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Metabolic flux analysis (MFA) is a powerful tool for exploring and quantifying carbon traffic in metabolic networks. Accurate flux quantification requires (1) high-quality isotopomer measurements, usually of biomass components including proteinogenic/free amino acids or central carbon metabolites, and (2) a mathematical model that relates the unknown fluxes to the measured isotopomers. Modeling requires a thorough knowledge of the structure of the underlying metabolic network, often available from many databases, as well as the ability to make reasonable assumptions that will enable simplification of the model. Here we describe a general methodology underlying computer-aided mathematical modeling of a flux–isotopomer relationship and some of the accompanying data-processing steps. One of two modeling strategies will need to be employed, depending on the type of isotope labeling experiment performed. These strategies—steady-state modeling and instationary modeling—have different experimental and computational demands. We discuss the concepts underlying these two types of modeling and demonstrate steady-state modeling in a step-by-step manner. Our methodology should be applicable to most isotope-assisted MFA applications and should serve as a general framework applicable to many realistic metabolic networks with little modification.
