
Open access
Author
Date
2018Type
- Doctoral Thesis
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000271574Publication status
publishedExternal links
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Publisher
ETH ZurichSubject
Sampling methods; Metabolic Flux Analysis; Isotopomer labeling experiments; MCMC methodsOrganisational unit
03699 - Stelling, Jörg / Stelling, Jörg
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ETH Bibliography
yes
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