A Method to Constrain Genome-Scale Models with 13C Labeling Data
Fig 3
A) 2S-13C MFA models microbial metabolism at two different scales of resolution, hence minimizing the computational effort to explain the experimental data. While stoichiometric balances are taken into account for the full genome-scale model (iJR904 in this case), metabolite labeling originating from the 13C feed in the labeling experiments is only tracked for the core set of reactions responsible for the main fraction of metabolite labeling (green box). The two-scale approximation assumes that non-core metabolites do not directly affect core metabolite labeling. The core set is expandable through the recursive procedure shown in Fig 2. B) Exemplary network of 20 reactions that illustrates the two-scale approximation and the approach. Measured data involves the MDV for metabolites A, C and E and extracellular fluxes for reactions producing metabolites T, U, Y and Z. The initial core set involves reactions and metabolites in the green box. The fit involves finding fluxes which best match the measured labeling and the values of the measured extracellular fluxes, where only the contribution of reactions inside the green box is taken into account to fit the labeling of metabolites A, C and E. However, the metabolite balance is global. In this way the fluxes are not overconstrained by e.g. NADPH balance: any excess NADPH can be balanced by the non-core fluxes that consume NADPH. C) Right lower panel illustrates External Labeling Variability Analysis (ELVA) for the exemplary network. ELVA gauges the effect of non-core reactions by considering only the core network and simulating the impact of non-core metabolite labeling through inflow metabolites (inflowD, inflowE, inflowF). The ELVA optimization problem (Eqs 9–15) finds the maximum impact that the unknown inflow metabolite labeling can have on the measured labeling pattern.