Evidence map›Paper›PMID 30609418›Full record

ArticlePhysical biology2019

A stochastic model for post-transcriptional regulation of rare events in gene expression.

Niraj Kumar, Rahul V Kulkarni

Open access · greenAbstract read
In one paragraph

Article in Physical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 45% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. First passage time in post-transcriptional regulation by multiple small RNAs.The European physical journal. E, Soft matter · 2021
    Article
  2. 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

2 authors at 1 institution in 1 country.

Niraj KumarDepartment of Physics, University of Massachusetts Boston, Boston, MA 02125, United States of America.ORCID 0000-0002-9200-576X
Rahul V KulkarniDepartment of Physics, University of Massachusetts Boston, Boston, MA 02125, United States of America.
University of Massachusetts Boston · US

Funding

UMass Boston-DF/HCC U54 Partnership (2 of 2)U54CA156734 · NCI · UNIVERSITY OF MASSACHUSETTS BOSTON · PI ADAN COLON-CARMONA, Jill A. Macoska · 2010 to 2026
$24.9M
NCI NIH HHS U54 CA156734
6 · The paper itself

Abstract

Post-transcriptional regulation of gene expression is often a critical component of cellular processes involved in cell-fate decisions. Correspondingly, considerable efforts have focused on modeling post-transcriptional regulation of stochastic gene expression and on quantifying its impact on the mean and variance of protein levels. However, the impact of post-transcriptional regulation on rare events corresponding to large deviations in gene expression is less well understood. Here, we study a simple model involving post-transcriptional control of gene expression and characterize the impact of regulation on large deviations in activity fluctuations. We derive analytical results for the large deviation function for protein production rate and for the corresponding driven process which characterizes system fluctuations conditional on the rare event. Our results suggest that fluctuations in burst size rather than frequency play a dominant role in controlling rare events. The results derived also provide insight into how post-transcriptional regulation can be used to fine-tune the probability of rare fluctuations and to thereby control phenotypic variation in a population of cells.

Indexed as

Models, GeneticStochastic ProcessesAnalysis of VarianceGene Expression RegulationHumansModels, BiologicalModels, StatisticalProbabilityProtein BiosynthesisProteinsRNA, MessengerProteinsRNA, Messenger

Identifiers

PMID30609418
PMCPMC7430188
OpenAlexW2906694655

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.