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  1. Bioinformatics. Fundamentals of molecular biology (structure of prokaryotic and eukaryotic cells, structure and function of nucleic acids and proteins, replication, transcription and translation). Genomics, genome and methods of its research. Proteomics, proteome and methods of its research. Bioinformatics, definition, field of interest, bioinformatics data. Sequence similarity, Sequence alignment, related algorithms. PCR, DNA sequencing, in silico gene identification, genomic browsers. Protein mass spectrometry. Basics of phylogenetics, methods of creating phylogenetic trees.
  2. Software engineering. SW development process. Unified Process Methodology. Agile SW development. Testing phases and types of tests. Software metrics, code refactoring. Software quality. Estimation of costs and time of SW development. Maintenance and reusability.
    PA017, PA104
  3. Formal models in systems biology. Qualitative models. Act on the active action of matter, kinetics of enzymes and gene regulation. Stochastic models: Markov chain of continuous time, stochastic Petri nets, Monte Carlo simulations. Use of process algebras for specification of biological models. Hypothesis specification using temporal logics, robustness of the model with respect to temporal properties.
  4. Visualization of complex data. Definitions and types of data visualization. Types of complex data. Basics of visualization (elements of visualizations, basic principles). Specifics of multidimensional data, specifics of hierarchical and graph data. The main types of visualizations and the types of data for which they are most suitable. Computing environment for visualization and their properties and possibilities of use (R, Processing). Data clustering. PCA and other types of data dimension reduction.
  5. Object-oriented methods of information systems design. Object-oriented methods of information systems design. Design patterns. Software architectures, architectural patterns. Component interfaces, service definitions, Object-Constraint Language. Qualitative aspects of services (QoS). Object-oriented methods of software development, Rational Unified Process methodology.
  6. Effective use of database systems. Database. Data storage, addressing of records. Indexing and hashing for multiple attributes, bitmap indexes, dynamic hashing. Query evaluation, transformation rules, statistics and estimates. Query and schema optimization. Transaction processing, outages and recovery. DB security, access rights.
  7. Computer networks. Layers of network models, their functionality and interoperability, standardization. Network layer protocols, advanced IPv6 features, addressing, address space division. Routing: router architecture, routing protocol families, MPLS and TE. Transport protocols: UDP, mechanisms and variants of TCP, protocols for high-speed networks with high latency. Self-organizing networks: Ad-hoc and sensor networks (media access and routing protocols). P2P networks: architecture, partitioning, routing.
  8. Computational methods in bioinformatics and systems biology. Genome organization and computational tools for its analysis. Hidden Markov models and their use in bioinformatics. Basic principles of the pardigm of systems biology, the problem of model reconstruction, data integration. Experimental data processing: hierarchical clustering, K-means method. Reconstruction of models from experimental data: Boolean networks, Bayesian networks.
  9. Machine learning and knowledge acquisition. The process of acquiring knowledge from data, typical tasks in knowledge acquisition. Machine learning methods: learning with a teacher; learning without a teacher; learning in multirelational data; combination of learning algorithms. Data preprocessing: selection of attributes; construction of new attributes; sampling methods; active learning. Searching for common patterns and association rules: the Apriori algorithm.
  10. Numerical methods. Solution of nonlinear equations - iterative methods, their order and convergence, Newton's method, secant method. Direct methods for solving a system of linear equations - Gaussian elimination method, Crout method, stability of algorithms and conditionality of problems. Iterative methods for solving a system of linear equations - principle of construction of iterative methods, convergence theorems, Jacobi iterative method, Gauss-Seidel method.