Evidence map›Paper›PMID 32076772›Full record

ReviewApplied microbiology and biotechnology2020

Stepwise optimization of recombinant protein production in Escherichia coli utilizing computational and experimental approaches.

Kulandai Arockia Rajesh Packiam, Ramakrishnan Nagasundara Ramanan, Chien Wei Ooi, Lakshminarasimhan Krishnaswamy, Beng Ti Tey

Abstract readReview
PubMed Publisher
In one paragraph

Review in Applied microbiology and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  18. Expression of full and fragment-B of diphtheria toxin genes inClinical and experimental vaccine research · 2022
    Article
  19. Recombinant Protein Production and Purification of Insoluble Proteins.Methods in molecular biology (Clifton, N.J.) · 2022
    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

5 authors.

Kulandai Arockia Rajesh PackiamChemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Malaysia.ORCID https://orcid.org/0000-0001-7812-1804
Ramakrishnan Nagasundara RamananChemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Malaysia. ramanan@monash.edu.ORCID https://orcid.org/0000-0003-3819-2813
Chien Wei OoiChemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Malaysia.ORCID https://orcid.org/0000-0003-2656-6398
Lakshminarasimhan KrishnaswamyScience and Math Cluster, Singapore University of Technology and Design (SUTD), Singapore, 487372, Singapore.
Beng Ti TeyChemical Engineering Discipline, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Malaysia.ORCID https://orcid.org/0000-0002-6453-5763

Funding

Fundamental Research Grant Scheme (FRGS) FRGS/1/2016/TK02/MUSM/02/3
6 · The paper itself

Abstract

Over the past few decades, Escherichia coli (E. coli) remains the most favorable host among the microbial cell factories for the production of soluble recombinant proteins. Recombinant protein production (RPP) via E. coli is optimized at the level of gene expression (expression level) and the process condition of fermentation (process level). Presently, the reported studies do not give a clear view on the selection of methods employed in the optimization of RPP. Here, we have reviewed various optimization methods and their preferences with respect to the factors at expression and process levels to achieve the optimal levels of soluble RPP. With a greater understanding of these optimization methods, we proposed a stepwise methodology linking the factors from both levels for optimizing the production of soluble recombinant protein in E. coli. The proposed methodology is further explained through five sets of examples demonstrating the optimization of RPP at both expression and process levels.Key Points• Stepwise methodology of optimizing recombinant protein production is proposed.• In silico tools can facilitate the optimization of gene- and protein-based factors.• Optimization of gene- and protein-based factors aids host-vector selection.• Statistical optimization is preferred for achieving optimal levels of process factors.

Indexed as

FermentationComputational BiologyComputer SimulationCulture MediaEscherichia coliGene ExpressionRecombinant ProteinsCulture MediaRecombinant ProteinsDesign of experimentsOptimizationResponse surface methodologySoluble recombinant proteinSupport vector machine

Identifiers

PMID32076772

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.