Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce
Long-Term Adaptation of E-coli Cultures in Large-Scale Bioreactors:
Experimental Evidence and Mathematical Model
Rapidly changing concentrations of substrates frequently occur during
large-scale microbial cultivations. These changing conditions, caused by
large mixing times, result in a heterogeneous population distribution.
Here, we present a powerful and efficient modeling approach to predict
the influence of varying substrate levels on the transcriptional and
translational response of the cell. This approach consists of two parts,
a single-cell model to describe transcription and translation for an
exemplary operon (trp operon) and a second part to characterize cell
distribution during the experimental setup. Combination of both models
enables prediction of transcriptional patterns for the whole population.
In summary, the resulting model is not only able to anticipate the
experimentally observed short-term and long-term transcriptional
response, it further allows envision of altered protein levels. Our
model shows that locally induced stress responses propagate throughout
the bioreactor, resulting in temporal, and spatial population
heterogeneity. Stress induced transcriptional response leads to a new
population steady-state shortly after imposing fluctuating substrate
conditions. In contrast, the protein levels take more than 10 h to
achieve steady-state conditions.
%0 Journal Article
%1 ISI:000404329200005
%A Niess, Alexander
%A Loeffler, Michael
%A Simen, Joana D.
%A Takors, Ralf
%C PO BOX 110, EPFL INNOVATION PARK, BUILDING I, LAUSANNE, 1015, SWITZERLAND
%D 2017
%I FRONTIERS MEDIA SA
%J FRONTIERS IN MICROBIOLOGY
%K myown
%R 10.3389/fmicb.2017.01195
%T Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce
Long-Term Adaptation of E-coli Cultures in Large-Scale Bioreactors:
Experimental Evidence and Mathematical Model
%U https://doi.org/10.3389/fmicb.2017.01195
%V 8
%X Rapidly changing concentrations of substrates frequently occur during
large-scale microbial cultivations. These changing conditions, caused by
large mixing times, result in a heterogeneous population distribution.
Here, we present a powerful and efficient modeling approach to predict
the influence of varying substrate levels on the transcriptional and
translational response of the cell. This approach consists of two parts,
a single-cell model to describe transcription and translation for an
exemplary operon (trp operon) and a second part to characterize cell
distribution during the experimental setup. Combination of both models
enables prediction of transcriptional patterns for the whole population.
In summary, the resulting model is not only able to anticipate the
experimentally observed short-term and long-term transcriptional
response, it further allows envision of altered protein levels. Our
model shows that locally induced stress responses propagate throughout
the bioreactor, resulting in temporal, and spatial population
heterogeneity. Stress induced transcriptional response leads to a new
population steady-state shortly after imposing fluctuating substrate
conditions. In contrast, the protein levels take more than 10 h to
achieve steady-state conditions.
@article{ISI:000404329200005,
abstract = {{Rapidly changing concentrations of substrates frequently occur during
large-scale microbial cultivations. These changing conditions, caused by
large mixing times, result in a heterogeneous population distribution.
Here, we present a powerful and efficient modeling approach to predict
the influence of varying substrate levels on the transcriptional and
translational response of the cell. This approach consists of two parts,
a single-cell model to describe transcription and translation for an
exemplary operon (trp operon) and a second part to characterize cell
distribution during the experimental setup. Combination of both models
enables prediction of transcriptional patterns for the whole population.
In summary, the resulting model is not only able to anticipate the
experimentally observed short-term and long-term transcriptional
response, it further allows envision of altered protein levels. Our
model shows that locally induced stress responses propagate throughout
the bioreactor, resulting in temporal, and spatial population
heterogeneity. Stress induced transcriptional response leads to a new
population steady-state shortly after imposing fluctuating substrate
conditions. In contrast, the protein levels take more than 10 h to
achieve steady-state conditions.}},
added-at = {2018-06-08T11:32:35.000+0200},
address = {{PO BOX 110, EPFL INNOVATION PARK, BUILDING I, LAUSANNE, 1015, SWITZERLAND}},
affiliation = {{Takors, R (Reprint Author), Univ Stuttgart, Inst Biochem Engn, Stuttgart, Germany.
Niess, Alexander; Loeffler, Michael; Simen, Joana D.; Takors, Ralf, Univ Stuttgart, Inst Biochem Engn, Stuttgart, Germany.}},
article-number = {{1195}},
author = {Niess, Alexander and Loeffler, Michael and Simen, Joana D. and Takors, Ralf},
author-email = {{takors@ibvt.uni-stuttgart.de}},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ddaafca0c1171d0cf4566280e0615fe5/ralftakors},
da = {{2018-01-26}},
doc-delivery-number = {{EY9OZ}},
doi = {{10.3389/fmicb.2017.01195}},
funding-acknowledgement = {{Bundesministerium fur Bildung and Forschung (BMBF) {[}FKZ0316178A]}},
funding-text = {{The authors gratefully acknowledge the funding by the Bundesministerium
fur Bildung and Forschung (BMBF, Grant FKZ0316178A).}},
interhash = {21bccd236a178c1f9d328e12ede7d497},
intrahash = {ddaafca0c1171d0cf4566280e0615fe5},
issn = {{1664-302X}},
journal = {{FRONTIERS IN MICROBIOLOGY}},
journal-iso = {{Front. Microbiol.}},
keywords = {myown},
keywords-plus = {{SUBSTRATE OSCILLATIONS; CELL; DEGRADATION; EXPRESSION; GROWTH; OPERON;
RNA; TRP}},
language = {{English}},
month = {{JUN 28}},
number-of-cited-references = {{28}},
publisher = {{FRONTIERS MEDIA SA}},
research-areas = {{Microbiology}},
times-cited = {{1}},
timestamp = {2018-06-08T09:32:35.000+0200},
title = {{Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce
Long-Term Adaptation of E-coli Cultures in Large-Scale Bioreactors:
Experimental Evidence and Mathematical Model}},
type = {{Article}},
unique-id = {{ISI:000404329200005}},
url = {https://doi.org/10.3389/fmicb.2017.01195},
usage-count-last-180-days = {{0}},
usage-count-since-2013 = {{0}},
volume = {{8}},
web-of-science-categories = {{Microbiology}},
year = {{2017}}
}