Evidence map›Paper›PMID 40279643›Full record

ReviewAnnual review of genomics and human genetics2025

Human Synthetic Biology and Programmable Gene Regulation Control.

Abby R Thurm, Geovanni L Janer Carattini, Lacramioara Bintu

Abstract readReview
In one paragraph

Review in Annual review of genomics and human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. 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

3 authors.

Abby R ThurmProgram in Biophysics, Stanford University School of Medicine, Stanford, California, USA; email: athurm@stanford.edu.
Geovanni L Janer CarattiniDepartment of Genetics, Stanford University School of Medicine, Stanford, California, USA; email: glj14@stanford.edu.
Lacramioara BintuDepartment of Bioengineering, Stanford University, Stanford, California, USA; email: lbintu@stanford.edu.

Funding

Single-cell analysis and synthetic control of mammalian chromatin dynamics and gene regulationR35GM128947 · NIGMS · STANFORD UNIVERSITY · PI Lacramioara Bintu · 2018 to 2026
$3.1M
High-Throughput Measurements of RNA-Mediated Regulation of Gene Expression and OncogenesisF30CA287739 · NCI · STANFORD UNIVERSITY · PI Abby Thurm · 2024 to 2026
$136k
NCI NIH HHS F30 CA287739NIGMS NIH HHS R35 GM128947
6 · The paper itself

Abstract

The growing field of human synthetic biology has rapidly accelerated the development of programmable genetic systems that can control cellular phenotypes and function. As the scale of synthetic systems has increased, researchers have focused on identifying modular regulators that act at the levels of DNA, RNA, and protein to create synthetic control points at each level of gene expression. Expanding these assays to multiple cellular contexts has made it possible to both manipulate endogenous gene programs and create synthetic gene circuits that yield designer cell outputs. Here, we review recent advances in high-throughput human synthetic biology that have led to the development of multilevel tools for gene expression control. We highlight the development of synthetic gene programs that can both provide information on and manipulate cellular behavior and discuss the application of programmable genetic tools in therapeutic contexts to illuminate the power of these new biological approaches.

Indexed as

Gene Expression RegulationSynthetic BiologyGene Regulatory NetworksHumansCRISPR-Cas9degronsepigeneticsgene regulationRNA-binding proteinssynthetic biologysynthetic circuitstranscription factors

Identifiers

PMID40279643
PMCPMC12867107

What OpenQuestion holds

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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.