A quantitative assessment of model behavior was done by comparing model predictions to experimentally determined amino acid uptake rates and by-product secretion rates reported by Takahashi et al. (). (A) Experimentally measured fractions of amino acids consumed by *P. gingivalis* against corresponding flux balance model predictions in linear scale (main plot) and log-log scale (inset) (see Materials and Methods for details). The model correctly predicts glutamate, aspartate, serine, and threonine as the four amino acids with the highest uptake rates. In addition, the model predictions of the relative rates with which less highly utilized amino acids are taken up shows strong correlation with the experiments. On the other hand, model predictions for some amino acids do not match the experiments. For example, the model fails to predict that valine, leucine, and arginine are taken up in significant amounts, although experimental measurements suggest they are utilized nearly as much as serine, threonine, and alanine. Such discrepancies indicate incomplete knowledge of the catabolic route for these amino acids and suggest metabolic pathways where further research can provide novel insights. (B) Experimentally determined by-product secretion rates (▪) were compared to two sets of model predictions. The first (▨) was the set of model-predicted production rates compatible with optimal growth, with the minimal Manhattan distance from the associated experiments. The second (□) was the set of model predicted production rates compatible with optimal growth, which were associated with the flux solution minimizing the overall sum of absolute values of fluxes through the network. Minimizing the sum of flux through the network is a common secondary optimization used to select a particular optimal flux solution. Both sets of predicted fluxes show good agreement with experiments for butyrate, acetate, and ammonium secretion. The predicted rates, which were minimally distant from the experimental values, also accurately capture propionate production. Despite the good agreement with experiments, there is clearly significant flexibility in model-predicted by-product production rates that are compatible with optimal growth. (C) The relationship between model predictions for propionate, succinate, and butyrate production, in the same tryptone media utilized in part A, was assessed by computing all possible values for the three production fluxes, which were consistent with optimal growth. This analysis demonstrated that the model only has constraints on the overall by-product production rate and not on the rates of production of the specific by-products. Succinate and propionate production are metabolically equivalent in the model, with butyrate production differing from the other two by a multiplicative factor. It should be noted that the equivalence between these by-products is only true for the catabolism of particular amino acids. This explains why, despite the symmetry of the surface, butyrate production must be greater than zero for all solutions, whereas succinate and propionate production can be zero. Specifically, butyrate production from glutamate is not equivalent to propionate and succinate production.

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