Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Enzyme kinetics shapes the growth response of metabolic networks. |
| Συγγραφείς: |
Seeger, Leon1 (AUTHOR), Pinheiro, Fernanda2 (AUTHOR), Lässig, Michael1 (AUTHOR) mlaessig@uni-koeln.de |
| Πηγή: |
PLoS Computational Biology. 8/19/2026, Vol. 22 Issue 8, p1-32. 32p. |
| Θεματικοί όροι: |
*Enzyme kinetics, *Microbial metabolism, *Biological networks, *Metabolites, *Protein expression, *Regulation of growth |
| Περίληψη: |
Microbes adjust their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates. Average expression levels in large proteome sectors change coherently, while individual proteins show divergent shifts even within the same pathway. Here, we establish a metabolic model that integrates local enzyme kinetics and global network architecture to predict the joint growth response of proteins and metabolites. Under nutrient limitation, we predict a remarkably simple pattern of proteome reallocation with growth rate: protein expression levels change linearly but heterogeneously. For a given enzyme, the direction of change is determined by its local kinetic constants – catalytic rate and substrate affinity – and by the degree of nutrient restriction affecting its embedding pathway. This double-graded growth response of the proteome is mediated by restriction-dependent metabolite levels, which are predicted to decrease with growth rate in a nonlinear way. The model establishes three specific growth laws: protein expression changes of individual enzymes are negatively correlated with their expression and with their substrate saturation at high growth; average changes of pathways and larger functional sectors are correlated with their internal variance. These predictions are in quantitative agreement with measured system-wide proteomics and metabolomics data of E. coli. Enzyme-specific response patterns are a starting point for model-guided interventions into bacterial metabolism. Author summary: Engineering microbes, microbial communities, or novel antimicrobials depends on understanding cell metabolism in diverse growth environments. Here, we establish a model of cellular resource allocation in networks of biochemically heterogeneous enzymes and metabolites. We identify key variables that determine resource allocation to an enzyme-substrate pair: its biochemical quality and its position in the metabolic network. Our model predicts quantitative patterns of systems metabolism, explains empirical observations of growth-dependent protein expression, and sets the stage for regulatory and evolutionary analysis of metabolic networks. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: |
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