ArticlePLoS computational biology2019
Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism.
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 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Coarse-grained resource allocation modeling for decoding and rewiring microbial metabolism.Trends in microbiology · 2026Review
- Integrative Thermodynamic Strategies in Microbial Metabolism.International journal of molecular sciences · 2025Article
- Nutrient colimitation is a quantitative, dynamic property of microbial populations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Repeated glucose oscillations in high cell-density cultures influence stress-related functions ofPNAS nexus · 2024Article
- Article
- A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes.Journal of the Royal Society, Interface · 2023Article
- Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions.Nature communications · 2023Article
- Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains.eLife · 2023Article
- Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains.PLoS computational biology · 2022Article
- Trade-offs predicted by metabolic network structure give rise to evolutionary specialization and phenotypic diversification.Molecular biology and evolution · 2022Article
- Global pleiotropic effects in adaptively evolvedOpen biology · 2022Article
- Review
- Identification of flux trade-offs in metabolic networks.Scientific reports · 2021Article
- Energetic scaling in microbial growth.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis.BMC bioinformatics · 2021Review
- Functional Analysis of Deoxyhexose Sugar Utilization in Escherichia coli Reveals Fermentative Metabolism under Aerobic Conditions.Applied and environmental microbiology · 2021Article
- Evolution of Pseudomonas aeruginosa toward higher fitness under standard laboratory conditions.The ISME journal · 2021Article
- Bacterial fitness landscapes stratify based on proteome allocation associated with discrete aero-types.PLoS computational biology · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growth rate and yield are fundamental features of microbial growth. However, we lack a mechanistic and quantitative understanding of the rate-yield relationship. Studies pairing computational predictions with experiments have shown the importance of maintenance energy and proteome allocation in explaining rate-yield tradeoffs and overflow metabolism. Recently, adaptive evolution experiments of Escherichia coli reveal a phenotypic diversity beyond what has been explained using simple models of growth rate versus yield. Here, we identify a two-dimensional rate-yield tradeoff in adapted E. coli strains where the dimensions are (A) a tradeoff between growth rate and yield and (B) a tradeoff between substrate (glucose) uptake rate and growth yield. We employ a multi-scale modeling approach, combining a previously reported coarse-grained small-scale proteome allocation model with a fine-grained genome-scale model of metabolism and gene expression (ME-model), to develop a quantitative description of the full rate-yield relationship for E. coli K-12 MG1655. The multi-scale analysis resolves the complexity of ME-model which hindered its practical use in proteome complexity analysis, and provides a mechanistic explanation of the two-dimensional tradeoff. Further, the analysis identifies modifications to the P/O ratio and the flux allocation between glycolysis and pentose phosphate pathway (PPP) as potential mechanisms that enable the tradeoff between glucose uptake rate and growth yield. Thus, the rate-yield tradeoffs that govern microbial adaptation to new environments are more complex than previously reported, and they can be understood in mechanistic detail using a multi-scale modeling approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.