to the Quantitative Biology and Bioinformatics group page. We are an interdisciplinary team with competencies in metabolomics, computational systems biology, bioinformatics and advanced analytical technologies. We are part of the Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research (2012-2017), which is directed by Prof. Matej Orešič.
Our group aims to achieve a system-level understanding of metabolism and to translate this knowledge into novel solutions to benefit human health.
The motivation for studying metabolism is multi-fold. Due to its central life-sustaining function metabolism is tightly homeostatically regulated. Accordingly, its understanding may shed light on complex multi-level interactions within biological systems and with the environment. Furthermore, the biochemical networks underlying metabolism are the best characterized of any biological network. The study of metabolism using emerging analytical and computational tools of metabolomics may thus provide an opportunity for their quantitative analysis. From the translational research perspective, derangements of metabolism play important roles in the pathogenesis of most common diseases. These derangements may also occur as co-morbidities underlying multiple apparently unrelated diseases. System-level study of metabolism may thus identify common and specific pathways and vulnerabilities underlying the pathogenesis of many diseases.
In our research we found that altered metabolic phenotypes are not always directly associated with the progression to the disease, but may also indicate an activation of the adaptive mechanism or the existence of a risk metabolic phenotype necessary for the initiation of the disease process. Furthermore, our studies suggest that the acquisition of a specific risk phenotype may lead to global changes in metabolic network properties leading to diminished capacity to adaptively regulate metabolism.
We rely on systems medicine approach, where instead of focusing on each disease individually, the aim is to account for the complex gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. We are particularly interested in the identification of disease vulnerabilities associated with different metabolic phenotypes and the underlying mechanisms linking these vulnerabilities with the development of specific disorders or their co-morbidities. Such in depth understanding of the metabolic phenotypes in health and disease is crucial if one is to implement personalized medicine. In addition to new knowledge on the disease etiology and pathogenesis, our studies may discover novel biomarkers for early disease detection as well as identify novel avenues for disease prevention or therapy.
"If I were doing a PhD, I’d be doing it in metabolomics" - James Watson (2013)
Join us at 2015 Keystone Symposium on Systems Biology of Lipid Metabolism (February 9-13, 2015; Beaver Run Resort, Breckenridge, CO, USA), organized by Matej Orešič, Antonio Vidal-Puig, and Ana Maria Cuervo.
This sort of metabolomic approach to T1D natural history may be a pioneering example of environmental data-driven approaches. From commentary on our paper Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes, J. Exp. Med. 205, 2975 (2008).