Elementary metabolite units (EMU): A novel framework for modeling isotopic distributions

Abstract

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 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 elementary metabolite units, 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 2 million isotopomers.

Quantifying Reductive Carboxylation Flux of Glutamine to Lipid in a Brown Adipocyte Cell Line

“…Metabolic fluxes and their confidence intervals were determined by simultaneous fitting of external fluxes and mass isotopomer abundances of palmitate, pyruvate, lactate, alanine, malate, aspartate, glutamate, glutamine, and citrate to a detailed metabolic network model of adipocytes using the software Metran. Metran estimates fluxes by minimizing the difference between the observed and predicted measurements using an iterative least-squares regression procedure and isotopomer balancing (20). The objective of this routine is to evaluate a set of feasible fluxes that best account for the observed isotopomers and extracellular flux measurements.…”

“…The model included reversible reactions between ␣-ketoglutarate and citrate; malate and fumarate; malate and oxaloacetate; and glutamine, glutamate, and ␣-ketoglutarate. The model was implemented with a software tool, Metran, based on the concepts described elsewhere (19,20).…”

Identification of optimal measurement sets for complete flux elucidation in metabolic flux analysis experiments

“…A Python script was also written to generate EMU networks from the input files utilizing EMU reduction (Antoniewicz et al, 2007b) and network decomposition with block decoupling (Young et al, 2007).…”

“…These analysis methods infer intracellular fluxes using external flux and isotopic measurements (Sauer, 2006). The flux inference relies on a mathematical model describing the propagation of labeled atoms, for which a variety of techniques have been developed including positional enrichment (Sonntag et al, 1993), isotopomers (Schmidt et al, 1997;Zupke and Stephanopoulos, 1994), cumomers , bondomers (van Winden et al, 2002), and elementary metabolite units (EMUs) (Antoniewicz et al, 2007b).…”

ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis

“…Adding carbon mappings manually to basic stoichiometric models is an extremely error prone task as even small models of central carbon metabolism contain hundreds, even thousands of carbon mappings. On the other hand, recent advancements in the computational analysis techniques [24,16,25] and in the techniques for conducting isotopomer measurements [26,27] would facilitate the use of more detailed models in 13 C metabolic flux analysis. These detailed models are not readily available, but have to be constructed before 13 C metabolic flux analysis can take place.…”