Evidence map›Paper›PMID 31134415›Full record

ReviewJournal of industrial microbiology & biotechnology2019

Tools and systems for evolutionary engineering of biomolecules and microorganisms.

Sungho Jang, Minsun Kim, Jaeseong Hwang, Gyoo Yeol Jung

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of industrial microbiology & biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Recent progress in adaptive laboratory evolution of industrial microorganisms.Journal of industrial microbiology & biotechnology · 2023
    Article
  3. Review
  4. Article
  5. Article
  6. Review
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

4 authors.

Sungho JangDepartment of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, 37673, Korea.
Minsun KimSchool of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, 37673, Korea.
Jaeseong HwangSchool of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, 37673, Korea.
Gyoo Yeol JungDepartment of Chemical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang, Gyeongbuk, 37673, Korea. gyjung@postech.ac.kr.ORCID http://orcid.org/0000-0002-9742-3207

Funding

Korea Institute of Energy Technology Evaluation and Planning 20174030201600National Research Foundation of Korea NRF-2016K1A1A2912829
6 · The paper itself

Abstract

Evolutionary approaches have been providing solutions to various bioengineering challenges in an efficient manner. In addition to traditional adaptive laboratory evolution and directed evolution, recent advances in synthetic biology and fluidic systems have opened a new era of evolutionary engineering. Synthetic genetic circuits have been created to control mutagenesis and enable screening of various phenotypes, particularly metabolite production. Fluidic systems can be used for high-throughput screening and multiplexed continuous cultivation of microorganisms. Moreover, continuous directed evolution has been achieved by combining all the steps of evolutionary engineering. Overall, modern tools and systems for evolutionary engineering can be used to establish the artificial equivalent to natural evolution for various research applications.

Indexed as

BioengineeringDirected Molecular EvolutionHumansPhenotypeContinuous evolutionEvolutionary engineeringHigh-throughput screeningMutagenesisSelection

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.