Evidence map›Paper›PMID 34278591›Full record

ArticleBioEssays : news and reviews in molecular, cellular and developmental biology2021

Predicting plasmid persistence in microbial communities by coarse-grained modeling.

Teng Wang, Andrea Weiss, Yuanchi Ha, Lingchong You

Abstract read
In one paragraph

Article in BioEssays : news and reviews in molecular, cellular and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. A plasmid with theMicrobiology (Reading, England) · 2025
    Article
  3. Article
  4. 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

4 authors.

Teng WangDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Andrea WeissDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Yuanchi HaDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.
Lingchong YouDepartment of Biomedical Engineering, Duke University, Durham, North Carolina, USA.ORCID 0000-0003-3725-4007

Funding

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
NIAID NIH HHS R01 AI125604NIBIB NIH HHS R01 EB031869
6 · The paper itself

Abstract

Plasmids are a major type of mobile genetic elements (MGEs) that mediate horizontal gene transfer. The stable maintenance of plasmids plays a critical role in the functions and survival for microbial populations. However, predicting and controlling plasmid persistence and abundance in complex microbial communities remain challenging. Computationally, this challenge arises from the combinatorial explosion associated with the conventional modeling framework. Recently, a plasmid-centric framework (PCF) has been developed to overcome this computational bottleneck. This framework enables the derivation of a simple metric, the persistence potential, to predict plasmid persistence and abundance. Here, we discuss how PCF can be extended to account for plasmid interactions. We also discuss how such model-guided predictions of plasmid fates can benefit from the development of new experimental tools and data-driven computational methods.

Indexed as

Gene Transfer, HorizontalMicrobiotaPlasmidscoarse-grained modelhorizontal gene transfermachine learningmicrobial communitiesmobile genetic elementsnext generation sequencingplasmid persistence

Identifiers

PMID34278591
PMCPMC8822872

What OpenQuestion holds

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Registered trials

None linked

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.