Evidence map›Paper›PMID 38811603›Full record

ArticleNPJ systems biology and applications2024

Mechanisms of cell size regulation in slow-growing Escherichia coli cells: discriminating models beyond the adder.

César Nieto, César Augusto Vargas-García, Juan Manuel Pedraza, Abhyudai Singh

Abstract read
In one paragraph

Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  10. A GeneralizedbioRxiv : the preprint server for biology · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

César NietoDepartment of Physics, Universidad de los Andes, Bogotá, Colombia.ORCID http://orcid.org/0000-0001-6504-4619
César Augusto Vargas-GarcíaAGROSAVIA Corporación Colombiana de Investigación Agropecuaria, Mosquera, Colombia.
Juan Manuel PedrazaDepartment of Physics, Universidad de los Andes, Bogotá, Colombia.
Abhyudai SinghDepartment of Electrical and Computer Engineering, University of Delaware, Newark, DE, 19716, USA. absingh@udel.edu.

Funding

Generalized fluctuation test for deciphering phenotypic switching within cell populationsR35GM148351 · NIGMS · UNIVERSITY OF DELAWARE · PI Abhyudai Singh · 2023 to 2026
$1.6M
NIGMS NIH HHS R35 GM148351U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) R35GM148351
6 · The paper itself

Abstract

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.

Indexed as

Escherichia coliModels, BiologicalCell DivisionCulture MediaCulture Media

Identifiers

PMID38811603
PMCPMC11137094

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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.