Evidence map›Paper›PMID 33198305›Full record

ArticleMetabolites2020

Streamlining the Analysis of Dynamic

André Feith, Andreas Schwentner, Attila Teleki, Lorenzo Favilli, Bastian Blombach, Ralf Takors

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 36% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Transcriptional Regulation of the Creatine Utilization Genes ofFrontiers in bioengineering and biotechnology · 2022
    Article
  3. 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

6 authors at 2 institutions in 1 country.

André FeithInstitute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.ORCID 0000-0001-5934-3073
Andreas SchwentnerInstitute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
Attila TelekiInstitute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
Lorenzo FavilliInstitute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
Bastian BlombachMicrobial Biotechnology, Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Schulgasse 22, 94315 Straubing, Germany.ORCID 0000-0002-2996-2049
Ralf TakorsInstitute of Biochemical Engineering, University of Stuttgart, Allmandring 31, 70569 Stuttgart, Germany.
University of Stuttgart · DETechnical University of Munich · DE

Funding

Fachagentur Nachwachsende Rohstoffe 22008014
6 · The paper itself

Abstract

Today's possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream

Indexed as

Corynebacterium glutamicumdesign-build-test-learn (DBTL) cyclehydrophilic interaction chromatography (HILIC)l-histidinemetabolic engineeringmetabolomicspool influx kineticsquadrupole time-of-flight high-resolution mass spectrometer (QTOF-HRMS)strain engineering

Identifiers

PMID33198305
PMCPMC7696456
OpenAlexW3100465255

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.