Evidence map›Paper›PMID 30736778›Full record

ReviewMicrobial cell factories2019

Engineering microbial consortia by division of labor.

Garrett W Roell, Jian Zha, Rhiannon R Carr, Mattheos A Koffas, Stephen S Fong, Yinjie J Tang

Abstract readReview
In one paragraph

Review in Microbial cell factories, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.

0numbers the graph read from it
0cells of the map it votes in
96citing 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

96 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article

36 more citing papers are in PubMed but not listed here.

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.

Garrett W RoellDepartment of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, MO, 63130, USA.
Jian ZhaDepartment of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY, 12180, USA.
Rhiannon R CarrDepartment of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, MO, 63130, USA.
Mattheos A KoffasDepartment of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY, 12180, USA.
Stephen S FongDepartment of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, 23284, USA.
Yinjie J TangDepartment of Energy, Environmental and Chemical Engineering, Washington University, Saint Louis, MO, 63130, USA. yinjie.tang@seas.wustl.edu.ORCID http://orcid.org/0000-0002-5112-0649

Funding

Biological and Environmental Research DESC0018324Division of Molecular and Cellular Biosciences (US) MCB 1616619
6 · The paper itself

Abstract

During microbial applications, metabolic burdens can lead to a significant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and

Indexed as

Metabolic Networks and PathwaysMicrobial ConsortiaFermentationIndustrial MicrobiologyMetabolic EngineeringMetabolic Flux AnalysisSynthetic Biology13C-metabolic flux analysisCross-feedingMetabolite channelingReporter proteinSubpopulations

Identifiers

PMID30736778
PMCPMC6368712

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.