Evidence map›Paper›PMID 29203832›Full record

ArticleScientific reports2017

Efficient and flexible implementation of Langevin simulation for gene burst production.

Ching-Cher Sanders Yan, Surendhar Reddy Chepyala, Chao-Ming Yen, Chao-Ping Hsu

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2017. 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
0.4field-weighted citation impact, top 36% 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, 7 citations in OpenAlex.

  1. 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

4 authors at 3 institutions in 1 country.

Ching-Cher Sanders YanInstitute of Chemistry, Academia Sinica, Taipei, 115, Taiwan.
Surendhar Reddy ChepyalaInstitute of Chemistry, Academia Sinica, Taipei, 115, Taiwan.
Chao-Ming YenInstitute of Chemistry, Academia Sinica, Taipei, 115, Taiwan.
Chao-Ping HsuInstitute of Chemistry, Academia Sinica, Taipei, 115, Taiwan. cherri@sinica.edu.tw.
Institute of Chemistry, Academia Sinica · TWInstitute of Biological Chemistry, Academia Sinica · TWInstitute of Information Science, Academia Sinica · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression involves bursts of production of both mRNA and protein, and the fluctuations in their number are increased due to such bursts. The Langevin equation is an efficient and versatile means to simulate such number fluctuation. However, how to include these mRNA and protein bursts in the Langevin equation is not intuitively clear. In this work, we estimated the variance in burst production from a general gene expression model and introduced such variation in the Langevin equation. Our approach offers different Langevin expressions for either or both transcriptional and translational bursts considered and saves computer time by including many production events at once in a short burst time. The errors can be controlled to be rather precise (<2%) for the mean and <10% for the standard deviation of the steady-state distribution. Our scheme allows for high-quality stochastic simulations with the Langevin equation for gene expression, which is useful in analysis of biological networks.

Indexed as

AlgorithmsModels, GeneticGene ExpressionProteinsRNA, MessengerProteinsRNA, Messenger

Identifiers

PMID29203832
PMCPMC5715166
OpenAlexW2769051115

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.