MétaCan
Menu
Back to cohort
Record W2929296217 · doi:10.1111/jam.14270

Applications and limitations of regulatory<scp>RNA</scp>elements in synthetic biology and biotechnology

2019· review· en· W2929296217 on OpenAlexafffund
Jean-Claude Nshogozabahizi, K.L. Aubrey, Joseph A. Ross, Nehal Thakor

Bibliographic record

VenueJournal of Applied Microbiology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of Lethbridge
FundersMinistry of Economic Development and Trade, Government of AlbertaMinistero dello Sviluppo EconomicoAlberta Innovates - Technology Futures
KeywordsSynthetic biologyRiboswitchComputational biologyRNABiologyFunction (biology)Biochemical engineeringSystems biologyModular designBiotechnologyGeneComputer scienceGeneticsNon-coding RNAEngineering

Abstract

fetched live from OpenAlex

Synthetic biology requires the design and implementation of novel enzymes, genetic circuits or even entire cells, which can be controlled by the user. RNA-based regulatory elements have many important functional properties in this regard, such as their modular nature and their ability to respond to specific external stimuli. These properties have led to the widespread exploration of their use as gene regulation devices in synthetic biology. In this review, we focus on two major types of RNA elements: riboswitches and RNA thermometers (RNATs). We describe their general structure and function, before discussing their potential uses in synthetic biology (e.g. in the production of biofuels and biodegradable plastics). We also discuss their limitations, and novel strategies to implement RNA-based regulatory devices in biotechnological applications. We close with a description of some common model organisms used in synthetic biology, with a focus on the current applications and limitations of RNA-based regulation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.273
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied MicrobiologySame topicRNA and protein synthesis mechanismsFrench-language works237,207