A Method to Constrain Genome-Scale Models with 13C Labeling Data
Fig 4
External Labeling Variability Analysis (ELVA, see methods) shows how the impact of ignored reactions diminishes by expanding the core set of reactions. Each point corresponds to an m value of the Mass Distribution Vector (MDV) for each of the metabolites considered. The inset provides the same information for malate in a more intuitive form (red for experimental data, blue for computational fits), see S1–S3 Figs. Horizontal error bars indicate experimental CE-TOFMS error obtained from the instrument. Vertical error bars indicate computational errors obtained from the ELVA. These computational error bars indicate the maximum effect that non-core reactions (whose contribution to the carbon labeling is being ignored) could possibly have. The initial core set (left) shows a large computational error for malate (mal-L, green dots). By expanding the core set, the computational errors collapse to levels comparable with the experimental error as can be seen in the right panel. Hence, the method is self-consistent by ensuring that the final result meets the approximation used to calculate it.