Antoniewicz Maciek R, Kelleher Joanne K, Stephanopoulos Gregory
Department of Chemical Engineering, Bioinformatics and Metabolic Engineering Laboratory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Metab Eng. 2007 Jan;9(1):68-86. doi: 10.1016/j.ymben.2006.09.001. Epub 2006 Sep 17.
Metabolic flux analysis (MFA) has emerged as a tool of great significance for metabolic engineering and mammalian physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC/MS and nuclear magnetic resonance (NMR) measurements, is the large number of isotopomer or cumomer equations that need to be solved, especially when multiple isotopic tracers are used for the labeling of the system. This restriction reduces the ability of MFA to fully utilize the power of multiple isotopic tracers in elucidating the physiology of realistic situations comprising complex bioreaction networks. Here, we present a novel framework for the modeling of isotopic labeling systems that significantly reduces the number of system variables without any loss of information. The elementary metabolite unit (EMU) framework is based on a highly efficient decomposition method that identifies the minimum amount of information needed to simulate isotopic labeling within a reaction network using the knowledge of atomic transitions occurring in the network reactions. The functional units generated by the decomposition algorithm, called EMUs, form the new basis for generating system equations that describe the relationship between fluxes and stable isotope measurements. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer methods, however, require significantly less computation time. For a typical (13)C-labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s isotopomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, analysis of the gluconeogenesis pathway with (2)H, (13)C, and (18)O tracers requires only 354 EMUs, compared to more than two million isotopomers.
代谢通量分析（MFA）已成为代谢工程和哺乳动物生理学中具有重要意义的工具。通过稳定同位素标记以及气相色谱/质谱联用（GC/MS）和核磁共振（NMR）测量来进行MFA时，一个重要的局限性在于需要求解大量的同位素异构体或累积异构体方程，尤其是当使用多种同位素示踪剂对系统进行标记时。这种限制降低了MFA在阐明包含复杂生物反应网络的实际情况的生理学过程中充分利用多种同位素示踪剂作用的能力。在此，我们提出了一种用于同位素标记系统建模的新框架，该框架能显著减少系统变量的数量且不会丢失任何信息。基本代谢物单元（EMU）框架基于一种高效的分解方法，该方法利用网络反应中发生的原子跃迁知识，确定模拟反应网络内同位素标记所需的最少信息量。由分解算法生成的功能单元，即EMU，构成了生成描述通量与稳定同位素测量之间关系的系统方程的新基础。使用EMU框架模拟的同位素异构体丰度与使用同位素异构体和累积异构体方法获得的结果相同，然而，所需的计算时间却显著减少。对于典型的（13）C标记系统，需要求解的方程总数减少了一个数量级（100多个EMU对1000多个同位素异构体）。因此，EMU框架对于分析多种同位素示踪剂的标记最为有效。例如，用（2）H、（13）C和（18）O示踪剂分析糖异生途径仅需354个EMU，而相比之下，使用同位素异构体方法则需要超过两百万个。
