Annotation

The course deals with efficient algorithms for various tasks in bioinformatics. One fo such task is an alingment of two or more sequences. Other topic covers algorithms for individual phases of genome aseembly. The course also presents compressed data structures for storing and indexing genomes and very fast pattern matching in them. Algorithms for efficient analysis and comparison of genomes.

Syllabus

1. Multiple sequence alignment algorithms I - scoring systems, dynamic programming
2. Muliple sequence alignment algorithms II - heuristic algorithms - progressive and branch and bound methods
3. Read alignment algorithms
4. Genome assembly and string graphs
5. De Bruijn graphs and Eulerian walks
6. Building de Bruijn graphs
7. Representations of de Bruijn graphs
8. Scaffolding algorithms
9. Genome compression I - LZ based
10. Genome compression II - BWT based
11. Genome analysis and comparison I - space-efficient genome analysis
12. Genome analysis and comparison II - comparing genomes without alignment
13. Latest developments in computational genome processing
14. Summary of algorithms for computational genomics

Annotation

The class covers advanced chemoinformatics and computational drug design techniques, such as lead optimization, biological information in models or the generation and exploration of chemical space.

Syllabus

1. Chamoinformatics methods for lead optimization - MMPA (matched molecular pairs analysis), bioisosters, scaffold hopping, multi-objective optimization methods
2. Biological information in chemeoinformatics – chemogenomics space, experimental and computational approaches of chemogenomics space exploration, affinity fingerprints and their applications, proteochemometrics, ligand/protein interaction descriptors, protein/ligand interaction space modeling
3. Information theory and fingerprint engineering
4. QSAR modeling – QSAR model quality assessment, applicability domain in classification and regression models, deep learning methos in QSAR and their other applications
5. Generating and exploration of chemical space, chemotype diversity and its assessment, pharmacophore modeling (topological pharmacophores and pharmacophore fingerprints), molecular docking (conformer generation, protein flexibility, consensus scoring)

Annotation

The course covers selected advanced topics in structural bioinformatics. We first focus on protein-protein and protein-DNA interactions. We introduce methods to calculate Gibbs free energy associated with these interactions, as well as docking algorithms, software tools and web interfaces. We then explain computational proteomics, protein interaction networks, induced fit, and ab initio methods for protein and peptide design. The section devoted to nucleic acids deals with sequence-dependent structural features of DNA, as well as RNA secondary and tertiary structure modelling. Finally, integrative structural bioinformatics combining experimental and theoretical approaches is exposed. Case studies will present selected problems based on current journal literature.

Syllabus

1. Protein-protein and protein-nucleic acid interactions
2. Gibbs free energy calculation methods for biomolecular interactions
3. Docking algorithms for protein-protein and protein-DNA interactions
4. Software, servers and web interfaces for predicting biomolecular interactions
5. Protein Structure Initiative and computational proteomics
6. Interactome and protein interaction networks
7. Prediction of induced fit
8. Protein design and peptide docking using ab initio methods
9. Sequence dependent structural properties of DNA and their functional role
10. RNA secondary structure prediction
11. Modeling of 3D RNA structure
12. Integrative structural bioinformatics – combining experimental data and computational methods
13. Case studies

Annotation

Processes in molecular biology and genetics take place at different length and time scales. The course deals with modelling at the mesoscopic scale, where the atoms of the biomolecules are united in larger entities. Students will first deepen their knowledge of nucleic acid structure and dynamics, an important application field of the course. Mesoscopic models of biomolecules and the solvent, including the necessary mathematical tools, are then exposed. Biomolecules often behave as stochastic systems, exhibiting transitions between different conformational states. The course therefore also includes an introduction to Markov processes and their algorithmic realization. Case studies will present selected problems based on current journal literature.

Syllabus

1. Biomolecular modelling in molecular biology and genetics
2. Structure and dynamics of nucleic acids
3. United atom models
4. Rotations and their description
5. Biomolecules represented by systems of rigid bodies
6. Polymer models
7. Vectors and tensors
8. Mesoscopic models of electrostatic interactions
9. Hydrodynamic interactions
10. Markov processes
11. Numerical implementation of random processes
12. Case studies

Annotation

A course introduces theoretical and practical aspects of Semantic Web technologies used in areas of chemical and biological databases. The Semantic Web was meant to address data interoperability. The interoperability is achieved by the conceptualization of data and storing them according to standardized rules. The key is the creation of ontologies that describe data organizations in various scientific domains. The theoretical part of the lecture is focused on Semantic Web technologies allowing storing, accessing, querying and processing of data. Whereas the practical part of the lecture is focused on ontologies used in chemical and biological databases build on these technologies.

Syllabus

1. Basics of the Semantic Web (Resource Description Framework)
2. Basics of the SPARQL query language
3. Advanced language constructions of SPARQL
4. Mapping from relational databases to RDF (R2RML)
5. Existing implementations and frameworks (Jena, RDF4J, …)
6. Implementation of user extensions to SPARQL
7. Ontology languages (RDFS, OWL, …)
8. Syntaxes for writing ontologies (RDF, XML, functional and Manchester syntax)
9. Automatic derivation of new data (simple, RDF, RDFS and OWL entailment)
10. Common ontologies (DCMI, CiTO, …)
11. Cheminformatics ontologies (ChEBI, CHEMINF, …)
12. Bioinformatics ontologies (GO, BioPAX, BAO, …)
13. Global identification of database entities
14. Related issues of chemical and biological database interoperability

Annotation

This is an introductory course of systems biology. We will focus on the structure of regulatory networks, their global properties and enrichment of regulatory motifs. We will study commonly repeating motifs, and will explain their function and reason for their evolutionary conservation.

Syllabus

1. How cells perceive a world. Regulatory networks.
2. Transcription networks and their properties. Network motifs.
3. Autoregulation: SOS DNA repair system in E. coli.
4. Coherent feed-forward loop: protection against random fluctuations. Arabinosis system in E. coli.
5. Noncoherent feed-forward loop: quick response to the environment change. Galactosis system in E. coli.
6. Regulatory networks in embryonal development: bistable switch. Sonic Hedgehog and the limb development in vertebrae.
7. Neural networks: multilayer perceptron in C. elegans.
8. Other network motifs and global structure of regulatory networks. Project assignment.
9. Robustness of protein circuits: chemotaxion in E. coli.
10. Robustness in embryonal development: body segmentation in D. melanogaster.
11. Kinetic proofreading: antigenu T recognition by the cell.
12. Optimality of gene circuits, relationship with a biological fitness: LacZ protein in E. coli.
13. Optimality of gene circuits, rule of the demand.
14. Project presentations.

Important note

The necessary prerequisite is the completion of the Information retrieval course from the module B.

Annotation

A number of electronic documents grows much faster than a human is able to deal with. Though inormation retrieval methods help to identify documents likely containing a given information based on keywords, text mining approaches deal with the interpretation of information hidden in the documents. This difficult task is related to the semantics of a natural language that is difficult to interpret unequivocally even for trained experts. Text mining adopts various statistical and information retrieval methods, approaches of a computational linguistics and artificial intelligence classification methods. In text mining, following tasks are solved: Informatin extraction - the identification of key text components and of relationships between them, Topic tracking - an intelligent text filtering based on the user profile, Summarization - the summariozation of text content, Sentence extraction - the identification of sentences that are important for text understanding, Categorization, classification, clustering - text categorization based on content similarity, Concept linkage - the identification of relationships between texts with common concepts.

Syllabus

1. Text Mining, Data Mining, Knowledge Discovery, Text Processing - basic concepts
2. Information Retrieval - basic concepts, text documents and keywords, relevance and fuzzy logic, indexing, vector model
3. Latent semantic indexing and singular value decomposition
4. Clustering of keywords and documents
5. Text classification, porobabilistic classification - Naive Bayes, k nearest neighbors, decision trees, neural networks, support vector machines
6. Linguistics in text mining, lexicon, part-of-speech tagging, named entity recognition, parsing, co-references
7. Text mining applications, automatic content extraction, automatic question answering

Annotation

The subject deals with modern methods and tool in biomedisal and medical field (CT, NMR,...). Students will solve several case studies including real applications. For the exam it is necessary to propose a draft of publication form the field of disertation thesis.

Syllabus

1. Advanced CT image analysis
2. Methods of NMR analysis
3. EEG data analysis
4. Biomedical signals modeling - overview
5. Adnaced biomedical signal visualisation in time and frequency domain
6. Chaos and dynamical analysis
7. Biomedical data formats
8. PWA model of EEG data
9. Modeling of neuron electrical activity
10. EEG synchronisation
11. EMG, detection, localisation and classification
12. Comunication models in biomedical objects
13. Biostaistics
14. Advanced modeling in biology and physiology

Annotation

The primary focus of the lectures is on the description of biological and biochemical systems using principles of physical chemistry. This interdisciplinary approach allows to apply the methodology of physical chemistry on biological and biochemical systems and to use the mathematical background of physico-chemical methods for the description of the behavior of living matter on the molecular and structural level.

Syllabus

1. The course is aimed at physico-chemical approach for the study of biological systems at different levels. The key topics including theoretical background as well as methods for experimental data mining will be discussed. The aspects of biomacromolecules´ structure and interactions, the biogenesis of biomacromolecules´ complexes including membranes are discussed from the thermodynamical point of view. Other topics include kinetics of important bio-processes (e.g. membrane transport, pharmacokinetics description of human body). The integral part of course is discussion about selected methods suitable for analysis of biological systems and their application on concrete examples.

Annotation

The course focuses on antibody-antigen interaction aspects especially in terms of in vitro characterization, resulting in a wide variety of immunoassays. After understanding the functions of the immune system and the characteristics of the structure and properties of antigens and antibodies, including their interaction, methods of targeted antibody preparation for analyzes and the characteristics of immunochemical techniques will be described. In individual projects the students will study a selected type of immunochemical technique, principles and possibilities of use. The choice of topics will either be in line with the topic of the doctoral project, or with regard to the current state of research in this area. The results will be presented in the form of short presentation during the course.

Syllabus

1. Lectures will be focused on the basic functions of individual components of immune system; adaptive part of the immune system (interaction of cell components leading to the final production of specific antibodies); characteristics related to the structure and properties of antigens and antibodies; methods of antibody preparation; parameters antigen-antibody interaction in-vitro; haptens and immunoassay; chemical modification of molecules of antigens and antibodies for immunoassays needs; details about principles and procedures of basic immunochemical methods (precipitation vs. non-precipitation methods, methods with a mark vs. without a mark); Enzyme Immunoassay and differences in variations of its arrangement; non-precipitation immunoassay with other marker types (than enzyme); amplification of the label signal per unit of antibody-antigen interaction; principles of multidetection antibody chips, immunosensors including microfluidic arrangements and surface plasma resonance; immunoblotting, immunohistochemical applications (including fluorescence multiplex), fluorescence flow cytometry. Immuno-affinity chromatography, user-friendly immunochemical assay formats. Part of the course will be devoted to elaboration and presentation of individual professional projects of students.

Annotation

A number of electronic documents grows much faster than a human is able to deal with. The information retrieval methods help to identify documents likely containing a given information. The selection of documents is based on keywords, that are assigned to characterize document content and used to specify the aims of user search. To achieve this aim, information retrieval utilizes the methods of linear algebra that work with the vector model, statistical and probability methods, methods of computational linguistics or classification and clustering methods of artificial intelligence.

Syllabus

1. Introduction to information retrieval, uncertainty, relevance, text document normalization, Zipf's law
2. Text documents indexing, querying and searching - metrics, vector model - dimensionality reduction, latent semantic indexing
3. Document and keyword clustering, distance, similarity metrics, centroid, clustering algorithms
4. Document classification, Bayesian classification, k nearest neighbors, decision trees, metoda support vector machines
5. The aims and capabilities of text mining, linguistic methods in text mining, tokenization, part-of-speech tagging, named entity recognition, parsing, coreferences
6. Text mining in information retrieval: document content extraction, automatic document summarization, automatic question answering

Annotation

The aim of the classes is to introduce to students the major topics of microbial ecology. Apart from origins of life, students will get familiar with extensive interactions in the microbial world, interactions among microorganisms and higher organisms or interactions among microorganisms and their abiotic surroundings. Information will be provided on how these interactions determine stability of microbial communities, ecosystem functioning and equilibrium maintaining. Furthermore, the relation of phylogenetics and taxonomic and functional diversity will be given. The classes are composed in a way that students will obtain key theoretical knowledge as well as overview of state of the art microbiological-ecological techniques.

Syllabus

1. Origins of life, origin of organic molecules, first cell, endosymbiotic theories.
2. Microbial phylogenetics and recunstruction of tree of life.
3. Definition of species, microbial taxonomy, operational taxonomic units.
4. Microbial ecology and functional diversity of natural habitats.
5. Ecological niches, microenvironments.
6. Integrated genomics and post-genomics approaches in microbial ecology.
7. Geomicrobiology and microbial contributions to geochemical cycles.
8. Applied microbial ecology and bioremediation.
9. New trends in microbial ecology.

Annotation

Basics of modeling of molecules (and other many-particle systems) by means of classical statistical mechanics, from force field construction to molecular dynamics and Monte Carlo simulations. Emphasis is on the methodology of a computer experiment (pseudoexperiment). A simulation project is required, either developing a code for a simple system or using a simulation package. Edu-software is available.

Syllabus

1. Introduction - What are simulations good for?
2. Repetition of statistical thermodynamics and less common ensembles (isobaric).
3. Atomistic and lattice models. Force field.
4. Molecular dynamics: Verlet's method, leap-frog. Fundamentals of Hamilton's mechanics, conservation laws. Symplecticity.
5. Other integrators (Gear, multiple timestep). Thermostats in MD.
6. Monte Carlo Methods - MC integration, Metropolis method. Random numbers.
7. Methodology of simulations and measurement of quantities, statistical errors. Boundary conditions.
8. Structural quantities: radial distribution functions, structure factor.
9. Entropic quantities: thermodynamic integration, non-Boltzmann sampling, integration of mean force, Widom's method.
10. Potential range, cutoff corrections. Coulomb's forces: Ewald summation, reaction field.
11. Other ensembles: isobaric, grandkanonical, Gibbs. Additional degrees of freedom in MD: Nose-Hoover, barostat.
12. Other MC methods: preferential sampling, molecules, polymers. Constraint dynamics (SHAKE). Optimization of simulations.
13. Brownian (Langevin) dynamics and DPD. Kinetic quantities: EMD vs. NEMD.
14. Optimization: Simulated annealing, genetic algorithms.

Annotation

The subject deals with cellular and molecular biology of host-pathogen interactions, placing a particular emphasis on mechanisms by which bacteria manipulate the immunity of the host. Specific attention is paid to regulation of gene expression and to genes involved in virulence of bacterial pathogens. The mechanisms of evasion from host immunity and action of adhesins, toxins and other virulence factors are analyzed in detail using prototypic examples of human and animal pathogens.

Syllabus

1. Co-evolution of bacterial pathogens with their hosts – basic concepts of interaction
2. The immune system of mammals and mucosal and systemic immunity to infection
3. Genomes of bacterial pathogens, horizontal gene transfer and regulation of gene expression
4. The microbial organ – microbiomes of the host in health and disease
5. Basic categories of virulence factors and pathways of their secretion beyond the bacterial membrane
6. Types and mechanisms of action of bacterial protein toxins
7. Pathways and mechanisms of intracellular penetration and survival of bacterial pathogens
8. Experimental system for host-pathogen interaction studies and identification of involved genes
9. Basic vaccine types, their design and development
10. Mechanisms of virulence of the genera Bordetella, Mycobacterium, Neisseria, Listeria
11. Corynebacterium diphteriae, Streptococcus pyogenes a Staphylococcus aureus.
12. Clostridium botulinum, perfrigens, tetani, B. cereus, Helicobacter pylori.
13. Ricketsia, Francisella, Coxiella, Neisseria, Listeria, Borrelia, Mycoplasma, Chlamydia
14. Yersinia, Salmonella, Shigella, Vibrio cholerae. Escherichia coli, EPEC, ETEC, UPEC, NMEC.

Annotation

The subject presents computational and visualization tools of the MATLAB / SIMULINK environment and its use for numeric and symbolic solution of selected computational problems. A special attention is paid to problems of data analysis, solution of linear algebraic equations, linear and nonlinear approximation, solution of nonlinear equations, to methods of numeric interpolation, derivation and integration and to the solution of difference equations. Further topis will include fundamentals of modelling and data visualization. Selected case studies will be devoted to applications of computational methods for processing of engineering and biomedical signals and images.

Syllabus

1. Computational and visualization system MATLAB, algorithmic and programming tools
2. Functions, object graphics, data structures, symbolic mathematics in MATLAB
3. Block oriented computational tools, modelling in the SIMULINK environment
4. Numeric a symbolic methods of linear algebra
5. Linear and nonlinear approximation of functions, the least square method, gradient methods
6. Solution of nonlinear equations and their systems
7. Numeric and symbolic methods of interpolation, derivation and integration
8. Solution of differencial equations
9. The boundary method and the shooting method for solution of differential equations
10. Computational intelligence in data analysis
11. The use of computational methods for processing of engineering and biomedical signals
12. CASE STUDY 1: Aproximation and statistical analysis in data processing
13. CASE STUDY 2: Solution of nonlinear equations
14. CASE STUDY 3: Modeling in the block oriented SIMULINK environment

Annotation

The lectures aim to extend the student's view to the field of numerical linear algebra. All of the most important topics in the field are covered, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability.

Syllabus

1. Eigenvalues, Singular Values, The Singular Value Decomposition.
2. QR Factorization.
3. Gram-Schmidt Orthogonalization.
4. Householder Triangularization.
5. Least Squares Problems.
6. Conditioning and Condition Numbers, Stability.
7. Stability of Gaussian Elimination. Pivoting.
8. Cholesky Factorization.
9. Eigenvalue Problems.
10. Rayleigh Quotient, Inverse Iteration.
11. QR Algorithm.
12. The Arnoldi Iteration.
13. Conjugate Gradients.
14. Preconditioning.

Annotation

The course requires the ability of application of basic characteristics of microorganisms arising from cytology, morfology, taxonomy, genetics, growth conditions, reproduction and metabolism on solving of concrete task conected with microorganisms.

Syllabus

1. Basic principles of microbiology.
2. Cell Structure and function of bacteria
3. Cell Structure and function of archae
4. Cell Structure and function of yeasts
5. Cell Structure and function of micromycetas
6. Viruses: structure and function
7. Nutrition of microorganisms their source and transport to cells
8. Cultivation of microorganisms
9. Microbial growth,reproduction and spore forming
10. Limitation of the growth of microorgannisms by external conditions
11. Metabolic diversity of microorganisms and its technological importance
12. Molecular principles of microbial genetic
13. Modern methods for microorganisms detection
14. Nowadays principles of microbial taxonomy

Annotation

The subject is devoted to selected problems of computational intelligence and machine learning including architecture of artificial neural networks, their optimization for signal and image processing and their use for adaptive noise rejection. A special attention is paid to signal and image features extraction, pattern recognition and to the use of neural networks for their classification into given number of classes. Selected case studies presented in the MATLAB computational environment are devoted to biomedical and engineering data processing.

Syllabus

1. Computational intelligence in data processing
2. Architecture of artificial neural networks, their modelling and optimization in the MATLAB environment
3. Learning and verification
4. Adaptive lineare element and their use in signal denoising
5. Multilayesr feedforward and recurrent neural networks in signal prediction
6. Feature matrix construction and classification methods in signal and image processing
7. Neural networks with topology, alternative methods of data classification
8. Machine learning, pattern recognition
9. Neural networks in image processing
10. Neural networks based on deep learning
11. Selected applications of adaptive signal processing, neural networks in robotics
12. CASE STUDY 1: Real data denoising
13. CASE STUDY 2: Signal prediction
14. CASE STUDY 3: Feature extraction and classification in biomedicine

Annotation

The lecture gives theoretical foundations to modern methods of computational chemistry and applications to molecular properties-IR, NMR spectra, etc. The lecture is altered with practical exercises on computers.

Syllabus

1. basic equations and procedures of molecular dynamics and mechanics
2. basic postulates of quantum chemistry
3. computational methods, Hartree-Fock approximation, electron density theory, perturbation calculus, inclusion of solvent into quantum chemical computations
4. Maxwell equations, molecule in electromagnetic field
5. computation of parameters of nuclear magnetic resonance
6. computations of vibrational molecular spectra, Raman scattering, infrared absorption, Raman optical activity, vibrational circular dichroism, electronic molecular spectra

Annotation

The course covers advanced methods of molecular and coarse-grained computer simulations with applications in biology, thermodynamics of solutions, and theory of phase transitions.

Syllabus

1. Parallel tempering – Replica Exchange Molecular Dynamics.
2. Metadynamics – application of adjustable external potential.
3. Kinetics of rare events techniques – transition path sampling.
4. Generalized Monte Carlo methods – Wang-Landa algorithm.
5. Statistical thermodynamics of solutions – Kirkwood-Buff theory.
6. Free energy functional theory – mean-field theories, Flory-de-Gennes theory.
7. Langevin equation, fluctuation-dissipation theorem. Stochastic thermostats.
8. Brownian dynamics, dissipative particle dynamics.
9. Special ensembles in MC: from the grand canonical ensemble to Gibbs ensemble to reaction ensemble. Osmotic ensemble in MC and MD.
10. Phase equilibria. Slab geometry, chemical potential of liquids and crystals.
11. Surface tension and interfacial energy of crystals.
12. Critical point: how to beat critical slowing-down, finite-size scaling, renormalization group.
13. MD and MC simulations of polarizable molecules.
14. Kinetic quantities (viscosity, el. conductivity, diffusivity). EMD: Linear Response Theory, Green-Kubo formulas, Einstein relations. NEMD, SLODD.

Annotation

Microscopy techniques provide an efficient and unique approach to the study of fixed tissues and living cells with high specificity and sensitivity. Course aims to acquaint students with modern microscopy techniques for biology object imaging. Part of the workshop will be the introduction to automated wide-field, confocal and superresolution fluorescent microscopy. First, a brief introduction to the physical mechanisms underlying these imaging techniques will be given. Then we introduce the theoretical and/or practical basis of particular advanced imaging techniques. The basics of image analysis will be covered as well.

Syllabus

1. Fluorescence microscopy as a specific part of optical microscopy, quantification of fluorescence, fluorescent tags.
2. Confocal, multiphoton and correlation microscopy.
3. The application of fluorescence microscopy - FRAP (Fluorescence Recovery After Photobleaching), FRET (Förster Resonance Energy Transfer)
4. The application of fluorescence microscopy - TIRF (Total Internal Reflection Fluorescence), SIM (Structured Illumination Microscopy)
5. The application of fluorescence microscopy - PALM (PhotoActivated Localization Microscopy), STORM (Stochastic Optical Reconstruction Microscopy)
6. The application of fluorescence microscopy - STED (Stimulated Emission Depletion Microscopy), FLIM (Fluorescence-Lifetime Imaging Microscopy).
7. Image analysis – parameter ajdustment before acquisition, software for image processing and analysis – deconvolution, 3D visualization, colocalization, tracking, correction of chromatic abberations.

Annotation

This course focuses on kinetics and mechanisms of enzymatic reactions, thermodynamics of enzyme-inhibitor (or enzyme-cofactor, enzyme-activator etc) interactions, analytical applications and biophysical methods for enzyme assays, 3D structure and mechanisms of their action. The course also focuses on enzyme engineering approaches and enzyme prospection by gene technologies. Properties of selected industrial enzymes will be presented. Enzymes used in molecular biology will be also discussed. In individual projects the students will study a selected type of enzymes, their structure, mechanism, function and application. The choice will follow the topic of the doctoral project or the current state of research in enzymology. The results will be presented in the form of short presentation.

Syllabus

1. Content of this subject is divided into three blocks the first of which will be focused on the basic characteristics of enzymes, i.e. a) detailed description of enzyme kinetics of single- or multiple-substrate reactions b) enzyme inhibition and c) determination of enzyme activity by the application of spectrophotometric, separatory, electrochemical and other techniques. In the second block students will be acquainted with a) mechanisms of enzyme reactions and approaches to their research and b) modes of regulation of enzyme activity on the molecular level, i.e. by non-covalent interactions and covalent modifications. Third block will be dedicated to the possibilities of enzyme application with respect to a) development of new enzymes by the methods of enzyme engineering, directed evolution, rational design, metagenomic approach etc. b) sources and production of technologically important enzymes and enzymes used in medicine c) characterization of selected enzymes and d) characterization of enzymes applied genetic engineering and molecular biology.

Annotation

The basic concepts of graph theory are introduced. Algorithmic solutions of engineering problems are discussed.

Syllabus

1. Basic concepts in graph theory. Representations of graphs.
2. Paths in graphs. The task of the shortest path.
3. Connected graphs, Components od connectivity, blocks of graphs.
4. Trees. Heap-sort.
5. Spanning tree. Greedy algorithm for minimal spanning tree.
6. System of distinct representatives. Matching in bipartite graphs.
7. Matching in general graphs.
8. Euler graphs. The Chinese postman problem.
9. Hamiltonian graphs. The travelling salesman problem.
10. Planar graphs and their characteristics.
11. Coloring. Coloring of Planar graphs.
12. Flows in networks.
13. Theory of complexity. P and NP problems. Good characteristics.
14. Examples of graph theory applications.

Annotation

An interactive course focused on current research trends in the areas of biochemical and microbiological research with emphasis on topical and fundamental discoveries and new, state-of-the-art experimental strategies. The course consists of independent lectures on subjects inspired by recent publications in top journals, followed by facilitated discussions. Specific topics of lectures are not usually repeated year-round, teachers select them based on the progress in relevant scientific fields. Lectures are prepared and delivered by students under the guidance of teachers; the lectures on selected topics will be delivered by invited experts. In the introductory lecture, the students are acquainted with the lecture topics plan, which may be a subject to modification based on the discussions of the proposals from the students. The sessions can be organized in semester weeks or as a block "miniconference".

Syllabus

1. Presented and discussed topics will focus on advances in fields covering research on cell and molecular biology [in bacteria, archebacteria, fungi, plants and humans], microbiomes and (meta)genomes, intercellular communication and signaling, population dynamics, extremophiles, plant and animal pathogens and viruses and the host-pathogen/virus interactions, evolution of organisms, modern therapies and diagnostics, immunology, production and bioconversion of valuable substances, climatic changes and the impacts of environmental pollution.

Annotation

The subject presents principles of image processing and functional transforms usage in image analysis. Special attention is paid to colour processing, image segmentation, noise components rejection, image enhancement, data compression, pattern recognition and feature classification including estimation of accuracy and cross-validation errors. Applications include processing of images related to chemistry, material engineering, biochemistry, and biomedicine.

Syllabus

1. Fundamentals of computational, programming and visualization system MATLAB / SIMULINK
2. Mathematical representation of multidimensional signals, image coding
3. Selected numerical methods for image processing: interpolation, approximation
4. Two-dimensional discrete Fourier transform in image analysis and resolution changes
5. Multi-dimensional digital filters in image-denoising
6. Gradient methods in image enhancement
7. Selected methods of image segmentation, feature extraction and classification
8. Discrete wavelet transform in data compression image denoising
9. Computational intelligence in microscopic data analysis and classification
10. Spatial data visualization and processing

Annotation

This course follows the structure of a typical research paper. Using real-life examples, it presents a philosophy of scientific writing, covers the key areas of grammar related to scientific English, and provides techniques for generating and sustaining reader attention.

Syllabus

1. Style Matters
2. Word Choice
3. First Impressions – Your Title
4. Articles in your Article
5. Word Order
6. Making the Abstract Solid
7. Grammar for the Abstract
8. Sentence Flow
9. Paragraph Structure
10. The Introduction
11. Materials and Methods
12. Expressing Results, Discussing Results
13. The Right Conclusion
14. Our Full Stop: Punctuation