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@PHDTHESIS{Nieer:1004881,
author = {Nießer, Jochen},
othercontributors = {Wiechert, Wolfgang and Blank, Lars M.},
title = {{A}utomation, miniaturization, and parallelization of
isotopic labeling experiments for the advanced analysis of
microbial systems},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-01685},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2025},
abstract = {The generation and optimization of bioprocesses and strains
for industrial application as well as the investigation of
fundamental biological research hypotheses require adequate
phenotyping experiments. Generally, there is a trade-off
between informativeness and experimental throughput which
became ever more relevant as the creation of genetic
diversity and cultivation of mutant strain variants was
increasingly accelerated. Isotopic labeling experiments are
located at the extreme of high informativeness and low
throughput with the additional limitation of significant
associated costs per experiment. Commonly, they are
conducted in lab-scale bioreactors, shakingflasks, and as
the result of recent advances in mini-bioreactors at a scale
ranging from liters to milliliters. In the present
dissertation, an automated, miniaturized, and parallelized
experimental setup taking advantage of modern liquid
handling robots and microbioreactors is established and
validated. The development of an automated quenching method
for this workflow enables the analysis of labeling patterns
from free amino acids and intermediates of the central
carbon metabolism, even at a microliter scale. It is then
embedded into an overarching integrated pipeline for
isotopic labeling experiments and applied to biological case
studies. In order to realize such a pipeline, multiple
Python programs are constructed and most notably the open
source package PeakPerformance using an innovative peak
fitting approach by Bayesian inference is developed and
utilized for the evaluation of chromatographic peak data.
For the first application study, a novel bioprocess
modelling approach for estimating intracellular metabolite
pool sizes based on 13C-labeling data is developed and
demonstrated in Corynebacterium glutamicum. Thereby, the
pool sizes of multiple amino acids the synthesis pathways of
which are branching from the glycolysis were identified with
a relatively high certainty. For the second study, the first
ever automated isotopically non-stationary 13C-metabolic
flux analysis is conducted at an unprecedented microliter
scale to elucidate the fluxome of the evolved strain C.
glutamicum $WT_EtOH-Evo$ grown on ethanol as the sole carbon
source. Since no fluxome of C. glutamicum grown exclusively
on ethanol had been published prior, new insight regarding
the pertaining pathway usage was generated, in particular an
increased glyoxylate shunt activity compared to other
substrates entering the central carbon metabolism via
acetyl-CoA.},
cin = {420410 / 161710 / 160000 / 057700},
ddc = {570},
cid = {$I:(DE-82)420410_20140620$ / $I:(DE-82)161710_20140620$ /
$I:(DE-82)160000_20140620$ / $I:(DE-82)057700_20231115$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-01685},
url = {https://publications.rwth-aachen.de/record/1004881},
}
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