Evidence map›Paper›PMID 25724988›Full record

ArticleJournal of biological physics2015

Modeling the effect of transcriptional noise on switching in gene networks in a genetic bistable switch.

Srabanti Chaudhury

Abstract read
In one paragraph

Article in Journal of biological physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Srabanti ChaudhuryDepartment of Chemistry, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pune, 411008, India, srabanti@iiserpune.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene regulatory networks in cells allow transitions between gene expression states under the influence of both intrinsic and extrinsic noise. Here we introduce a new theoretical method to study the dynamics of switching in a two-state gene expression model with positive feedback by explicitly accounting for the transcriptional noise. Within this theoretical framework, we employ a semi-classical path integral technique to calculate the mean switching time starting from either an active or inactive promoter state. Our analytical predictions are in good agreement with Monte Carlo simulations and experimental observations.

Indexed as

Gene Regulatory NetworksModels, GeneticTranscription, GeneticFeedback, PhysiologicalMonte Carlo MethodPromoter Regions, Genetic

Identifiers

PMID25724988
PMCPMC4456491

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.