Most of this page has been translated by Google. Only Czech version will be used at the state exam: English version only serves the basic informative purpose to those not mastering Czech.


  1. Bioinformatics. Fundamentals of molecular biology (construction of prokaryotic and eukaryotic cells, structure and function of nucleic acids and proteins, replication, transcription and translation). Genomics, genome and methods of its investigation. Proteomics, proteome and methods of its investigation. Bioinformatics, definition, field of interest, bioinformatic data. Sequence Similarity, Sequence Alignment, Related Algorithms. PCR, DNA sequencing, gene identification in silico, genomic browsers. Mass spectrometry of proteins. Fundamentals of phylogenetics, methods of phylogenetic tree formation.
  2. Software Engineering. SW development process. Unified Process Methodology. Agile SW development. Testing phases and test types. Software metrics, code refaktoring. Software quality. Estimating cost and time of SW development. Maintenance and reusability.
    PA017, PA104
  3. Formal models in system biology. Qualitative models. Law on active mass action, enzyme kinetics and gene regulation. Stochastic models: Markov's chain of continuous time, stochastic Petri nets, Monte Carlo simulations. Using algeber processes to specify biological models. Hypothesis specification using temporal logic, robustness of the model with respect to temporal properties.
  4. Visualization of complex data. Definitions and types of data visualization. Types of complex data. Fundamentals of visualization (visualization elements, basic principles). Specifics of multidimensional data, specifics of hierarchical and graph data. The main types of visualizations and data types for which they are best suited. Computational environment for visualization and its properties and uses (R, Processing). Data clustering. PCA and other types of data dimension reduction.
  5. Objective methods of designing information systems. Objective methods of designing information systems. Design patterns. Software architectures, architectural designs. Component Interface, Service Definition, Object-Constraint Language. Quality aspects of services (QoS). Object methods of software development, Rational Unified Process methodology.
  6. Efficient use of database systems. Database. Data storage, addressing of records. Indexing and hash for multiple attributes, bitmap indexes, dynamic hash. Query evaluation, transformation rules, statistics and estimates. Optimizing queries and schema. Transaction processing, outages and recovery. DB security, access rights.
  7. Computer networks. Layers of network models, their functionality and synergy, standardization. Network layer protocols, advanced IPv6 properties, addressing, address space division. Routing: router architecture, family of routing protocols, MPLS and TE. Transport protocols: UDP, TCP mechanisms and variants, protocols for high-speed networks with high latency. Self-organizing networks: Ad-hoc and sensor networks (environment and routing protocols). P2P networks: architecture, breakdown, routing.
  8. Computational methods in bioinformatics and systemic biology. Genome organization and computational tools for its analysis. Hidden Markov models and their use in bioinformatics. Basic principles of system biology pardigma, problem of model reconstruction, data integration. Processing of experimental data: hierarchical clustering, K-means method. Reconstruction of models from experimental data: Bool networks, Bayesian networks.
  9. Machine learning and knowledge mining. The process of acquiring knowledge from data, typical tasks in conquering knowledge. Machine Learning Methods: Teaching with a Teacher; learning without a teacher; learning in multirelational data; combination of learning algorithms. Data Preprocessing: Attribute Selection; design of new attributes; sampling methods; active learning. Search for common patterns and association rules: Apriori algorithm.
  10. Numerical methods. Solutions of nonlinear equations - iterative methods, their order and convergence, Newton method, cut method. Direct Methods of Solving Linear Equations - Gaussian elimination method, Crout method, stability of algorithms and conditionality of tasks. Iterative methods of solving the system of linear equations - principle of constructing iterative methods, convergence clauses, Jacobi iteration method, Gauss-Seidel method.