SUMMARYMetabolic fluxes estimated from stable-isotope studies provide a key to understanding cell physiology and regulation of metabolism. A limitation of the classical method for metabolic flux analysis (MFA) is the requirement for isotopic steady state. To extend the scope of flux determination from stationary to nonstationary systems, we present a novel modeling strategy that combines key ideas from isotopomer spectral analysis (ISA) and stationary MFA. Isotopic transients of the precursor pool and the sampled products are described by two parameters, D and G parameters, respectively, which are incorporated into the flux model. The G value is the fraction of labeled product in the sample, and the D value is the fractional contribution of the feed for the production of labeled products. We illustrate the novel modeling strategy with a nonstationary system that closely resembles industrial production conditions, i.e. fed-batch fermentation of E. coli that produces 1,3-propanediol (PDO). Metabolic fluxes and the D and G parameters were estimated by fitting labeling distributions of biomass amino acids measured by GC/MS to a model of E. coli metabolism. We obtained highly consistent fits from the data with 82 redundant measurements. Metabolic fluxes were estimated for 20 time points during course of the fermentation. As such we established, for the first time, detailed time profiles of in vivo fluxes. We found that intracellular fluxes changed significantly during the fed-batch. The intracellular flux associated with PDO pathway increased by 10%. Concurrently, we observed a decrease in the split ratio between glycolysis and pentose phosphate pathway from 70/30 to 50/50 as a function of time. The TCA cycle flux, on the other hand, remained constant throughout the fermentation. Furthermore, our flux results provided additional insight in support of the assumed genotype of the organism. KeywordsInstationary fluxes; 13C flux analysis; elementary metabolite units (EMU); gas chromatography mass spectrometry (GC/MS); statistical analysis

The emergence of antimicrobial resistance has led to an increase in research directed toward the engineering of novel polyketides. To date, less than 10 000 polyketide structures have been discovered experimentally; however, the theoretical analysis of polyketide biosynthesis performed suggests that over a billion possible structures can be synthesized. Polyketide synthesis, which involves the formation of a linear chain and its subsequent cyclization, is catalyzed by an enzyme complex called polyketide synthase (PKS). There are a number of variables in the linear chain synthesis controlled by the PKS: the number, identity, stereochemistry and sequence of the monomer units used in the elongation steps, and the degree of reduction that occurs after each of the condensation reactions. The theoretical analysis performed demonstrates that changes in these variables lead to the formation of different polyketide linear chains and, consequently, a high diversity of polyketide structures. The complexity in the number of possible structures led to the implementation of this system in BNICE, a computational framework that generates all possible biochemical pathways using a given set of enzyme reaction rules. This formulation allowed the analysis of the evolution of diversity in the synthesis mechanism and the construction of the pathway architecture of polyketide biosynthesis. It is expected that, after future implementation of the cyclization reactions, this framework can be used to identify all possible polyketides and their corresponding synthesis pathways. Consequently, this formulation would prove useful in guiding experimental approaches to engineer novel polyketides, a number of which will likely have medicinal properties.

Conventional compensation of flow cytometry (FMC) data of an N-stained sample requires additional data sets, of N single-stained control samples, to estimate the spillover coefficients. Single-stained controls however are the least rigorous controls because any of the multi-stained controls are closer to the N-stained sample. In this paper a new, optimization based, compensation method has been developed that is able to use not only single- but also multi-stained controls to improve estimates of the spillover coefficients. The method is demonstrated on a data set from 5-stained dentritic cells (DCs) with 5 single-stained and 8 multi-stained controls. This approach is practical and leads to significant improvements in FCM compensation.