(5bo) Elementary Metabolite Units (Emu): Novel Tools for Modeling Isotopic Tracer Distributions and Determining Metabolic Fluxes

Metabolic Flux Analysis has emerged as a tool of great significance for metabolic engineering and quantitative cell physiology. An important limitation of MFA, as carried out via stable isotope labeling and GC-MS measurements, is the large number of isotopomer/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 tracer systems that significantly reduces the number of system variables without any loss of information. The 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 (EMUs), form the new basis for generating system equations that describe the relationship between fluxes and isotopomer abundances. The decomposition algorithm is completely unsupervised and converges within seconds even for very large network models. Isotopomer abundances simulated using the EMU framework are identical to those obtained using the isotopomer and cumomer frameworks, however, requiring significantly less computation time. For a typical carbon labeling system the total number of equations that needs to be solved is reduced by one order-of-magnitude (100s EMUs vs. 1000s cumomers). As such, the EMU framework is most efficient for the analysis of labeling by multiple isotopic tracers. For example, the analysis of gluconeogenesis network model with 2H and 13C tracers requires only 300 EMUs compared to >3e4 cumomers. The developed computational and experimental methodologies for flux quantification were applied to two biological systems of major industrial and medical significance.

First, central carbon metabolism of E. coli that overproduces 1,3-propanediol (PDO) was investigated. 13C-labeling experiment was performed and nonstationary intracellular fluxes were determined by fitting labeling patterns of cellular amino acids to a detailed network model of E. coli. Our flux results confirmed the genotype of the organism and provided further insight into the physiology of PDO overproduction. It was demonstrated that during the fed-batch fermentation, under industrial relevant conditions, intracellular fluxes were relatively constant in time.

Second, the pathway of hepatic gluconeogenesis was investigated. We applied multiple 13C- and 2H-labeled tracers and analyzed the resulting mass isotopomer distributions of glucose. We estimated net and exchange fluxes of gluconeogenesis, and developed a novel multiple-tracer method for accurate analysis of this pathway independent of the isotopic steady-state assumption that can be used for in vivo study of Type II diabetes and other metabolic diseases.