Evidence map›Paper›PMID 31462231›Full record

ArticleBMC bioinformatics2019

Determination of growth-coupling strategies and their underlying principles.

Tobias B Alter, Birgitta E Ebert

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

22 citing papers in PubMed, 52 citations in OpenAlex.

  1. Article
  2. Article
  3. Balancing Cell Growth and Product Synthesis for Efficient Microbial Cell Factories.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  4. Directed evolution of hydrocarbon-producing enzymes.Biotechnology for biofuels and bioproducts · 2025
    Review
  5. Metabolic growth-coupling strategies forMetabolic engineering communications · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Adaptive laboratory evolution for growth coupled microbial production.World journal of microbiology & biotechnology · 2020
    Review
  19. The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020
    Review
  20. 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 at 2 institutions in 2 countries.

Tobias B AlterInstitute of Applied Microbiology, RWTH Aachen University, Aachen, Germany.
Birgitta E EbertInstitute of Applied Microbiology, RWTH Aachen University, Aachen, Germany. birgitta.ebert@uq.edu.au.ORCID http://orcid.org/0000-0001-9425-7509
RWTH Aachen University · DEThe University of Queensland · AU

Funding

Deutsche Forschungsgemeinschaft EXC 2186
6 · The paper itself

Abstract

backgroundMetabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.

resultsWe adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.

conclusionMaximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

Indexed as

Genome, BacterialMetabolic EngineeringMetabolic Networks and PathwaysModels, BiologicalComputer SimulationEscherichia coliBilevel algorithmsGrowth-coupled productionModel-guided metabolic engineeringOptimality principlesStoichiometric modeling

Identifiers

PMID31462231
PMCPMC6714386
OpenAlexW2970610255

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.