Metabolic flux elucidation for large-scale models using 13C labeled isotopes

Abstract

A key consideration in metabolic engineering is the determination of fluxes of the metabolites within the cell. This determination provides an unambiguous description of metabolism before and/or after engineering interventions. Here, we present a computational framework that combines a constraintbased modeling framework with isotopic label tracing on a large-scale. When cells are fed a growth substrate with certain carbon positions labeled with 13 C, the distribution of this label in the intracellular metabolites can be calculated based on the known biochemistry of the participating pathways. Most labeling studies focus on skeletal representations of central metabolism and ignore many flux routes that could contribute to the observed isotopic labeling patterns. In contrast, our approach investigates the importance of carrying out isotopic labeling studies using a more comprehensive reaction network consisting of 350 fluxes and 184 metabolites in Escherichia coli including global metabolite balances on cofactors such as ATP, NADH, and NADPH. The proposed procedure is demonstrated on an E. coli strain engineered to produce amorphadiene, a precursor to the anti-malarial drug artemisinin. The cells were grown in continuous culture on glucose containing 20% [U-13 C]glucose; the measurements are made using GC-MS performed on 13 amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree well with the measurements alluding to the accuracy of the network reconstruction. Furthermore, we explore the robustness of the flux calculations to variability in the experimental MS measurements, as well as highlight the key experimental measurements necessary for flux determination. Finally, we discuss the effect of reducing the model, as well as shed light onto the customization of the developed computational framework to other systems.

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

“…Theorem 3 of Isermann and Wiechert (2003)). We found that I PDO reduces the sum of all flux ranges (see Suthers et al, 2007) by about an order of magnitude. In summary, in this example we demonstrated that a preliminary implementation of OptMeas can exhaustively generate minimal measurement sets without requiring any measurement to be pre-selected in advance for small to medium-size metabolic models.…”

“…whereas the production and consumption terms for every isotopomer form of each intermediate metabolite must be equal to each other (see Suthers et al, 2007 for a derivation):…”

“…However, the nonlinearities in isotopic balances make it difficult to guarantee that even a seemingly encompassing set of measurements can uniquely determine all fluxes. Indeed, it was shown that a single labeling experiment is unlikely to uniquely determine the great majority of fluxes especially when large-scale isotope mapping reconstructions are employed (Suthers et al, 2007).…”

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…These analyses typically utilize models containing approximately 100 reactions or less and most often, focus on incorporating nonlinear analysis to understand quantitative experimental data (e.g., isotopomer modeling). With the advancement of computational power and developed platforms, the networks that can be analyzed will grow in size 97 . Given that the results produced from analyses such as isotopomer modeling have been shown to be highly dependent on the content of a reduced model, the logical starting point for building such models is the E. coli GEM 97 .…”

“…With the advancement of computational power and developed platforms, the networks that can be analyzed will grow in size 97 . Given that the results produced from analyses such as isotopomer modeling have been shown to be highly dependent on the content of a reduced model, the logical starting point for building such models is the E. coli GEM 97 . A number of noteworthy studies have been conducted with reduced models, but not detailed here as they are outside the scope of this review.…”