Evidence map›Paper›PMID 31158226›Full record

ArticlePLoS computational biology2019

Mathematical modelling reveals unexpected inheritance and variability patterns of cell cycle parameters in mammalian cells.

Marzena Mura, Céline Feillet, Roberto Bertolusso, Franck Delaunay, Marek Kimmel

Abstract read
In one paragraph

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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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  10. Statistical Inference for the Evolutionary History of Cancer Genomes.Statistical science : a review journal of the Institute of Mathematical Statistics · 2020
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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.

Marzena MuraSystem Engineering Group, Silesian University of Technology, Gliwice, Poland.ORCID 0000-0002-8482-3439
Céline FeilletUniversité Côte d'Azur, CNRS, INSERM, iBV, Nice, France.
Roberto BertolussoDepartment of Statistics, Rice University, Houston, TX, United States of America.
Franck DelaunayUniversité Côte d'Azur, CNRS, INSERM, iBV, Nice, France.
Marek KimmelSystem Engineering Group, Silesian University of Technology, Gliwice, Poland.

Funding

Multiscale Modeling of Myelodysplastic SyndromesR01HL128173 · NHLBI · VIRGINIA COMMONWEALTH UNIVERSITY · PI COREY, SETH JOEL, KIMMEL, MAREK · 2015 to 2019
$3.8M
NHLBI NIH HHS R01 HL128173
6 · The paper itself

Abstract

The cell cycle is the fundamental process of cell populations, it is regulated by environmental cues and by intracellular checkpoints. Cell cycle variability in clonal cell population is caused by stochastic processes such as random partitioning of cellular components to progeny cells at division and random interactions among biomolecules in cells. One of the important biological questions is how the dynamics at the cell cycle scale, which is related to family dependencies between the cell and its descendants, affects cell population behavior in the long-run. We address this question using a "mechanistic" model, built based on observations of single cells over several cell generations, and then extrapolated in time. We used cell pedigree observations of NIH 3T3 cells including FUCCI markers, to determine patterns of inheritance of cell-cycle phase durations and single-cell protein dynamics. Based on that information we developed a hybrid mathematical model, involving bifurcating autoregression to describe stochasticity of partitioning and inheritance of cell-cycle-phase times, and an ordinary differential equation system to capture single-cell protein dynamics. Long-term simulations, concordant with in vitro experiments, demonstrated the model reproduced the main features of our data and had homeostatic properties. Moreover, heterogeneity of cell cycle may have important consequences during population development. We discovered an effect similar to genetic drift, amplified by family relationships among cells. In consequence, the progeny of a single cell with a short cell cycle time had a high probability of eventually dominating the population, due to the heritability of cell-cycle phases. Patterns of epigenetic heritability in proliferating cells are important for understanding long-term trends of cell populations which are either required to provide the influx of maturing cells (such as hematopoietic stem cells) or which started proliferating uncontrollably (such as cancer cells).

Indexed as

Models, BiologicalAnimalsCell CycleCell ProliferationComputational BiologyComputer SimulationMiceNIH 3T3 Cells

Identifiers

PMID31158226
PMCPMC6564046

What OpenQuestion holds

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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.