Abstract
Post-genomic molecular biology embodies high-throughput
experimental tech-niques and hence is a data-rich field.
The goal of this thesis is to develop bioin-formatics
methods to utilise publicly available data in order to
produce knowl-edge and to aid mining of newly generated
data. As an example of knowledge or hypothesis
generation, consider function prediction of biological
molecules. Assignment of protein function is a
non-trivial task owing to the fact that the same protein
may be involved in different biological processes,
depending on the state of the biological system and
protein localisation. The function of a gene or a gene
product may be provided as a textual description in a
gene or protein annotation database. Such textual
descriptions lack in providing the contextual meaning of
the gene function. Therefore, we need ways to represent
the meaning in a formal way. Here we apply data
integration approach to provide rich repre-sentation that
enables context-sensitive mining of biological data in
terms of integrated networks and conceptual spaces.
Context-sensitive gene function an-notation follows
naturally from this framework, as a particular
application. Next, knowledge that is already publicly
available can be used to aid mining of new experimental
data. We developed an integrative bioinformatics method
that util-ises publicly available knowledge of
protein-protein interactions, metabolic net-works and
transcriptional regulatory networks to analyse
transcriptomics data and predict altered biological
processes. We applied this method to a study of dynamic
response of Saccharomyces cerevisiae to oxidative stress.
The applica-tion of our method revealed dynamically
altered biological functions in response to oxidative
stress, which were validated by comprehensive in vivo
metabolom-ics experiments. The results provided in this
thesis indicate that integration of heterogeneous
biological data facilitates advanced mining of the data.
The meth-ods can be applied for gaining insight into
functions of genes, gene products and other molecules, as
well as for offering functional interpretation to
transcriptom-ics and metabolomics experiments.
Original language | English |
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Qualification | Doctor Degree |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 14 May 2010 |
Place of Publication | Espoo |
Publisher | |
Print ISBNs | 978-951-38-7385-1 |
Electronic ISBNs | 978-951-38-7386-8 |
Publication status | Published - 2010 |
MoE publication type | G5 Doctoral dissertation (article) |
Keywords
- systems biology
- high-throughput data
- data integration
- data mining
- visualisation
- bioinformatics
- conceptual spaces
- network topology