Thesis supervisor: Attila Kiss
Location of studies (in Hungarian): Faculty of Informatics, Eötvös Loránd University Abbreviation of location of studies: ELTE
Description of the research topic:
The discovery of the double-helix structure of DNA by Watson and Crick more than fifty years ago was the starting point of a new era in molecular biology. Since then, our knowledge of biological structures and processes has grown tremendously. But many of these advances would have been unthinkable without using computational methods. Computer and Data science plays a leading role in the emerging interdisciplinary field of bioinformatics.
The main goal now is to analyze and make use of the collected data. The spectrum of topics includes classical subjects such as string algorithms, fundamental approaches for sequencing DNA and for analyzing the sequencing data, foundation of database management, but also more recent subjects.
Advances in mathematical methods and techniques in bioinformatics have been growing rapidly. Mathematics has a fundamental role in describing the complexities of biological structures, patterns, and processes. Mathematical analysis of structures of molecular systems has essential meaning for bioinformatics, biomathematics, and biotechnology. Mathematics is used to elucidate trends, patterns, connections, and relationships in a quantitative manner that can lead to important discoveries in biology.
Students have to develop new or improve classical bioinformatical algoritms, tools, and analyze them from algorithmic and mathematical aspects, also may have to build biological, genom databases using new technologies, making it efficiently searchable by indexes.
Textbook:
Hans-Joachim Böckenhauer and Dirk Bongartz. 2007. Algorithmic Aspects of Bioinformatics (Natural Computing Series). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
He, Matthew, and Sergey Petoukhov. Mathematics of Bioinformatics: Theory, Methods and Applications. Vol. 19. John Wiley & Sons, 2011.
Required language skills: English or Hungarian Recommended language skills (in Hungarian): English Further requirements: Interest in the biological challenges, ability to implement algorithm in Python, C++ or other programming language, some fundamental database knowledge, probability and statistics.
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