The research: Bioinformatics

One of the main focuses of our Computational Biology group led by Tiuryn is modeling and discovery of regulatory elements within the non-coding parts of genomes. Sequencing of multiple genomes revealed that most of the sequence does not encode proteins, but rather plays a regulatory role in the life of cells. From the computational perspective, the key two questions are: (1) discerning the regulatory regions from other non-coding DNA sequences, and (2) deciphering the regulatory functions of these regions.

While these tasks have been usually separated, it is planned to approach them simultaneously by combining mathematical models of the DNA evolution and computational approaches to finding regulatory elements in different species. With accumulated experience in creating models based solely on DNA sequence information (works by Dojer, Tiuryn, and Wilczynski), it is now possible to incorporate other types of data, such as Transcription Factor binding (works by Wilczynski in collaboration with Furlong from Heidelberg), chromatin epigenetic state, or cell-type specific information on chromatin openness (work by Jankowski and Tiuryn in collaboration with Prabhakar from Singapore) to complete differentiation. Using such heterogenic datasets, one should be able to understand better the non-additive, cooperative effects in Transcription Factor binding underlying function of regulatory elements. Integrative approaches, like the one we are proposing, form now the cutting edge of modern research in Molecular Cell Biology.
Having both expertise in computational methods and experience in collaborating with biologists in Max Planck Institute for Molecular Genetics in Berlin, European Molecular Biology Laboratory in Heidelberg, National University of Singapore, Nencki Institute and IBB PAS Warsaw, our group should be able to make important contributions to the field as a whole.

The second goal, addressed by Gambin and her collaborators belongs to integrative systems biology: inferring information from massive heterogeneous data. The project aims to interrelate heterogeneous and often noisy data by relying on statistical and computational methods. This integrated system-wide approach will be applied to study the role of proteolysis in cancer development and progression. Another project concerns stress-induced transposons activity, aiming at a simple mathematical model describing the dynamics of mobile genetic elements, in conditions of environmental stress experienced by host organisms.