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Flux Imbalance Analysis and the Sensitivity of Cellular Growth to Changes in Metabolite Pools

Figure 5

In a constraint-based method that integrates gene expression (GIMME/TEAM), shadow prices predict the direction of changes in metabolite abundance.

(A) Schematic of the GIMME/TEAM algorithm. Enzymes whose constituent genes show very low expression (red) are penalized. Then, a flux distribution is identified with the lowest total penalty (in this case, the alternative pathway with high expression, colored in green). (B) Schematic of the interpretation of shadow prices in TEAM. Consider a situation in which, at steady-state, a reaction with low gene expression (red, high penalty) is inferred by the model to carry a high flux, leading to a high penalty. When the metabolite is allowed to deviate from steady-state by lowering the flux through the highly penalized reaction, the penalty predicted by TEAM falls. The shadow price λM for this metabolite, whose concentration is predicted to be decreasing, is thus positive. (C) Shadow prices predicted by TEAM and observed changes in metabolite abundance are significantly negatively correlated. A threshold of θ = 0.88 was used, although other values of θ yielded similar results (SI Figure S1). Changes in metabolite abundance were calculated using measurements between hours 10 and 11 in [33] where acetate was observed to be secreted from the cell [32]. Expression data used as input to TEAM is taken from hour 35 of [32]. Both time points correspond to the same phase in the metabolic cycle of yeast, during the end of the oxidative and beginning of the reductive/building phase.

Figure 5

doi: https://doi.org/10.1371/journal.pcbi.1003195.g005