Matej Oresic
VTT
Biotechnology
Tietotie 2
P.O. Box 1500
Espoo,
FIN-02044
Finland
Email:
matej.oresic@vtt.fi
Phone:
+358-9-456-4491
High-throughput integrated quantitative analysis
of biological systems is becoming a desired approach to the study of functional
interactions between genes, proteins, and metabolites. Recent such applications
include studies of metabolic networks in S.
Cerevisiae, energy transduction in Halobacterium
NRC-1, and as a first such applications to the mammalian system, study of
Atherosclerosis using ApoE3-Leiden mice as a disease model.
Analysis and comparison of molecular profile
data across multiple samples has been an active field of recent research, and
several statistical approaches to normalisation were developed enabling
quantitative comparison of expression levels across different samples and
genes. However, applications of techniques such as mass spectrometry and NMR to
measure protein and metabolite levels in complex samples offer new challenges
for data processing, integration, and interpretation. In order to obtain
protein and metabolite levels several processing steps need to be applied, such
as spectral alignment, peak detection, and normalisation.
Unlike
in microarrays where gene identities are largely known, mass spectra require
further steps for identification, such as MS/MS sequencing of tryptic peptides
and subsequent peptide database searches for protein identification. Due to
current instrumental limitations the identification process is very time
consuming and only a fraction of spectral peaks can be associated with
particular peptides or proteins, therefore a variety of computational methods
need to be applied to select the peaks with highest potential relevants to the
biological phenomena studied. As an example, we will present a recent study of
ApoE3-Leiden mouse model of Atherosclerosis. We obtained simultaneous snapshots
of gene expression, protein, and metabolic levels in two groups of mice,
ApoE3-Leiden transgenic mice and an isogenic control group.