Metabolic flux profiling designates the quantitative determination of the rates at which metabolites are converted through intracellular biochemical reactions—the metabolic fluxes—in a living biological system, yielding a flux map that describes the functional state of the metabolic network under defined genetic or environmental conditions ^[1]. Its conceptual core rests on a distinction between two classes of flux: extracellular fluxes, such as substrate uptake and product secretion rates, which cross the cell membrane and are directly measurable by tracking extracellular metabolite concentrations or biomass over time; and intracellular fluxes, which do not cross the membrane and cannot be measured directly ^[2]. Intracellular flux profiling therefore proceeds inferentially: an isotopically labeled substrate (for example, ¹³C-glucose) is supplied, the resulting labeling patterns of downstream metabolites—expressed as mass distribution vectors describing the fractional enrichment of isotopologues and isotopomers—are measured by mass spectrometry or nuclear magnetic resonance, and a metabolic network model is used to compute the flux distribution that best accounts for those patterns ^[1][2]. The computational procedure integrating isotope labeling data with a stoichiometric network model to generate a quantitative flux map is termed isotope-assisted metabolic flux analysis (iMFA) ^[3]. Profiling may be conducted under metabolic and isotopic steady state, in which labeling patterns are stable and fluxes are assumed constant, or under isotopically nonstationary conditions, in which transient labeling kinetics are modeled to resolve fluxes in systems that do not reach steady state ^[4]. A defining feature of metabolic flux profiling is that its output—the flux distribution—constitutes a phenotype-level description that integrates the effects of gene expression, enzyme abundance, and allosteric regulation, capturing the net functional behavior of the metabolic network rather than the abundance of any individual component ^[2][5].