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On the conditions for shifts in metabolic strategies

Droste, M.; Planque, R.; Bruggeman, F.

2025-10-04 systems biology
10.1101/2025.10.03.680276 bioRxiv
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AO_SCPLOWBSTRACTC_SCPLOWMany heterotrophic microorganisms gradually replace an energetically-efficient mode of metabolism by an inefficient, more-wasteful overflow metabolism above a critical growth rate, even though the energy demand continues to rise with growth rate. For instance, complete respiration of a sugar is replaced by its fermentation. We derive the conditions for this to happen, using a core model of metabolism and growth that is qualitatively in agreement with experimental data. Assuming a fixed cellular protein content, the model shows that protein expression of efficient metabolism and anabolism rises as function of growth rate until a critical value is reached. This growth-associated protein expression is at the expense of proteins associated with future adaptation. At the critical growth rate, this preparatory pool is reduced to zero. Beyond the critical growth rate, the anabolic protein pool and the energy demand continue to rise and therefore less protein remains for catabolism. In this regime, the inefficient metabolism gradually takes over ATP synthesis from the efficient mode. It can do so if it requires less protein per unit of ATP flux. The catabolic protein pool then decreases while the anabolic pool increases. This continues until all catabolic protein is allocated to the inefficient strategy and to anabolism. We show that this can occur only if the maximal growth rate of the inefficient mode is higher than the critical growth rate. We show that this condition is precisely the requirement for the second mode to be more proteome-efficient than the first mode. Finally, we reduce a genome-scale model of protein expression in the yeast Saccharomyces cerevisiae to a variant of our core model and show that it is still qualitatively in agreement with the experimental data used to validate the original model.

Published in PLOS Computational Biology (predicted rank #1) · training set

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