Research projects
Bioinformatics and Statistical mining of biological data group
Learning methods for bioinformatics
Period: 1/2005-12/2008
Researchers: Abhishek Tripathi, Jaakko Peltonen, Antti Ajanki, Samuel Kaski
Funding: University of Helsinki Research Funds
The project develops methods for combining different biological measurement data and for using background information to analyse new measurement data. In many areas of biological research, it is only possible to make measurements of small amounts of samples, but with the help of microarraytechniques, hundreds of thousands of values can be extracted from one sample. The analysis of this kind of data is challenging and requires in-depth knowledge of biology. The project develops new computational methods for using pre-existing measurements in the analysis, or for creating new background data efficiently and automatically from large collections of measurements. So far, the project has developed solutions for three areas of this problem field. A rapid linear pre-processing method has been developed for data fusion to retain information shared by different materials, and to discard material-specific information. We have also developed a rapid method for searching for components describing classification (discriminative components). Furthermore, we have developed a method based on Bayes networks for analysis of gene-regulatory networks; it is used to discover changes in situational regulation interaction.
Experimental and computational analysis of physiological regulation at transcriptome, proteome and metabolome level (SYSFYS)
Period: 1/2004-12/2007
Researchers: Juho Rousu, Esko Ukkonen, Ari Rantanen, Paula Jouhten, Esa Pitkänen
Funding: Academy of Finland
The Department of Computer Science at the Univversity of Helsinki, the Institute of Biotechnology and VTT cooperate to form the SYSFYS research consortium with the aim of developing and implementing advanced experimental and computational methods for the analysis of metabolic fluxes in cells. Cooperation with the Uwe Sauer (ETH Zürich) laboratory started in 2007 to refine the flux-estimation methods developed by the CS department into practical tools for research into metabolic fluxes. The project carried out further development of methods for computing atomic descriptions of enzymatic reactions as well as for predicting the fragmentation of molecules in tandem mass spectrometers.
Modelling functional shifts in enzyme evolution (UR-ENZYMES)
Period: 1/2006-12/2008
Researchers: Juho Rousu, Katja Astikainen, Esa Pitkänen, Liisa Holm (Biotekniikan instituutti)
Funding: Academy of Finland
UR-ENZYMES is a multi-disciplinary project that combines machine learning with genomics in order to explain molecule evolution. The project creates new algorithms for mining genomic data, for comparative genomics and for reconstruction of metabolic fluxes. The project core consists of the presentation of enzymatic reactions in such a form that the shifts of enzyme-gene functions during biological evolution can be traced. The project group at the department has focused on developing descriptions of enzyme sequences and chemical reactions as well as developing machine-learning methods that utilise them.