Evidence map›Paper›PMID 42420735›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Harnessing CRISPRi Competition to Develop Multimodule Controllers for Resource-Aware Circuit Design.

Sadikshya Rijal, Rong Zhang, Xiao-Jun Tian

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Sadikshya RijalSchool of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA.
Rong ZhangSchool of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA.
Xiao-Jun TianSchool of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA. xiaojun.tian@asu.edu.

Funding

Multi-Scale Engineering of Heterogeneity in the Host-Aware Synthetic Gene CircuitsR35GM142896 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI TIAN, XIAOJUN · 2021 to 2025
$2.0M
NIGMS NIH HHS R35 GM142896
6 · The paper itself

Abstract

Cellular resource limitations give rise to resource competition, undermining the modularity and predictability of engineered genetic circuits. In systems containing positive feedback, such competition can drive Winner-Takes-All (WTA) dynamics, resulting in severe imbalances in resource allocation across circuit modules. In this chapter, we present an experimental implementation of a Negatively Competitive Regulatory (NCR) controller based on CRISPR interference (CRISPRi) in dual self-activation (DSA) circuits. We describe a detailed workflow for chromosomal integration of a tunable dCas9 expression cassette, as well as the design of self-activation modules and module-specific guide RNAs that induce self-repression through competition for limiting dCas9. This architecture introduces effective negative feedback to the more active module while reallocating resources to the less active one, thereby promoting balanced module activity. Finally, we provide guidelines for quantitatively assessing the regulation of resource competition between DSA modules using the NCR strategy. Overall, these guidelines demonstrate how CRISPRi can be leveraged to implement NCR strategy in gene circuits, thereby enhancing circuit modularity and predictability.

Indexed as

CRISPR-Cas SystemsGene Regulatory NetworksGenetic EngineeringRNA, Guide, CRISPR-Cas SystemsRNA, Guide, CRISPR-Cas SystemsdCas9 competitionModularityModule coactivationMulti-module controlResource decoupling

Identifiers

PMID42420735
PMCPMC13566794

What OpenQuestion holds

Textmetadata
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.