Evidence map›Paper›PMID 41640038›Full record

ArticleMolecular systems biology2023

Mapping single-cell responses to population-level dynamics during antibiotic treatment.

Kyeri Kim, Teng Wang, Helena R Ma, Emrah Şimşek, Boyan Li, Virgile Andreani, Lingchong You

Abstract read
In one paragraph

Article in Molecular systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Contributions of intra- and extracellular antibiotic degradation to collective [Formula: see text]-lactam survival.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Evolutionary Advantage of Cell Size Control.Physical review letters · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Microbial life in slow and stopped lanes.Trends in microbiology · 2024
    Review
  14. Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  15. 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

7 authors.

Kyeri KimDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0003-1503-9090
Teng WangDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.
Helena R MaDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0003-3659-7948
Emrah ŞimşekDepartment of Biomedical Engineering, Duke University, Durham, NC, USA.ORCID https://orcid.org/0000-0003-4550-7158
Boyan LiIntegrated Science Program, Yuanpei College, Peking University, Beijing, China.
Virgile AndreaniBiomedical Engineering Department, Boston University, Boston, MA, USA.ORCID https://orcid.org/0000-0002-2383-1478
Lingchong YouDepartment of Biomedical Engineering, Duke University, Durham, NC, USA. you@duke.edu.ORCID https://orcid.org/0000-0003-3725-4007

Funding

Post-antibiotic effect and design of optimal antibiotic dosing protocolsR01GM098642 · NIGMS · DUKE UNIVERSITY · PI YOU, LINGCHONG · 2011 to 2025
$3.6M
Tradeoffs between fitness costs and transfer rates in horizontal gene transferR01AI125604 · NIAID · DUKE UNIVERSITY · PI LINGCHONG YOU · 2017 to 2026
$3.3M
Targeted control of self-transmissible plasmids by using engineered interfering plasmidsR01EB031869 · NIBIB · DUKE UNIVERSITY · PI YOU, LINGCHONG · 2021 to 2024
$1.5M
HHS|National Institutes of Health (NIH) R01AI125604HHS|National Institutes of Health (NIH) R01EB031869HHS|National Institutes of Health (NIH) R01GM098642National Science Foundation (NSF) MCB-1937259US-Israel Binational Science Foundation 2021192
6 · The paper itself

Abstract

Treatment of sensitive bacteria with beta-lactam antibiotics often leads to two salient population-level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population-level responses emerge from collective single-cell responses. During beta-lactam treatment, it is well-recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single-cell lysis probabilities can give rise to population-level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time-kill curve emerges from single cells and allows the representation of both single- and population-level responses with universal parameters.

Indexed as

antibiotic responsebacterial population dynamicsfilamentationquantitative biologysingle‐cell analysis

Identifiers

PMID41640038
PMCPMC10333910

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Registered trials

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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.