Quantitative biology and bioinformatics
Competence and challanges
VTT builds new life science and biomedical applications based on the systems approaches. The expertise covers computational biology, biostatistics, and bioinformatics, tightly integrated with molecular profiling such as metabolomics, lipidomics, proteomics, gut microbiota characterization, genomics and transcriptiomics and finally linked to physiology and fluxomics. We work closely with our collaborators and customers to elucidate biological systems and build new applications.
Metabolomics at VTT includes mass spectrometry and NMR-based analytical platforms for studies of primary metabolites as well as of plant or microbial secondary metabolites. Several platforms for studies of lipids have been implemented and successfully applied in e.g. biomarker search and other studies linking molecular biology and physiology. Development of customized metabolomic platforms for specific customer needs is also available.
The overload of information and new technologies in the life sciences is a challenge and requires new informatics and modelling solutions to interpret the available data in the biological and physiological context. Integrative bioinformatics software developed at VTT is based on a three-tier architecture and enables integration, visualization and mining of complex life science data, as well as studies of complex biological networks. The Systems Biology platform at VTT combines bioinformatics with molecular profiling approaches and new measurement technologies, and benefits from in-house knowledge across information technology, biosensors, microbiology, plants, nutrition, and medical domains. It offers solutions to manage, interpret, analyze, and model life science data.
Systems biology applications can be found across a wide range of areas at VTT, including:
Yeast and plant systems biology for advanced metabolic engineering, e.g. biosynthesis
of valuable compounds from Catharanthus roseus cells.
Medical systems biology including nutrition studies aiming at predictive
models, phenotyping, and understanding metabolism, e.g. lipotoxicity
induced insulin resistance
Biomarker discovery, e.g. early
prediction of type 1 diabetes
Management and mining of large scale biomedical data, e.g. development of
database infrastructure for storing and combining genome-wide association
study data with other phenotypic and clinical data across several European
cohorts in the AtheroRemo EU project.
Bioinformatics software development, e.g. MZmine
for metabolomics and proteomics data processing and megNet
for visualisation of complex life science data.
Through the integrative approach combining biological data using the systems biology approach, the extensive amount of data available can be exploited widely for the benefit of our customers.