Evidence map›Paper›PMID 31594925›Full record

ArticleNature communications2019

Transient hysteresis and inherent stochasticity in gene regulatory networks.

M Pájaro, I Otero-Muras, C Vázquez, A A Alonso

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Inheritance of extraordinary metabolic activity from parental bacteria individuals.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

4 authors.

M PájaroBioProcess Engineering Group, IIM-CSIC. Spanish National Research Council, Eduardo Cabello 6, 36208, Vigo, Spain.ORCID http://orcid.org/0000-0001-7340-9737
I Otero-MurasBioProcess Engineering Group, IIM-CSIC. Spanish National Research Council, Eduardo Cabello 6, 36208, Vigo, Spain.ORCID http://orcid.org/0000-0003-2895-997X
C VázquezDepartment of Mathematics, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain.ORCID http://orcid.org/0000-0001-6591-2252
A A AlonsoBioProcess Engineering Group, IIM-CSIC. Spanish National Research Council, Eduardo Cabello 6, 36208, Vigo, Spain. antonio@iim.csic.es.ORCID http://orcid.org/0000-0001-8203-1799

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell fate determination, the process through which cells commit to differentiated states is commonly mediated by gene regulatory motifs with mutually exclusive expression states. The classical deterministic picture for cell fate determination includes bistability and hysteresis, which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. We provide a quantitative description of a transient hysteresis phenomenon reconciling experimental evidence of hysteretic behaviour in gene regulatory networks with inherent stochasticity: under sufficiently slow dynamics hysteresis is transient. We quantify this with an estimate of the convergence rate to the equilibrium and introduce a natural landscape capturing system's evolution that, unlike traditional cell fate potential landscapes, is compatible with coexistence at the microscopic level.

Indexed as

Gene Regulatory NetworksModels, GeneticCell DifferentiationComputer SimulationStochastic Processes

Identifiers

PMID31594925
PMCPMC6783536

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.