(679d) Metabolic Flux Elucidation for Genome-Scale Models Using 13C Labeled Isotopes

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 constraint-based modeling framework with isotopic label tracing on the genome-scale. When cells are fed a growth substrate with certain carbon positions labeled with ¹³C, 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 complete reaction network consisting of 353 reactions and 184 metabolites in Escherichia coli. Further, by including global metabolite balances on cofactors such as ATP, NADH, and NADPH, our framework generates biologically-relevant flux distributions. 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 a small fraction of labeled glucose; the measurements are made using GCMS performed on amino acids extracted from the cells. We identify flux distributions for which the calculated labeling patterns agree within a few percent of 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 constraints on the model, as well as shed light onto the customization of the developed computational framework to other systems.