Evidence map›Paper›PMID 29286407›Full record

ArticleJournal of visualized experiments : JoVE2017

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology.

Johannes Hemmerich, Lars Freier, Wolfgang Wiechert, Eric von Lieres, Marco Oldiges

Open access · greenAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 39% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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 at 1 institution in 1 country.

Johannes HemmerichIBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC).
Lars FreierIBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC).
Wolfgang WiechertIBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC); Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University.
Eric von LieresIBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC); e.von.lieres@fz-juelich.de.
Marco OldigesIBG-1: Biotechnology, Forschungszentrum Jülich; Research Center Jülich, Bioeconomy Science Center (BioSC); Institute for Biotechnology, RWTH Aachen University; m.oldiges@fz-juelich.de.
Forschungszentrum Jülich · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A core business in industrial biotechnology using microbial production cell factories is the iterative process of strain engineering and optimization of bioprocess conditions. One important aspect is the improvement of cultivation medium to provide an optimal environment for microbial formation of the product of interest. It is well accepted that the media composition can dramatically influence overall bioprocess performance. Nutrition medium optimization is known to improve recombinant protein production with microbial systems and thus, this is a rewarding step in bioprocess development. However, very often standard media recipes are taken from literature, since tailor-made design of the cultivation medium is a tedious task that demands microbioreactor technology for sufficient cultivation throughput, fast product analytics, as well as support by lab robotics to enable reliability in liquid handling steps. Furthermore, advanced mathematical methods are required for rationally analyzing measurement data and efficiently designing parallel experiments such as to achieve optimal information content. The generic nature of the presented protocol allows for easy adaption to different lab equipment, other expression hosts, and target proteins of interest, as well as further bioprocess parameters. Moreover, other optimization objectives like protein production rate, specific yield, or product quality can be chosen to fit the scope of other optimization studies. The applied Kriging Toolbox (KriKit) is a general tool for Design of Experiments (DOE) that contributes to improved holistic bioprocess optimization. It also supports multi-objective optimization which can be important in optimizing both upstream and downstream processes.

Indexed as

BioreactorsBiotechnologyRecombinant ProteinsReproducibility of ResultsRecombinant Proteins

Identifiers

PMID29286407
PMCPMC5755569
OpenAlexW2773344717

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.