Evidence map›Paper›PMID 28983758›Full record

ReviewWorld journal of microbiology & biotechnology2017

Biotechnological production of bio-based long-chain dicarboxylic acids with oleogenious yeasts.

Nicole Werner, Susanne Zibek

Abstract readReview
PubMed Publisher
In one paragraph

Review in World journal of microbiology & biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Frontiers in microbiology · 2020
    Review
  9. Cellular and metabolic engineering of oleaginous yeastEngineering in life sciences · 2019
    Review
  10. Review
  11. Article
  12. 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

2 authors.

Nicole WernerFraunhofer Institute for Interfacial Engineering and Biotechnology IGB, Stuttgart, Germany.ORCID http://orcid.org/0000-0002-1096-0043
Susanne ZibekFraunhofer Institute for Interfacial Engineering and Biotechnology IGB, Stuttgart, Germany. susanne.zibek@igb.fraunhofer.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long-chain α,ω-dicarboxylic acids (DCAs) are versatile chemical intermediates of industrial importance used as building blocks for the production of polymers, lubricants, or adhesives. The majority of industrial long-chain DCAs is produced from petro-chemical resources. An alternative is their biotechnological production from renewable materials like plant oil fatty acids by microbial fermentation using oleogenious yeasts. Oleogenious yeasts are natural long-chain DCA producers, which have to be genetically engineered for high-yield DCA production. Although, some commercialized fermentation processes using engineered yeasts are reported, bio-based long-chain DCAs are still far from being a mass product. Further progress in bioprocess engineering and rational strain design is necessary to advance their further commercialization. The present article reviews the basic strategies, as well as novel approaches in the strain design of oleogenious yeasts, such as the combination of traditional metabolic engineering with system biology strategies for high-yield long-chain DCA production. Therefore a detailed overview of the involved metabolic processes for the biochemical long-chain DCA synthesis is given.

Indexed as

BiotechnologyDicarboxylic AcidsFermentationGenetic EngineeringPlant OilsRecombinant ProteinsYeastsDicarboxylic AcidsPlant OilsRecombinant ProteinsBioconversionBiopolymersDiacidsMetabolic engineeringVegetable oil

Identifiers

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.