Studia doktoranckie "Life Sciences for Biomedical Research"

Wydział Biologii Uniwersytetu Warszawskiego ogłasza rekrutację na anglojęzyczne studia III stopnia "Life Sciences for Biomedical Research", prowadzone przez Wydział Biologii oraz partnerskie Wydziały Uniwersytetu, w tym Wydział Matematyki, Informatyki i Mechaniki, oraz Instytuty PAN. Termin zgłoszeń upływa 20 marca.

W ramach tych studiów dwa projekty prac doktorskich (opisane poniżej) będą realizowane przez Wydział MIM. Rekrutację na studia prowadzi Wydział Biologii.

Więcej informacji: http://www.biol.uw.edu.pl/pl/aktualnosci/54-aktualnosci/1250-rekrutacja-na-studia-doktoranckie-life-sciences-for-biomedical-research.

Efficient algorithms and statistical models for mass spectra processing

Anna Gambin (aniag@mimuw.edu.pl) Instytut Informatyki, Wydział MIM UW and Dirk Valkenborg (dirk.valkenborg@vito.be)

The project is situated at the intersection of mathematics/informatics and the application of mass spectrometry technology for the high­throughput analysis of proteins, metabolites and other bioanalytes. Our main point of interest is to apply theoretical concepts from mathematics, statistics and computer science into mass spectral data analysis. A key point in this project will be the rigorous evaluation of the isotope distributions observed in mass spectra, as an additional knowledge source for generating new hypothesis with respect to modifications, e.g., phosphorylation (paramount for understanding the complexity of biological processes), glycosylation, etc.

Further, we want to evaluate if the information content of isotope distributions is rich enough to predict the chemical composition based on the observed isotope distribution. This is useful for discerning between different types of biomolecules, e.g., to distinct bioactive peptides from lipids based on the isotope patterns observed in complex samples. Doing so new information becomes available from low­level mass spectral data.

A prospective candidate is expected to have a good understanding of stochastic methods and willingness to learn biochemistry.

Mathematical model of regulatory domain evolution

Dr Bartek Wilczyński (bartek@mimuw.edu.pl) i prof. Jerzy Tiuryn (tiuryn@mimuw.edu.pl), Instytut Informatyki, Wydział MIM UW

One of the surprising results of the whole genome sequencing projects of the last decade was the discovery of the dominance of non-coding DNA sequences in the genomes of complex organisms. While the number of genes and the length of the coding sequence remains relatively stable between organisms with very different apparent complexity such as C.elegans and H. sapiens, the size of the non-coding part of the genome seems to show some correlation with the complexity of the organism, as measured for example by the number of different cell types present. This phenomenon is connected with the growing complexity of gene regulatory mechanisms exhibited by higher organisms. Especially in transcriptional regulation, it is clear that non-coding regulatory sequences play crucial role in establishing the variety of transcriptional profiles observed in animal genes. The proposed research project is centered around the concept of a regulatory domain, i.e. a continuous part of a chromosome that contains all regulatory information required for the expression of the genes present in the domain. Such simplified definition seems to be sufficient to explain majority of transcriptional regulation in metazoa. The plan of the study is to start from these data and, using our knowledge of verified enhancer elements (such as the enhancer.lbl.gov for mammals and redfly database for insects) to propose a mathematical model of regulatory domain evolution across multiple species that would be consistent with experimental evidence. We plan to include a variety of evolutionary events such as mutations (leading to gain or loss of binding sites), duplications and deletions of larger fragments due to genome rearrangements and transpositions.

A prospective candidate is expected to have a good understanding of evolution of DNA sequences and probabilistic models of evolutionary scenarios.