Selected technologies to control genes and their products for experimental and clinical purposes
Bibliographic record
Abstract
"On-demand" regulation of gene expression is a powerful tool to elucidate the functions of proteins and biologically-active RNAs. We describe here three different approaches to the regulation of expression or activity of genes or proteins. Promoter-based regulation of gene expression was among the most rapidly developing techniques in the 1980s and 1990 s. Here we provide basic information and also some characteristics of the metallothionein-promoter-based system, the tet-off system, Muristerone-A-regulated expression through the ecdysone response element, RheoSwitch, coumermycin/novobiocin-regulated gene expression, chemical dimerizer-based promoter activation systems, the "Dual Drug Control" system, "constitutive androstane receptor"-based regulation of gene expression, and RU486/mifepristone-driven regulation of promoter activity. A large part of the review concentrates on the principles and usage of various RNA interference techniques (RNAi: siRNA, shRNA, and miRNA-based methods). Finally, the last part of the review deals with historically the oldest, but still widely used, methods of temperature-dependent regulation of enzymatic activity or protein stability (temperature-sensitive mutants). Due to space limitations we do not describe in detail but just mention the tet-regulated systems and also fusion-protein-based regulation of protein activity, such as estrogen-receptor fusion proteins. The information provided below is aimed to assist researchers in choosing the most appropriate method for the planned development of experimental systems with regulated expression or activity of studied proteins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".