Algorithmic Bioinformatics (Prof. Canzar)

A meaningful interpretation of biomedical data requires a basic understanding of the biological units and processes from which the data were generated as well as knowledge of the technologies that produced the data. The aim of this module is to equip students with this understanding. It will cover fundamental concepts in molecular and cellular biology, genetics, and strengths and limitations of current DNA sequencing technologies. Furthermore, it will highlight how biological questions can be translated into computational problems, from sequence alignment and genome assembly to gene regulation and disease variant detection.

In the lecture and lab Applied Algorithms, students learn how to apply algorithmic methods to real-world problems. A broad range of algorithmic techniques is covered, including:

• Brute-force and branch-and-bound methods for identifying hidden motifs
• Greedy strategies
• Dynamic programming
• Graph-based algorithms
• Data structures for efficient pattern matching
• Clustering methods
• Hidden Markov models

The course combines theoretical concepts with practical examples and case studies, mostly from biology.