How Biochemical Constraints of Cellular Growth Shape Evolutionary Adaptations in Metabolism
Open accessAbstract
Evolutionary adaptations in metabolic networks are fundamental to evolution of microbial growth. Studies on unneeded-protein synthesis indicate reductions in fitness upon nonfunctional protein synthesis, showing that cell growth is limited by constraints acting on cellular protein content. Here, we present a theory for optimal metabolic enzyme activity when cells are selected for maximal growth rate given such growth-limiting biochemical constraints. We show how optimal enzyme levels can be understood to result from an enzyme benefit minus cost optimization. The constraints we consider originate from different biochemical aspects of microbial growth, such as competition for limiting amounts of ribosomes or RNA polymerases, or limitations in available energy. Enzyme benefit is related to its kinetics and its importance for fitness, while enzyme cost expresses to what extent resource consumption reduces fitness through constraint-induced reductions of other enzyme levels. A metabolic fitness landscape is introduced to define the fitness potential of an enzyme. This concept is related to the selection coefficient of the enzyme and can be expressed in terms of its fitness benefit and cost.
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Self-replicating models and whole-cell models are either mostly qualitative in nature or require an excessive number of parameters that are not currently available.
“The latter two approaches either are mostly qualitative in nature or require an excessive number of parameters that are not currently available.”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Berkhout et al, 2013
The protein-limitation explanation for overflow metabolism has been shown with different approaches in a self-replicator model, Escherichia coli, cancer cells, and Saccharomyces cerevisiae.
“This has been shown with different approaches in a self‐replicator model (Molenaar et al, 2009; Berkhout et al, 2013), E. coli (Vazquez et al, 2008; O'Brien et al, 2013; Basan et al, 2015; Peebo et al, 2015), cancer cells (Shlomi et al, 2011; Vazquez & Oltvai, 2011), and S. cerevisiae (Van Hoek & Merks, 2012; Nilsson & Nielsen, 2016).”
From Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints (2017) · cites this paper as Berkhout et al, 2013
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- Authors
- Berkhout, J., Bosdriesz, E., Nikerel, E., Molenaar, D., de Ridder, D., Teusink, B., & Bruggeman, F. J.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- Netherlands Bioinformatics Centre (NL)
- Kluyver Centre for Genomics of Industrial Fermentation , 2600 GA, Delft, The Netherlands
- Systems Bioinformatics , The Centre for Integrative Bioinformatics VU, VU University, 1081 HV, Amsterdam, The Netherlands
- Corresponding author
- Bruggeman, Frank J
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-06-01
- Volume
- 194
- Issue
- 2
- Pages
- 505-512
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC3664859
- OpenAlex
- W2122530127
- Cited by
- 56
- References
- 37
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 2 · 2020: 2 · 2019: 18 · 2018: 5 · 2017: 3 · 2016: 2 · 2015: 5 · 2014: 1 · 2013: 1
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- VU Research Portal (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (837.000.001)
- Norwegian Biodiversity Information Centre
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- growth rate maximization, protein synthesis, energy limitation, fitness landscape, microbial growth, selection coefficients, metabolic networks
- MeSH terms
- Adaptation, Physiological, Bacteria, Energy Metabolism, Enzymes, Models, Genetic, Ribosomes, DNA-Directed RNA Polymerases, Selection, Genetic, Protein Biosynthesis, Evolution, Molecular, Cell Growth Processes, Metabolic Networks and Pathways, Genetic Fitness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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