia coli as examples to demonstrate these results.
动态代谢模型对于深入了解和预测扰动的可能结果非常重要。目前的方法从选择集中的酶动力学开始，并在大型参数空间中确定参数。然而，动力学参数通常是未知的，并且获得这些参数需要对酶动力学进行详细的特征描述。在许多情况下，仅测量或估计稳态通量，但这些数据尚未用于构建动态模型。在这里，我们通过允许各种动力学速率表达式并采用更有效的稳态求解方法来扩展之前开发的集合建模方法。我们表明，将动态模型锚定到相同的通量会显著减小允许的参数空间，从而使得对高维动力学参数的采样变得有意义。该方法能够检查模型结构的特性，包括多个稳态。基于有限的稳态通量或代谢物谱对模型进行筛选进一步减小了参数空间，并且剩余的模型变得越来越具有预测性。我们使用琥珀酸过量产生和大肠杆菌中的中心碳代谢作为示例来演示这些结果。
