Evidence map›Paper›PMID 37580520›Full record

ArticleBulletin of mathematical biology2023

Emergent Lag Phase in Flux-Regulation Models of Bacterial Growth.

Fiona Bate, Yumechris Amekan, Dmitri O Pushkin, James P J Chong, Martin Bees

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Isolation and characterization of a potential heterotrophic nitrifying-aerobic denitrifying and phosphorus-accumulating bacteriumWater science and technology : a journal of the International Association on Water Pollution Research · 2026
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Fiona BateDepartment of Mathematics, University of York, York, YO10 5DD, UK. fiona.bate@york.ac.uk.ORCID 0000-0002-1424-495X
Yumechris AmekanDepartment of Biology, University of York, York, YO10 5DD, UK.
Dmitri O PushkinDepartment of Mathematics, University of York, York, YO10 5DD, UK.
James P J ChongDepartment of Biology, University of York, York, YO10 5DD, UK.
Martin BeesDepartment of Mathematics, University of York, York, YO10 5DD, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lag phase is observed in bacterial growth during a sudden change in conditions: growth is inhibited whilst cells adapt to the environment. Bi-phasic, or diauxic growth is commonly exhibited by many species. In the presence of two sugars, cells initially grow by consuming the preferred sugar then undergo a lag phase before resuming growth on the second. Biomass increase is characterised by a diauxic growth curve: exponential growth followed by a period of no growth before a second exponential growth. Recent literature lacks a complete dynamic description, artificially modelling lag phase and employing non-physical representations of precursor pools. Here, we formulate a rational mechanistic model based on flux-regulation/proteome partitioning with a finite precursor pool that reveals core mechanisms in a compact form. Unlike earlier systems, the characteristic dynamics emerge as part of the solution, including the lag phase. Focussing on growth of Escherichia coli on a glucose-lactose mixture we show results accurately reproduce experiments. We show that for a single strain of E. coli, diauxic growth leads to optimised biomass yields. However, intriguingly, for two competing strains diauxic growth is not always the best strategy. Our description can be generalised to model multiple different microorganisms and investigate competition between species/strains.

Indexed as

Escherichia coliModels, BiologicalAdaptation, PhysiologicalGlucoseMathematical ConceptsGlucoseDiauxic growthEscherichia coliFlux-regulationLag phaseMechanistic modelProteome partitioning

Identifiers

PMID37580520
PMCPMC10425510

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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.