A Metabolic Flux and Free Energy Analysis Software for Interpreting 13c, 2h, 18o, and 15n Isotope Tracing Data

Quantifying metabolic fluxes offers key insights into pathway utilization, kinetics, and thermodynamics in metabolism. Stable isotope tracers, molecules labeled with a stable isotope(s), are widely used for inferring in vivo metabolic fluxes. Because metabolic flux analysis has emerged as the predominant method for determining fluxes for applications in biotechnology and metabolic engineering, several software packages exist to analyze metabolic activity in silico. However, most existing metabolic flux analysis software do not yet support evaluation of experiments with non-carbon isotopes, which can be powerful complements to ¹³C tracers in parallel labeling experiments and reveal the turnover rates of inorganic metabolites. Moreover, current computational platforms cannot compute metabolic Gibbs free energies for thermodynamic pathway analysis. To address these challenges, we developed G-Flux, a MATLAB-based software application for computing metabolic fluxes by tracing ¹³C, ²H, ¹⁸O, and ¹⁵N isotopes. By integrating the backward-to-forward flux ratio, G-Flux can also compute metabolic reaction Gibbs free energies using only isotope-labeling data. Through simulations of ¹³C and ²H glucose tracing experiments, we computed metabolic flux and free energy values of reactions within the glycolysis and pentose phosphate pathways of Escherichia coli. Most 95% confidence intervals of the corresponding free energies determined by G-Flux were narrower than those of the published values. Using these precedingly calculated fluxes, we further simulated mass isotopomer distributions of metabolites in Escherichia coli and identified [6-¹⁸O] glucose to be a candidate for inferring glycolytic reversibility and [U-¹³C₅¹⁵N₂] glutamine to be a candidate for quantifying fluxes and free energies of transaminase and amino acid degradation pathways. Through quantifying metabolic rates and guiding the rational selection of optimal tracers for labeling experiments, we anticipate G-Flux to facilitate efforts in improved metabolic flux and free energy analysis.