Evidence map›Paper›PMID 35666436›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2022

RNP-Based Control Systems for Genetic Circuits in Synthetic Biology Beyond CRISPR.

Trevor R Simmons, Andrew D Ellington, Lydia M Contreras

Abstract readReview
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

3 authors at 1 institution in 1 country.

Trevor R SimmonsMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA.
Andrew D EllingtonDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA.
Lydia M ContrerasMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA. lcontrer@che.utexas.edu.
The University of Texas at Austin · US

Funding

Novel mechanisms of posttranscriptional regulation by CsrAR01GM135495 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI CONTRERAS, LYDIA MARIA · 2020 to 2024
$1.8M
NIGMS NIH HHS R01 GM135495
6 · The paper itself

Abstract

Ribonucleoproteins (RNPs) are RNA-protein complexes utilized natively in both prokaryotes and eukaryotes to regulate essential processes within the cell. Over the past few years, many of these native systems have been adapted to provide control over custom genetic targets. Engineered RNP-based control systems allow for fine-tune regulation of desired targets, by providing customizable nucleotide-nucleotide interactions. However, as there have been several engineered RNP systems developed recently, identifying an optimal system for various bioprocesses is challenging. Here, we review the most successful engineered RNP systems and their applications to survey the current state of the field. Additionally, we provide selection criteria to provide users a streamlined method for identifying an RNP control system most useful to their own work. Lastly, we discuss future applications of RNP control systems and how they can be utilized to address the current grand challenges of the synthetic biology community.

Indexed as

Clustered Regularly Interspaced Short Palindromic RepeatsGene EditingCRISPR-Cas SystemsNucleotidesRibonucleoproteinsSynthetic BiologyNucleotidesRibonucleoproteinsCRISPR-CasEngineered RNP controlGenetic circuitsPosttranscriptional regulationRibonucleoproteins

Identifiers

PMID35666436
PMCPMC13254704
OpenAlexW4285232892

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.