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Metab Eng. 2015 Nov;32:232-243. doi: 10.1016/j.ymben.2015.10.003. Epub 2015 Oct 21.

Quantitative prediction of genome-wide resource allocation in bacteria.

Author information

INRA, UR1404, MaIAGE, F-78350 Jouy-en-Josas, France.
Institute for Microbiology, Ernst-Moritz-Arndt University Greifswald, D-17489 Greifswald, Germany.
Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland.
INRA, UMR Micalis, F-78350 Jouy-en-Josas, France; AgroParisTech,UMR Micalis, F-78350 Jouy-en-Josas, France.
INRA, UR1404, MaIAGE, F-78350 Jouy-en-Josas, France. Electronic address:


Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.


Constraint-based modeling; Resource allocation; Strain design; Systems biology

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