Evidence map›Paper›PMID 31010155›Full record

ArticleMicroorganisms2019

Reproduction of Large-Scale Bioreactor Conditions on Microfluidic Chips.

Phuong Ho, Christoph Westerwalbesloh, Eugen Kaganovitch, Alexander Grünberger, Peter Neubauer, Dietrich Kohlheyer, Eric von Lieres

Abstract read
In one paragraph

Article in Microorganisms, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Scale-down bioreactors-comparative analysis of configurations.Bioprocess and biosystems engineering · 2025
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Kinetic Modeling ofMetabolites · 2022
    Review
  7. Automated Conditional Screening of MultipleBioengineering (Basel, Switzerland) · 2020
    Article
  8. Article
  9. Review
  10. 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

7 authors.

Phuong HoInstitute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany. p.ho@fz-juelich.de.ORCID 0000-0002-8655-595X
Christoph WesterwalbeslohInstitute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany. c.westerwalbesloh@fz-juelich.de.ORCID 0000-0002-3064-7425
Eugen KaganovitchInstitute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany. e.kaganovitch@fz-juelich.de.ORCID 0000-0001-5932-4657
Alexander GrünbergerInstitute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany. alexander.gruenberger@uni-bielefeld.de.ORCID 0000-0002-7564-4957
Peter NeubauerBioprocess Engineering, Department of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany. peter.neubauer@tu-berlin.de.ORCID 0000-0002-1214-9713
Dietrich KohlheyerInstitute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany. d.kohlheyer@fz-juelich.de.ORCID 0000-0001-6215-1857
Eric von LieresInstitute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52425 Jülich, Germany. e.von.lieres@fz-juelich.de.ORCID 0000-0002-0309-8408

Funding

Bundesministerium für Bildung und Forschung 031A302CBundesministerium für Bildung und Forschung FKZ031A095AHelmholtz-Gemeinschaft PD-311Helmholtz-Gemeinschaft VH-NG-1029
6 · The paper itself

Abstract

Microbial cells in industrial large-scale bioreactors are exposed to fluctuating conditions, e.g., nutrient concentration, dissolved oxygen, temperature, and pH. These inhomogeneities can influence the cell physiology and metabolism, e.g., decelerate cell growth and product formation. Microfluidic systems offer new opportunities to study such effects in great detail by examining responses to varying environmental conditions at single-cell level. However, the possibility to reproduce large-scale bioreactor conditions in microscale cultivation systems has not yet been systematically investigated. Hence, we apply computational fluid dynamics (CFD) simulations to analyze and compare three commonly used microfluidic single-cell trapping and cultivation devices that are based on (i) mother machines (MM), (ii) monolayer growth chambers (MGC), and (iii) negative dielectrophoresis (nDEP). Several representative time-variant nutrient concentration profiles are applied at the chip entry. Responses to these input signals within the studied microfluidic devices are comparatively evaluated at the positions of the cultivated cells. The results are comprehensively presented in a Bode diagram that illustrates the degree of signal damping depending on the frequency of change in the inlet concentration. As a key finding, the MM can accurately reproduce signal changes that occur within 1 s or slower, which are typical for the environmental conditions observed by single cells in large-scale bioreactors, while faster changes are levelled out. In contrast, the nDEP and MGC are found to level out signal changes occurring within 10 s or faster, which can be critical for the proposed application.

Indexed as

computational fluid dynamicsfrequency responselife linemicrofluidicsmodellingmonolayer growth chambermother machinenegative dielectrophoresissimulationsingle-cell analysis

Identifiers

PMID31010155
PMCPMC6518007

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.