The technological advances of the last decade have led to numerous studies focusing on genome-wide transcriptomic changes. This is also reflected in a great number of publications that compare the gene expression levels of healthy and other samples. However, the detailed molecular mechanisms that cause these changes remain largely unknown. The different types of cancer are popular examples, for which altered transcriptomic expression profiles are known for a long time, and researchers nowadays focus on identifying causes and treatments against these alterations. Epigenetic effects, such as DNA methylation, have been identified as associated to most types of cancer. The integration of both omics layers---transcriptomic and epigenomic---allowed for explaining varied gene expression levels for some genes. Despite these promising results, automated methods for an integration of multiple omics datasets are still in an early stage of development. More omics layers need to be analyzed integratively and more approaches need to be developed that help researchers finding causes for varied expression levels. For this purpose, systems biology methods are required that interpret biological coherences as an overall system and help to identify complex interactions. However, the biochemical networks that underly those systems must be reconstructed before most systems biology methods can be applied or interactions can be identified. Thus, approaches for the reconstruction of biochemical networks and novel methods, which help identifying complex coherences between different omics layers are firstly described in this thesis. It thereby focuses on the inference of biochemical networks and their subsequent integration with multilayered omics data. A widely-used source for biochemical networks are biological pathways. Unfortunately, they are mostly stored as inaccurate and incomplete qualitative descriptions in proprietary formats and thus, cannot be used directly with systems biology methods. This thesis presents a method that automatically fixes those issues and translates pathways to well-defined computational models. As a result of this work, 142,050 revised models of metabolic and non-metabolic pathways have been generated and put into the popular BioModels repository for the systems biology modeling language (SBML). The ModuleMaster method is a second approach for the generation of biochemical networks. More specifically, transcriptional regulatory networks are inferred from combinations of experimental gene expression data and a priori knowledge. To this end, gene expression data is clustered and regulatory transcription factors are determined. The inferred interactions between both span the final network. The established biochemical networks are used for a joint visualization of multilayered omics data. For this purpose, it is discussed how different omics layers can be integrated into a single dataset. On this basis, a methodology has been developed that integratively depicts messenger RNA, microRNA, DNA methylation and protein data in a single network. Besides this integration technique, several further integrated omics data analysis methods are presented in this thesis. The concept of gene-set enrichment analysis is extended to multilayered omics data and methods that ease a joint inspection of these high-dimensional datasets are presented. Finally, a machine learning approach is described that links epigenetic profiles to genetic features. This is used to discover the mechanisms and features that induce changes in the epigenome.
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@phdthesis{Wrzodek2013PhD,
author = {Wrzodek, Clemens},
title = {Inference and integration of biochemical networks with multilayered
omics data},
school = {University of Tuebingen},
year = {2013},
address = {T\"ubingen, Germany},
month = jun,
abstract = { The technological advances of the last decade have led to numerous
studies focusing on genome-wide transcriptomic changes. This is also
reflected in a great number of publications that compare the gene
expression levels of healthy and other samples. However, the detailed
molecular mechanisms that cause these changes remain largely unknown.
The different types of cancer are popular examples, for which altered
transcriptomic expression profiles are known for a long time, and
researchers nowadays focus on identifying causes and treatments against
these alterations. Epigenetic effects, such as DNA methylation, have
been identified as associated to most types of cancer. The integration
of both omics layers---transcriptomic and epigenomic---allowed for
explaining varied gene expression levels for some genes. Despite
these promising results, automated methods for an integration of
multiple omics datasets are still in an early stage of development.
More omics layers need to be analyzed integratively and more approaches
need to be developed that help researchers finding causes for varied
expression levels. For this purpose, systems biology methods are
required that interpret biological coherences as an overall system
and help to identify complex interactions. However, the biochemical
networks that underly those systems must be reconstructed before
most systems biology methods can be applied or interactions can be
identified. Thus, approaches for the reconstruction of biochemical
networks and novel methods, which help identifying complex coherences
between different omics layers are firstly described in this thesis.
It thereby focuses on the inference of biochemical networks and their
subsequent integration with multilayered omics data. A widely-used
source for biochemical networks are biological pathways. Unfortunately,
they are mostly stored as inaccurate and incomplete qualitative descriptions
in proprietary formats and thus, cannot be used directly with systems
biology methods. This thesis presents a method that automatically
fixes those issues and translates pathways to well-defined computational
models. As a result of this work, 142,050 revised models of metabolic
and non-metabolic pathways have been generated and put into the popular
BioModels repository for the systems biology modeling language (SBML).
The ModuleMaster method is a second approach for the generation of
biochemical networks. More specifically, transcriptional regulatory
networks are inferred from combinations of experimental gene expression
data and a priori knowledge. To this end, gene expression data is
clustered and regulatory transcription factors are determined. The
inferred interactions between both span the final network. The established
biochemical networks are used for a joint visualization of multilayered
omics data. For this purpose, it is discussed how different omics
layers can be integrated into a single dataset. On this basis, a
methodology has been developed that integratively depicts messenger
RNA, microRNA, DNA methylation and protein data in a single network.
Besides this integration technique, several further integrated omics
data analysis methods are presented in this thesis. The concept of
gene-set enrichment analysis is extended to multilayered omics data
and methods that ease a joint inspection of these high-dimensional
datasets are presented. Finally, a machine learning approach is described
that links epigenetic profiles to genetic features. This is used
to discover the mechanisms and features that induce changes in the
epigenome.},
isbn = {978-3-8439-1116-0},
keywords = {Integrator, InCroMAP, integration of omics data, machine learning,
omics data, JSBML, MARCAR, metabolic modeling, gene-regulatory network,
ModuleMaster, path2models, SBML, BioPAX, KEGG, KEGGtranslator, CpG
island},
InCroMAP, HepatoSys, MARCAR, NGFN-II, Spher4Sys},
publisher = {Verlag Dr.~Hut, Sternstra{\ss}e 18, M\"unchen},
url = {http://www.dr.hut-verlag.de/978-3-8439-1116-0.html}
}