Semestr: Summer

Range: 2+2c


Credits: 5

Programme type:

Study form: Fulltime

Course language: Czech

Time table at FEE


The course will explain the principles of algorithms employed for processing biological data at the molecular level, in particular those algorithms that are used for genome sequencing, comparing of biological sequences (primarily genes), their probabilistic and grammatical modeling, for search of associations between primary and higher structures of proteins, their functions and interactions, for analyzing high-throughput data (mainly gene expression data) and for system-biological modeling of processes such as metabolism or gene expression regulation. The course will also cover some neccessary elements of molecular biology as well as basic principles of technologies for the measurement of data that are to be processed by the instructed algorithms.


genomic data, alignment, expression

Course syllabus:

1. Introduction, principles of living matter organization.
2. Genetic information flow in living systems. The central dogma, DNA, RNA, protein, replication, transcriptions, translation, repair. Inheritance.
3. Sequencing algorithms, optimal fragment assembly.
4. Biological sequence comparisons and alignments, the BLAST algorithm, nucleotide databases.
5. Multiple sequence alignment, application of dynamic programming, heuristic methods.
6. Sequence modelling, Markov models, Viterbi algorithm, grammatical modelling.
7. Sequence evolution modelling, fylogenetic trees, application of hierarchical clustering.
8. Primary and higher protein structure modelling, associations between different level structures, protein databases.
9. Protein structure-activity association modelling, prediction of associations with other proteins, DNA and other molecules.
10. Gene expression and regulation, expression in health and disease states, cellular cycle control, cancer.
11. High-throughput data analysis. Clustering, detection of significant factors, predictive modelling.
12. Background knowledge for expression data analysis. Using gene ontologies, annotations and weakly structured textual information.
13. Transcription and metabolic pathway modelling. Structure and dynamics, representation standards.
14. Reserve.

Seminar syllabus:

1. Introduction. Outline of assignments. Introduction to biology. Assignment I: WEB SEARCH.
2. DNA Ssequence alignment. Assignment II: SEQUENCE ALIGNMENT.
3. BLAST. Deadline: WEB SEARCH., Consultation: SEQUENCE ALIGNMENT.
4. Fylogenetic trees. Deadline: SEQUENCE ALIGNMENT.
5. Markov models. Hidden markov models I.
6. Markov models. Hidden markov models II.
7. Markov models. Hidden markov models III. Assignment III: GENE FINDING.
8. DNA sequence assembly.
9. Gene expression I. Consultation: GENE FINDING. Assignment: GENE EXPRESSION.
10. Gene expression II. Consultation: GENE EXPRESSION.
11. Motivation examples. Deadline: GENE FINDING
12. Holiday.
13. Interesting topics in bioinformatics (Gene networks, optogenetics...). Deadline: GENE EXPRESSION.
14. Credits


[1] Hunter, L. (2004) Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22.
[2] Lesk, AM. (2002). Introduction to Bioinformatics, Oxford Univ Press.
[3] Baxevanis, AD., Ouellette, BFF. (eds) Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Wiley.