Evidence map›Paper›PMID 25379147›Full record

ReviewComputational and structural biotechnology journal2014

A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories.

Amanda K Fisher, Benjamin G Freedman, David R Bevan, Ryan S Senger

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Chemoenzymatic synthesis.Communications chemistry · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Systems metabolic engineering ofJournal of biological engineering · 2019
    Article
  18. Article
  19. Review
  20. 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.

Amanda K FisherDepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, United States ; Department of Biochemistry, Virginia Tech, Blacksburg, VA, United States ; Interdisciplinary Genetics, Bioinformatics, and Computational Biology Program, Virginia Tech, Blacksburg, VA, United States.
Benjamin G FreedmanDepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, United States.
David R BevanDepartment of Biochemistry, Virginia Tech, Blacksburg, VA, United States ; Interdisciplinary Genetics, Bioinformatics, and Computational Biology Program, Virginia Tech, Blacksburg, VA, United States.
Ryan S SengerDepartment of Biological Systems Engineering, Virginia Tech, Blacksburg, VA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.

Indexed as

DockingEnzyme engineeringGenome-scale modelMetabolic engineeringMicrobial cell factoryMolecular dynamics

Identifiers

PMID25379147
PMCPMC4212277

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.