An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis

Abstract

Nonstationary metabolic flux analysis (NMFA) is at present a very computationally intensive exercise, especially for large reaction networks. We applied elementary metabolite unit (EMU) theory to NMFA, dramatically reducing computational difficulty. We also introduced block decoupling, a new method that systematically and comprehensively divides EMU systems of equations into smaller subproblems to further reduce computational difficulty. These improvements led to a 5000-fold reduction in simulation times, enabling an entirely new and more complicated set of problems to be analyzed with NMFA. We simulated a series of nonstationary and stationary GC/MS measurements for a large E. coli network that was then used to estimate parameters and their associated confidence intervals. We found that fluxes could be successfully estimated using only nonstationary labeling data and external flux measurements. Addition of near-stationary and stationary time points increased the precision of most parameters. Contrary to prior reports, the precision of nonstationary estimates proved to be comparable to the precision of estimates based solely on stationary data. Finally, we applied EMU-based NMFA to experimental nonstationary measurements taken from brown adipocytes and successfully estimated fluxes and some metabolite concentrations. By using NFMA instead of traditional MFA, the experiment required only 6 h instead of 50 (the time necessary for most metabolite labeling to reach 99% of isotopic steady state).

Quantification of Metabolic Rearrangements During Neural Stem Cells Differentiation into Astrocytes by Metabolic Flux Analysis

“…Non-stationary 13 C-MFA was performed using the publicly available software package INCA [40], which implements the elementary metabolite units framework [41]. Briefly, fluxes are estimated by minimizing the variance-weighted sum of squared residuals (SSR) between experimental measurements and model predictions using least-squares regression.…”

Erk regulation of pyruvate dehydrogenase flux through PDK4 modulates cell proliferation

“…Labeling of metabolites was corrected for natural isotope abundances using an in-house algorithm adapted from Fernandez et al (1996). MFA and isotopomer spectral analysis (ISA) calculations were performed using Metran, an algorithm for flux and confidence interval determination (Young et al 2008). …”

Metabolic Fluxes in an Illuminated Arabidopsis Rosette

“…Very little information about fluxes can be extracted from data obtained at isotopic steady state following the feeding of 13 CO 2 . On the other hand, qualitative analysis of non-steady state labeling kinetics with 13 C is extremely challenging due to the large number of isotopomers (Antoniewicz et al, 2007;Young et al, 2008). Such analyses are further complicated by the large range of turnover times of metabolite pools, which span between <1 s for many metabolites in the CBC to ;1 min for intermediates in Suc synthesis to several minutes for intermediates of photorespiration (Stitt et al, 1983;Arrivault et al, 2009; for details, see Supplemental Methods 2 online).…”