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dc.contributor.author
Liphardt, Thomas
dc.contributor.supervisor
Stelling, Jörg
dc.contributor.supervisor
Sauer, Uwe
dc.contributor.supervisor
Noeh, Katharina
dc.date.accessioned
2018-06-22T10:34:22Z
dc.date.available
2018-06-22T10:14:19Z
dc.date.available
2018-06-22T10:34:22Z
dc.date.issued
2018
dc.identifier.uri
http://hdl.handle.net/20.500.11850/271574
dc.identifier.doi
10.3929/ethz-b-000271574
dc.description.abstract
The aim of metabolic flux analysis is to determine the rates at which the processes in metabolism take place. Stationary isotopomer labeling experiments are the state-of-the-art method to generate data for metabolic flux analysis. The analysis of such experiments requires an atom transition model which is able to simulate the carbon atom transitions that take place in metabolism. The operational state of metabolism is represented by the rates at which the considered processes take place. We call this operational state the flux distribution, and it is a parameter of the atom transition model. By comparing the results of the model simulation against experimental data, we gain information about the flux distribution. To increase the identifiability of this inverse problem, we use constraint-based modeling, i.e. we restrict the flux distribution by applying linear constraints that can be derived directly from the stoichiometry of the considered processes. We took a probabilistic view on this inverse problem. We developed computational methods for the complete computational pipeline which is required to carry out metabolic flux analysis based on stationary isotopomer labeling experiments. First, we developed methods for the parametrization of the solution space that arises from constraint-based modeling. We then implemented the software necessary to simulate and evaluate data from labeling experiments. We next formulated the probabilistic framework which describes labeling experiments. The key to carrying out this probabilistic analysis was the development of efficient sampling methods that are able to sample from polytope-supported probability distributions in high dimensions. We first improved the efficiency of existing MCMC methods for sampling uniformly from convex polytopes. We then developed an efficient sampling procedure for the sampling of general convex polytopes-supported probability distribution based on nested sampling. We analyzed datasets from labeling experiments and compared different methods for the computation of confidence intervals for the estimated fluxes. We further generated synthetic data representing simulated labeling experiments, outlining new ways of experimental design.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Sampling methods
en_US
dc.subject
Metabolic Flux Analysis
en_US
dc.subject
Isotopomer labeling experiments
en_US
dc.subject
MCMC methods
en_US
dc.title
Efficient computational methods for sampling-based metabolic flux analysis
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2018-06-22
ethz.size
183 p.
en_US
ethz.code.ddc
DDC - DDC::5 - Science::570 - Life sciences
ethz.identifier.diss
24756
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03699 - Stelling, Jörg / Stelling, Jörg
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::03699 - Stelling, Jörg / Stelling, Jörg
en_US
ethz.tag
Metabolic flux analysis
en_US
ethz.date.deposited
2018-06-22T10:14:21Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-06-22T10:34:49Z
ethz.rosetta.lastUpdated
2021-02-15T00:27:25Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Efficient%20computational%20methods%20for%20sampling-based%20metabolic%20flux%20analysis&rft.date=2018&rft.au=Liphardt,%20Thomas&rft.genre=unknown&rft.btitle=Efficient%20computational%20methods%20for%20sampling-based%20metabolic%20flux%20analysis
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