Evidence map›Paper›PMID 35700119›Full record

ArticleACS synthetic biology2022

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors.

Christopher J Hartline, Fuzhong Zhang

Open access · greenAbstract read
In one paragraph

Article in ACS synthetic biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.7field-weighted citation impact, top 15% 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

9 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
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  3. EnhancingApplied and environmental microbiology · 2025
    Article
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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

2 authors at 1 institution in 1 country.

Christopher J HartlineDepartment of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.ORCID 0000-0002-6030-911X
Fuzhong ZhangDepartment of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, Saint Louis, Missouri 63130, United States.ORCID 0000-0001-6979-7909
Washington University in St. Louis · US

Funding

Supplement for Purchase of a Cell Sorter for R35GM133797R35GM133797 · NIGMS · WASHINGTON UNIVERSITY · PI ZHANG, FUZHONG · 2019 to 2023
$2.2M
NIGMS NIH HHS R35 GM133797
6 · The paper itself

Abstract

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in

Indexed as

Biosensing TechniquesTranscription FactorsEscherichia coliGene Expression RegulationMetabolic EngineeringSynthetic BiologyTranscription Factorsbiological circuitcontext dependencedynamic pathway regulationmetabolic engineeringmetabolite biosensortranscriptional regulator

Identifiers

PMID35700119
PMCPMC9994378
OpenAlexW4282831060

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.