Abstract
BackgroundObtaining quantitative predictions for cellular metabolic activities requires the identification and modeling of the physicochemical constraints that are relevant at physiological growth conditions. Molecular crowding in a cell's cytoplasm is one such potential constraint, as it limits the solvent capacity available to metabolic enzymes.ResultsUsing a recently introduced flux balance modeling framework (FBAwMC) here we demonstrate that this constraint determines a metabolic switch in E. coli cells when they are shifted from low to high growth rates. The switch is characterized by a change in effective optimization strategy, the excretion of acetate at high growth rates, and a global reorganization of E. coli metabolic fluxes, the latter being partially confirmed by flux measurements of central metabolic reactions.ConclusionThese results implicate the solvent capacity as an important physiological constraint acting on E. coli cells operating at high metabolic rates and for the activation of a metabolic switch when they are shifted from low to high growth rates. The relevance of this constraint in the context of both the aerobic ethanol excretion seen in fast growing yeast cells (Crabtree effect) and the aerobic glycolysis observed in rapidly dividing cancer cells (Warburg effect) should be addressed in the future.
Highlights
Obtaining quantitative predictions for cellular metabolic activities requires the identification and modeling of the physicochemical constraints that are relevant at physiological growth conditions
The addition of the solvent capacity constraint to a flux balance analysis (FBA) model allowed us to explain, within a metabolic efficiency framework, the hierarchy of substrate consumption of E. coli cells growing in a mixture of carbon sources [12]
flux balance analysis with molecular crowding' (FBAwMC) predicts a change of effective metabolic efficiency objective Having estimated the crowding coefficients we evaluate the relevance of the solvent capacity constraint (Eq 3) at physiological growth conditions
Summary
Obtaining quantitative predictions for cellular metabolic activities requires the identification and modeling of the physicochemical constraints that are relevant at physiological growth conditions. The pattern of substrate consumption can be reproduced by superimposing regulatory information obtained e.g., from microarray data [13] Taking together, these results indicate that the FBA model together with the solvent capacity constraint can be used to predict the regulatory mechanisms and, importantly, to understand their advantage in terms of metabolic efficiency and constraints. These results indicate that the FBA model together with the solvent capacity constraint can be used to predict the regulatory mechanisms and, importantly, to understand their advantage in terms of metabolic efficiency and constraints It is not clear, if the limited capacity constraint play a role at other physiological growth conditions, e.g., when nutrients are scarce
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