ArticlePLoS computational biology2019
The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- On the conditions for shifts in metabolic strategies.PLoS computational biology · 2026Review
- Stoichiometric analysis of microbial communities links function, structure, and biomass carrying capacity.The ISME journal · 2026Review
- Algebraic differentiation for fast sensitivity analysis of optimal flux modes in metabolic models.Bioinformatics (Oxford, England) · 2025Article
- Carbon upshift inmBio · 2025Article
- A framework for integrating genomics, microbial traits, and ecosystem biogeochemistry.Nature communications · 2025Article
- Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model.Nature microbiology · 2024Article
- The Architecture of Metabolic Networks Constrains the Evolution of Microbial Resource Hierarchies.Molecular biology and evolution · 2023Article
- Overflow metabolism at the thermodynamic limit of life: How carboxydotrophic acetogens mitigate carbon monoxide toxicity.Microbial biotechnology · 2023Article
- Escherichia coli robustly expresses ATP synthase at growth rate-maximizing concentrations.The FEBS journal · 2022Article
- Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies.Nature communications · 2022Article
- Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis.BMC bioinformatics · 2021Review
- The effect of natural selection on the propagation of protein expression noise to bacterial growth.PLoS computational biology · 2021Article
- Understanding FBA Solutions under Multiple Nutrient Limitations.Metabolites · 2021Article
- Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments.Molecular systems biology · 2021Article
- Bacterial cell proliferation: from molecules to cells.FEMS microbiology reviews · 2021Review
- Article
- Searching for principles of microbial physiology.FEMS microbiology reviews · 2020Review
- Dynamic metabolic resource allocation based on the maximum entropy principle.Journal of mathematical biology · 2020Article
- Selecting for lactic acid producing and utilising bacteria in anaerobic enrichment cultures.Biotechnology and bioengineering · 2020Article
- Pyruvate kinase variant of fission yeast tunes carbon metabolism, cell regulation, growth and stress resistance.Molecular systems biology · 2020Article
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5 authors.
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Abstract
Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized-in a static environment under mass-conservation and enzyme expression constraints-we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.
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