Department of Chemical Engineering, Metabolic Engineering and Systems Biology Laboratory, University of Delaware, 150 Academy St., Newark, DE 19716, USA.
Metab Eng. 2012 Mar;14(2):150-61. doi: 10.1016/j.ymben.2011.12.005. Epub 2011 Dec 22.
Metabolic flux analysis (MFA) is a powerful technique for elucidating in vivo fluxes in microbial and mammalian systems. A key step in (13)C-MFA is the selection of an appropriate isotopic tracer to observe fluxes in a proposed network model. Despite the importance of MFA in metabolic engineering and beyond, current approaches for tracer experiment design are still largely based on trial-and-error. The lack of a rational methodology for selecting isotopic tracers prevents MFA from achieving its full potential. Here, we introduce a new technique for tracer experiment design based on the concept of elementary metabolite unit (EMU) basis vectors. We demonstrate that any metabolite in a network model can be expressed as a linear combination of so-called EMU basis vectors, where the corresponding coefficients indicate the fractional contribution of the EMU basis vector to the product metabolite. The strength of this approach is the decoupling of substrate labeling, i.e. the EMU basis vectors, from the dependence on free fluxes, i.e. the coefficients. In this work, we demonstrate that flux observability inherently depends on the number of independent EMU basis vectors and the sensitivities of coefficients with respect to free fluxes. Specifically, the number of independent EMU basis vectors places hard limits on how many free fluxes can be determined in a model. This constraint is used as a guide for selecting feasible substrate labeling. In three example models, we demonstrate that by maximizing the number of independent EMU basis vectors the observability of a system is improved. Inspection of sensitivities of coefficients with respect to free fluxes provides additional constraints for proper selection of tracers. The present contribution provides a fresh perspective on an important topic in metabolic engineering, and gives practical guidelines and design principles for a priori selection of isotopic tracers for (13)C-MFA studies.
代谢通量分析（MFA）是阐明微生物和哺乳动物系统中体内通量的强大技术。13C-MFA 的关键步骤是选择合适的示踪剂来观察所提出的网络模型中的通量。尽管 MFA 在代谢工程及其他领域非常重要，但目前的示踪剂实验设计方法仍然在很大程度上基于反复试验。缺乏选择同位素示踪剂的合理方法阻止了 MFA 充分发挥其潜力。在这里，我们引入了一种基于基本代谢物单元（EMU）基向量概念的示踪剂实验设计新技术。我们证明，网络模型中的任何代谢物都可以表示为所谓的 EMU 基向量的线性组合，其中相应的系数表示 EMU 基向量对产物代谢物的分数贡献。这种方法的优点是将基质标记（即 EMU 基向量）与对自由通量（即系数）的依赖性解耦。在这项工作中，我们证明通量可观测性本质上取决于独立 EMU 基向量的数量以及系数对自由通量的敏感性。具体而言，独立 EMU 基向量的数量对模型中可以确定的自由通量数量施加了硬性限制。该约束用作选择可行基质标记的指南。在三个示例模型中，我们证明通过最大化独立 EMU 基向量的数量可以提高系统的可观测性。检查系数对自由通量的敏感性为示踪剂的正确选择提供了附加约束。本研究为代谢工程中的一个重要主题提供了新的视角，并为 13C-MFA 研究中同位素示踪剂的预先选择提供了实用的指南和设计原则。
