CSC

 
 
Tehdyt toimenpiteet

VTT Biotechnology, Espoo
Quantitative Biology and Bioinformatics group

Group leader: Matej Oresic
Home page: http://sysbio.vtt.fi/qbix/


The omics revolution and emergence of systems biology is opening new opportunities to characterize biological systems and ask fundamental biological questions. We are exploring the ways to characterize and model biological systems, as well as apply those ways to and pursue our own interests in integrative physiology as related to metabolic diseases, cellular physiology, and more generally to mechanisms controlling the homeostasis of biological systems.

Our facilities include a comprehensive bioinformatics and chemoinformatics system integrating all major databases, as well as analytical facilities for metabolomics.

Main research activities:

(1) Integrative bioinformatics and conceptual biology

Overload of information and new technologies in life sciences require new informatics solutions to make sense of available data. More fundamentally, our improved ability to quantitatively characterize biological systems is challenging the way we formally describe biological systems and how we design experiments to address biological questions. Our aim is to develop new life science knowledge management solutions that can address the bio- and chemoinformatics challenges of systems biology. We have developed software based on three-tier architecture that enables integration and visualization of complex life science data.

(2) Algorithms and methodologies for metabolomics and proteomics

We are primarily interested in developing new approaches for processing and interpreting of mass spectrometry based metabolomics data. For that purpose we have developed software solutions for differential profiling of LC/MS, compound databases containing spectral and pathway information. On the analytical side, we have been developing a lipidomics platform for global screening of lipids, and developed database solutions for their automated identification.

(3) Lipidomic characterization of animal models in studies of lipotoxicity induced insulin resistance(with Antonio Vidal-Puig, Cambridge University)

We are utilizing our lipidomics platform and bioinformatics approaches to characterize the phenotypes of animal models linked to studies of lipotoxicity induced insulin resistance. Specifically, we aim at identifying key relevant endogenous compounds and pathways, aiming at identifying novel targets for interventions.

(4) DIPP Study: Systems Biology Approach to Biomarker Discovery in Type I Diabetes (with Riitta Lahesmaa and Olli Simell)

The overall objective of this study is to identify novel molecular markers that characterize development of diabetes-associated autoimmunity and progression towards overt clinical type I diabetes (T1D). Discovering genetic susceptibility markers and markers reflecting the disease activity is a specific aim of the project. This will be achieved by exploiting and integrating DNA microarray, proteomics, and metabolomics technologies in the analysis of samples obtained from a carefully selected patient population at defined stages of disease development. The data on gene expression, protein and metabolite profiles will be correlated with the Type 1 Diabetes Prediction and Prevention Project in Finland (DIPP) data on HLA gene alleles, autoantibodies, markers of enterovirus infections (antibodies, enterovirus RNA) and demographic and metabolic features of the study children to identify putative associations between a particular biomolecular profile and these key parameters.

(5) Plant systems biology(with Kirsi-Marja Oksman-Caldentey)

We are utilizing methods of metabolomics and bioinformatics to elucidate the pathways involved in production of plant secondary metabolites.

(6) Yeast systems biology (with Merja Penttilä, Esko Ukkonen, Liisa Holm)

Our interest is to develop and apply advanced experimental and computational tools to understand the cell physiology, using yeast as a model system. Specifically, we aim at developing novel computational methods for top-down cellular modelling. We aim at utilizing our methods in metabolic engineering applications.

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