Evidence map›Paper›PMID 34845228›Full record

ArticleNature communications2021

Independent control of mean and noise by convolution of gene expression distributions.

Karl P Gerhardt, Satyajit D Rao, Evan J Olson, Oleg A Igoshin, Jeffrey J Tabor

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

5 authors.

Karl P GerhardtDepartment of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Satyajit D RaoDepartment of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Evan J OlsonDepartment of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
Oleg A IgoshinDepartment of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.ORCID http://orcid.org/0000-0002-1449-4772
Jeffrey J TaborDepartment of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA. jeff.tabor@rice.edu.ORCID http://orcid.org/0000-0001-7316-0361

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Indexed as

Gene Expression Regulation, BacterialGenes, BacterialPromoter Regions, GeneticEscherichia coliFlow CytometryGenes, ReporterGenetic EngineeringGreen Fluorescent ProteinsLuminescent ProteinsPlasmidsRed Fluorescent ProteinRepressor ProteinsTransformation, BacterialGreen Fluorescent ProteinsLuminescent ProteinsRed Fluorescent ProteinRepressor Proteins

Identifiers

PMID34845228
PMCPMC8630168

What Socratic 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 Socratic graph.