Course for international guest/part time students
- Faculty
- Faculty of Science
- Organization
- TTK Department of Biological Physics
- Code
- rpbcompbiof20em
- Title
- Computational Biology
- Usual semester
- Autumn
- Published semester
- 2025/26/2
- ECTS
- 4
- Language
- hu
- Learning outcomes
- The course explores general techniques and principles that are important in computational biology but are applicable in any other field that requires data analysis and interpretation. The primary aim, however, is to teach topics that are fundamental to our understanding of biology, medicine, and human health. It, therefore, neglects computationally exciting problems that are biologically-inspired, but not relevant to biology. a) Knowledge: The subject provides in-depth and thorough professional knowledge in the field of computer biology, with the application of which the student will be able to effectively solve practical problems belonging to the field of biological processes and living systems. b) Abilities: Practical implementation of theoretical concepts using computer code. Computational analysis of large-scale biological data sets. General techniques and principles that are important in computational biology but can be applied to any field that requires data analysis and interpretation. c) Attitude: The subject helps the student to be able to continuously increase his / her knowledge and apply it confidently, to continue his / her studies within the framework of doctoral training, even outside of physics. One of the highlights of the material learned is that the primary consideration for computational biology is not abstract, theoretical curiosity, but the effective treatment of problems that are essential to understanding biology, medicine, and human health. d) Autonomy and responsibility: More broadly, the aim of the subject is to extend the physical intuition and mathematical problem-solving ability of the physics student to biological problems, in particular, and interdisciplinary problems in general, and thus raise awareness to the possibility of wider applicability of university studies and the associated societal responsibility.
- Course content
- "Perhaps the most fundamental reason why computational approaches are so well-suited to the study of biological data is that at their core, biological systems are fundamentally digital in nature. To be blunt, humans are not the first to build a digital computer – our ancestors are the first digital computer, as the earliest DNA-based life forms were already storing, copying, and processing digital information encoded in the letters A, C, G, and T." -- Manolis Kellis, MIT. This course is an introduction to computational biology with an emphasis on the fundamentals of nucleic acid and protein sequence and structural analysis. It also includes an introduction to comparative and population genomics. The course covers the principles and methods of sequence alignment, motif finding, structural modeling, structure prediction, and probabilistic modeling of biological data. Topics covered: Dynamic Programming, Global and Local Alignment Comparative genomics Markov and Hidden Markov Models of Genomic and Protein Features RNA Folding Protein Folding Phylogenetics: Molecular Evolution, Tree Building, Phylogenetic Inference Human Genetics, SNPs, and Genome Wide Associate Studies
- Assessment method
- oral exam
- Bibliography
- Understanding Bioinformatics by Marketa J. Zvelebil, Jeremy O. Baum (Garland Science 2008) Foundations of Computational and Systems Biology (MIT Open Course Ware, 2014) https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/ Computational Biology: Genomes, Networks, Evolution by Manolis Kellis (MIT OCW 2017) http://ocw.mit.edu/ans7870/6/6.047/f15/MIT6_047F15_Compiled.pdf Nature Biotechnology Primers https://www.nature.com/nbt/articles?type=primer