Bioinformatics
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| A6M33BIN | Z,ZK | 5 | 2+2c | Czech |
- Lecturer:
- Filip Železný (gar.), Jiří Kléma, Zdeněk Sedláček
- Tutor:
- Filip Železný (gar.), Jiří Kléma, Ondřej Kuželka
- Supervisor:
- Department of Cybernetics
- Synopsis:
-
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.
- Requirements:
-
Knowledge of these fields: algorithms and data structures, algorithmic complexity classes, NP complexity, principles of relational databases and querying, elementary graph, automata and grammar theory, elementary probability and statistics, principles of statistical tests.
- Syllabus of lectures:
-
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.
- Syllabus of tutorials:
-
1. A introduction into the topics of individual work, software tools and lab requirements.
2. Cytometry lab tour I.
3. Cytometry lab tour II.
4. Nucleotide databases, script-based querying.
5. Individual work assignment I: biological sequence processing.
6. Individual work.
7. Protein databases, script-based querying.
8. Individual work assignment II: structure-activity associations.
9. Individual work.
10. Expression databases, script-based querying.
11. Individual work assignment II: high-throughput data analysis.
12. Individual work.
13. Individual work.
14. Credits.
- Study Objective:
-
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.
- Study materials:
-
[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.
- Note:
- Time-table for winter semester 2011/2012:
- Time-table is not available yet
- Time-table for summer semester 2011/2012:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Fri Thu Fri - The course is a part of the following study plans:
-
- Biomedicínské inženýrství a informatika - Biomedicínská informatika (compulsory course of the specialization)