Chou I-Chun, Voit Eberhard O
Integrative BioSystems Institute and The Wallace H, Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA 30332, USA.
BMC Syst Biol. 2012 Jul 9;6:84. doi: 10.1186/1752-0509-6-84.
Advances in modern high-throughput techniques of molecular biology have enabled top-down approaches for the estimation of parameter values in metabolic systems, based on time series data. Special among them is the recent method of dynamic flux estimation (DFE), which uses such data not only for parameter estimation but also for the identification of functional forms of the processes governing a metabolic system. DFE furthermore provides diagnostic tools for the evaluation of model validity and of the quality of a model fit beyond residual errors. Unfortunately, DFE works only when the data are more or less complete and the system contains as many independent fluxes as metabolites. These drawbacks may be ameliorated with other types of estimation and information. However, such supplementations incur their own limitations. In particular, assumptions must be made regarding the functional forms of some processes and detailed kinetic information must be available, in addition to the time series data.
The authors propose here a systematic approach that supplements DFE and overcomes some of its shortcomings. Like DFE, the approach is model-free and requires only minimal assumptions. If sufficient time series data are available, the approach allows the determination of a subset of fluxes that enables the subsequent applicability of DFE to the rest of the flux system. The authors demonstrate the procedure with three artificial pathway systems exhibiting distinct characteristics and with actual data of the trehalose pathway in Saccharomyces cerevisiae.
The results demonstrate that the proposed method successfully complements DFE under various situations and without a priori assumptions regarding the model representation. The proposed method also permits an examination of whether at all, to what degree, or within what range the available time series data can be validly represented in a particular functional format of a flux within a pathway system. Based on these results, further experiments may be designed to generate data points that genuinely add new information to the structure identification and parameter estimation tasks at hand.
现代高通量分子生物学技术的进步使得基于时间序列数据的自上而下方法可用于估计代谢系统中的参数值。其中特别值得一提的是最近的动态通量估计（DFE）方法，该方法不仅将此类数据用于参数估计，还用于识别控制代谢系统的过程的函数形式。DFE还提供了诊断工具，用于评估模型的有效性以及模型拟合质量（超出残差误差）。不幸的是，DFE仅在数据或多或少完整且系统中独立通量与代谢物数量相当时才有效。这些缺点可以通过其他类型的估计和信息来改善。然而，这种补充也有其自身的局限性。特别是，除了时间序列数据外，还必须对某些过程的函数形式做出假设，并且必须有详细的动力学信息。
作者在此提出一种系统方法，该方法对DFE进行补充并克服其一些缺点。与DFE一样，该方法无需模型，仅需极少的假设。如果有足够的时间序列数据，该方法可以确定通量的一个子集，从而使DFE能够随后应用于通量系统的其余部分。作者用三个具有不同特征的人工途径系统以及酿酒酵母中海藻糖途径的实际数据展示了该过程。
结果表明，所提出的方法在各种情况下都能成功补充DFE，且无需对模型表示进行先验假设。所提出的方法还允许检查在特定途径系统中通量的特定函数格式下，可用时间序列数据是否完全、在何种程度上或在何种范围内可以有效表示。基于这些结果，可以设计进一步的实验来生成真正为手头的结构识别和参数估计任务增添新信息的数据点。
