Master study

Lectures are in Czech language only.


Study plan

1. grade

winter semester

CodeCourse nameP/C/LExaminationCredits
Compulsory courses
M320043 Molecular Modelling

Annotation

The subject covers theoretical backgrounds and practical examples of application of computational methods on molecular systems, namely molecular mechanics and quantum mechanics. It includes small molecules, as well as biomolecules and supra-molecular systems.

Syllabus

  1. 3D geometry, Cartesian coordinates, Z-matix, connectivity, PDB file
  2. Inspection of 3D models, bond lengths, angles, torsions, rendering
  3. Structure-potential energy relationship, different levels of theory, potential energy hypersurface
  4. Molecular mechanics, ball-spring model, single point calculations
  5. Schrödinger equation, wave function, approximative solutions, methods (including semi-empirical), variational methods, basis sets
  6. Prediction of properties (charges, reaction kinetics and mechanisms, spectral and chiroptical properties)
  7. Geometry optimization, local minima problem
  8. Molecular vibrations, normal modes
  9. Solvation, implicit and explicit solvent, continuum electrostatics
  10. Molecular dynamics, PBC, NPT, NVT, thermostats, constraints
  11. Sampling, data collection, analysis and visualisation
  12. Mesoscopic simulations (coarse graining, Brownian simulations)
  13. Free energy, metadynamics and related methods
  14. Example studies
 2/0/0 Zk 3
M143003 Genomics: Algorithms and Analysis

Annotation

Within this course contemporary high-throughput sequencing techniques are introduced. The main focus of the course are individual applications such as, e.g., ChIP-Seq, RNA-Seq, resequencing, metagenomics or de-novo sequencing. A procedures of the complete analyses for individual applications will be described, and will be demonstrated during practical exercises. Explained knowledge include sequence quality reading ("base calling") and its control, data formats and standards, advanced software tools and evaluation of their suitability, mapping of sequence data, short and long sequence assembly, identification of SNPs, and functional annotation.

Syllabus

  1. Itroduction to genomics and sequencing: historical overview, basic concepts.
  2. Sequencing techniques: Sanger method, NGS (new generation sequencing): Illumina, SOLID, 454, etc. Comparison of sequencing techniques.
  3. Sequencing data: Visualization - EnsEMBL, UCSC Browser, Artemis, ACT, Mummer, Circos. Base-calling and sequence quality control. Data formats.
  4. DNA sequence assembly I: Sequence mapping onto reference genome. De novo assembly of long sequencies - newbler, mira. De novo assembly of short sequencies - WGS, velvet, soap2, AbySS.
  5. DNA sequence assembly II: Sequencing projects, cDNA sequencing, scaffold formation. Software - Staden, AMOS, Consed.
  6. Genome annotation: Prediction of gene models. Identification of non-coding RNA. Identification of protein domains. Functional annotation. Database sources and annotation systems.
  7. Detection of sequence variations: Identification of single-nucleotide polymorphisms (SNPs), insertions and deletions, translocations, inversions, and copy-number variations (CNVs).
  8. Analysis of protein-nucleic acid: ChIP-seq - protein/DNA interaction. CLIP-seq - protein/RNA interaction.
  9. Differential expression I: RNA-seq. Sequence mapping. Measurement of gene expression. Normalization, summarization and differential expression. Identification of transcript variants - alternative splicing. Experimental design.
  10. Differential expression II: micro RNA expression profiling. Identification of novel micro RNAs.
  11. Epigenomics: Analysis of whole-genome methylation maps.
  12. Metagenomics: gene annotation and metabolism reconstruction - MG-RAST. Amplicon sequencing (16S rDNA) and analysis of taxonomic units.
  13. Comparative genomics: Interspecific genome comparison.
  14. Genome projects: ENCODE. Paleogenomics. Sequencing projects. WWW tools - Galaxy, GMOD, Gbrowser, BioCycle, RAST, EnsEMBL API. Personalised medicine.
 2/2/0 z, Zk 5
M319001 Molecular Biology

Annotation

The subject is devoted to the study of cellular processes at the molecular level and is conceived so that the students receive the basic knowledge for understanding the complex functioning of eukaryotic cells in multicellular organisms.

Syllabus

  1. Organization of eukaryotic cells and their integration into tissue, introduction to cytology and histology: eukaryotic cells organization: cell surface organization, cell-cell and cell-extracellular matrix interactions, cytoskeleton, cellular organels; types of tissue and their basic characterization: connective tissue, epithelial tissue, nervous tissue and muscle tissue
  2. Regulation of protein function: protein folding: molecular chaperon and chaperonins; abnormally folded proteins, amyloid fibrils; protein degradation – ubiquitin-proteasom system; synthesis x degradation, mRNA degradation; modification of proteins: non-covalent modification: molecular switches, covalent modification: ubiquitinylation, phosphorylation, acetylation, glycosylation, S-S bridges formation, neddylation, sumoylation, methylation
  3. Membrane proteins and their functions, mechanisms of transmembrane transport of ions and small molecules: main function of membrane proteins and their characteristic, main classes of membrane proteins tranporting ions and small molecules: channels: mechanism of ion selectivity, movements of water: aquaporins; transporters: transport of glucose, type of GLUT proteins, mechanism of GLUT 1 transporter; ATP-powered pumps P-, V- and F-classes, mechanism of Na+/K+ ATPase, ABC proteins - flipases;
  4. Moving proteins into membranes and organelles I: intracytoplasmic trafficking: targeting proteins to and across the ER membrane, protein quality control in ER, targeting to mitochondria, targeting to peroxisomes, transport in and out of the nucleus
  5. Moving proteins into membranes and organelles II: Vesicular trafficking, secretion and endocytosis: mechanism of vesicle budding and fusion, early stages of the secretory pathway: retrograde and anterograde transport, later stages of the secretory pathway, targeting to the lysosomes, endocytosis
  6. Signal transduction I: introduction to signal pathways, from extracellular signal to cellular response, signal transduction molecules: receptors, ligands, second messengers, monomeric and trimeric G-proteins, protein-kinases and phosphatases, adaptor molecules, G-protein-coupled receptors
  7. Signal transduction II: signaling pathways controlling gene expression: receptor of tyrosine kinases, receptor of serine kinases, JAK-STAT signaling pathway, Ras/MAP kinase pathway, phosphoinositide signaling pathway, PI-3 kinase pathway, signaling pathway controlled by ubiquitinylation: Wnt, Hedgehog and NFkB, signaling pathway controlled by protein cleavage: Notch/Delta
  8. Cytoskeleton I: components, microfilaments, G-actin and F-actin, dynamics of actin filaments, mechanism of actin filaments assembly, actin-binding proteins, organization of actin-based cellular structures, intracellular movements by regulation of actin polymerization, actin-based motors: myosins, principle of myosin-powered movements, contraction of skeletal and smooth muscles
  9. Cytoskeleton II: microtubule structure and characteristics of tubulin; microtubule-organizing center (MTOC), microtubul structure and dynamics and their regulation, microtubule-based motor proteins: kinesines and dyneins; the role of microtubules in mitosis, composition of mitotic spindle, kinetochores; intermediate filaments: classes, localization and function, coordination and cooperation of cytoskeleton elements
  10. The eukaryotic cell cycle and its control: characteristics of individual stages of the cell cycle and its control points, cyclins, cyclin-dependent kinases and ubiquitin-ligases and regulation of their activity, introduction to cell cycle regulation: restriction point, RB protein, E2F transcription factor, mitosis and its regulation, cytokinesis, meiosis
  11. From stem cells to cell death: early mammalian development and first differentiation events, embryonic stem cells, cell polarity mechanisms, asymmetric cell division, cell death and its regulation, programmed cell death – apoptosis, family of Bcl2 proteins and their characteristics, adapter proteins, apoptosome, caspase activation, apoptotic pathways and its regulation
  12. Integrating cells into tissues: cell-cell adhesion and interaction, cell-extracellular matrix adhesion and contacts, types of cellular junctions: adherent junctions and desmosomes, tight junctions and gap junctions, extracellular matrix: basal lamina
  13. Molecular biology of cancer: differences between normal and cancer cells, the origins and development of cancer, proto-oncogenes and tumor suppressors, cancer and misregulation of cell growth and cell death: growth factors, RB protein, p53 protein…., metastasis
  14. Methods in molecular biology
 2/0/0 Zk 3
M320017 Genetic Engineering

Annotation

The subject is focused on understanding of principles of manipulation with nucleic acids and their analysis. It cover information about various methods of introducing genes into diverse cell types, determination of gene expression and analysis. The goal is to deliver information about techniques genetic engineering for orientation to facilitate decision for optimal method for particular application. The lectures cover basic methods of isolation, analysis and modification of nucleic acids and special applications as gene modification for detection or affinity purification of gene product, study of interaction of proteins and nucleic acids, or gene therapies.

Syllabus

  1. Recapitulation of genetic principles.
  2. Bacterial plasmid vectors.
  3. Basic operations with DNA.
  4. Labeling of nucleic acids and application of probes.
  5. Analysis of DNA sequences.
  6. Principles of work with RNA.
  7. Polymerase chain reaction and its applications.
  8. Expression in microbial cells, markers, fusion proteins.
  9. Tissue cultures and their use for gene expression.
  10. Principle of construction of transgenic organisms.
  11. Mutations, directed mutagenesis.
  12. Genome mapping, restriction analysis.
  13. Product detection - metabolic labeling, electrophoretic and immunochemical methods.
  14. Methods for study in protein and nucleic acids interactions.
 2/0/0 Zk 3
M143002 Statistical Data Analysis

Annotation

This course is the follow-up to the Applied Statistics lectures. Freely available statistical software R is presented, and students use it to practise common statistical tasks such as, e.g., exploratory data analysis, hypothesis testing, analysis of variance or regression.

Syllabus

  1. Introduction to R statistics software, help
  2. Input and output in R
  3. Creating graphs in R
  4. Basic statistic distributions, random numbers in R
  5. Descriptive one-dimensional statistics, boxplot
  6. Standard deviation, mean error, confidence interval and their graphic representation in R
  7. Hypotheses testing in R: t-test
  8. Hypotheses testing in R: variance analysis
  9. Descriptive statistics and their use in R
  10. Creating and testing linear models in R
  11. Creating and testing non-linear models in R
  12. Basic linear algebra in R
  13. Principal component analysis, its implementation and use in R
  14. Cluster analysis in R
 2/2/0 z, Zk 5
M143010 Practical Classes in Bioinformatics I

Annotation

The goal of the course is to get practical knowledge of available bioinformatics toolkits and to be able to write some useful bioinformatics Python programs yourself.

Syllabus

  1. Data formats in bioinformatics.
  2. Algorithm complexity I. Recursion, memoization. Dynamic programming.
  3. Sequence alignment.
  4. Multiple sequence alignment.
  5. Markov chains and models. Hidden Markov models.
  6. Motifs discovery and search.
 0/2/0 kz 2
M500001 FIT Efficient Text Pattern Matching

Annotation

Students get knowledge of efficient algorithms for text pattern matching. They learn to use so called succinct data structures that are efficient in both access time and memory complexity. They will be able to use the knowledge in design of applications that utilize pattern matching.

Syllabus

  1. Introduction, basic definitions, border array.
  2. Text full index: Suffix array.
  3. Text full index: Suffix tree, LCP construction.
  4. Text full index: Factor, suffix automata, on-line construction.
  5. Exact pattern matching algorithms.
  6. FFT in pattern matching.
  7. Succinct data structure: rank & select.
  8. Succinct data structure: wavelet tree.
  9. FM-Index.
  10. Dictionary representation, spell checking.
  11. Approximate pattern matching.
  12. Pattern matching in bioinformatics and musicology.
  13. Pattern matching in bioinformatics and musicology.
 2/1/0 z, Zk 4
Compulsorily optional courses
M445021 Statistical Pattern Recognition

Annotation

The lectures are oriented to application of mathematical statistics in pattern recognition. Basic terms, their relationships to statistics, programming techniques, and descriptor design are poined out together with research, real data processing, and applications.

Syllabus

  1. Class, pattern, descriptors, pattern set for statistical pattern recognition
  2. Repetitorium of basic statistical terms and principles
  3. Quality of desroptor set: the best descriptor, acceptable subset of descriptors
  4. Linear discrimination analysis as a tool for pattern recognition
  5. Evaluation of pattern recognition quality: p-value, sensitivity, specificity, error, AIC, BIC
  6. Cross-validation methodology in evaluation of recognition quality
  7. Linear data transforms: normaliztion, standardization, PCA, spherization
  8. Robust and regularized methods and their advantages in pattern recognition
  9. Application of metrics in pattern recognition: Euclidean, Minkowski, Mahalanobis, k-NN, c-mean
  10. Application of PDF in pattern recognition: GMM, Parzen and LQ estimates
  11. Linear, non-linear, and logistic regression as pattern recognition tools
  12. Feature space reduction as application of binary optimization
  13. Kernel functions in design of non-linear classifiers
  14. Fuzzy sets in pattern recognition: fuzzification, FCM
 2/2/0 z, Zk 5
M445023 Introduction to Python

Annotation

The course Introduction to Python provides a basic overview of the possibilities of Python and its possible applications. Students will learn about data structures, program flow control, and basic Python features, including creating documentation, working with the versioning system, testing code, and using the numpy and matplotlib packages.

Syllabus

  1. Versioning, Primitive variables and their types, strings, basic functions.
  2. Container variables (list, dict, set, tuple).
  3. Basic operators and comparisons.
  4. Conditions, loops.
  5. Functions including functions with multiple arguments, optional arguments, kwargs, * args.
  6. Classes and objects, modules and packages and basics of PPE access.
  7. Serialization, lambda functions, maps, filter, files.
  8. Project.
  9. Docstrings and automatic documentation generation, regular expressions.
  10. Generators, list comprehension.
  11. Exceptions, code testing.
  12. Decorators.
  13. Numpy module and mathematics in Python.
  14. Data visualization using the matplotlib module.
 1/3/0 kz 5

summer semester

CodeCourse nameP/C/LExaminationCredits
Compulsory courses
M143007 Computational Drug Design

Annotation

The aim of the course is to introduce basic principles and methods of molecular informatics and cheminformatics which deal with computational analysis of relationships between small organic molecules and biomolecules. Students will also be acquainted with concepts of a related field of a computational drug design. Topics include e. g. molecular similarity, design of chemical libraries, quantitative structure-activity relationship (QSAR), virtual screening or prediction of pharmacokinetic and toxicologic properties of compounds.

Syllabus

  1. Molecular informatics - what is it, scientific origins and fundamental concepts. Molecular informatics and computational drug discovery. Relationship between molecular informatics, chemoinformatics and bioinformatics, their synergy and differences.
  2. Representation and manipulation of 2D molecular structures. Line notations for describing chemical structures (SMILES, InChI, InChIKey). Chemical table file formats (SDF, CTFile family).
  3. Structure and substructure searching, practical aspects of structure searching.
  4. Molecular descriptors calculated from 2D and 3D molecular representations.
  5. Molecular similarity methods - similarity based on 2D fingerprints, similarity coefficients, 3D similarity.
  6. Compound classification and selection. Combinatorial chemistry and library design - diverse and focused libraries, diversity estimation, multi-objective design.
  7. Ligand- and structure-based virtual screening. Paharmacophores, methods of their derivation and their use in virtual screening.
  8. Predictive QSAR (Quantitative Structure-Activity Relationships) modeling - general workflow and data preparation.
  9. Predictive QSAR modeling - development and validation of QSAR models.
  10. Analysis of high-throughput screening data.
  11. Predicting pharmacokinetics (ADME/Tox) properties.
  12. Computer-aided molecular design - inverse design and de novo design.
  13. Chemoinformatics software and database technologies.
  14. Integrated chemo- and bioinformatics approaches to virtual screening and computational drug design.
 2/2/0 z, Zk 5
M143017 Semestral project

Annotation

In this course, the student, under the guidance of a designated supervisor, will prepare a written review on a chosen topic. This topic is related to the scientific activities of the Department of Informatics and Chemistry and the student will, thus, become acquainted in advance with the scientific results that will be the starting point for the solution of his/her diploma thesis. The review will be used as the theoretical part of the thesis. The student will present the review orally to a wider scientific audience at the end of the semester.

Syllabus

  1. Formulation of project objectives.
  2. Ongoing work on partial tasks, ongoing communication with the supervisor, and work with literature resources.
  3. Evaluation of achieved results.
  4. Preparation of the project report.
  5. Oral presentation of project results.
 0/5/0 kz 3
M143004 Gene Expression Data Analysis

Annotation

This course will present basic types of functional genetics data such as RT-qPCR data, DNA chips profiling data, and high-throughput sequencing data. Students will acquire information on how to preprocess, clean, and standardize data, and will be acquainted with specific statistical and exploratory data analysis methods used for multidimensional genomic data processing. Students will learn how to interpret data using gene ontologies, how to archive, and how to visualize data. During exercises students will practise gained knowledge on the real life data, and will master commonly used online resources and analytical tools.

Syllabus

  1. Introduction. Types of functional genetics data. Aims of the anlyses.
  2. RT-qPCR data preprocessing: Primer and probe design. Standard curve.
  3. RT-qPCR data preprocessing: Amplification curve. Threshold cycle. Background correction. Data normalization.
  4. Transcription chips data preprocessing: Noise removal. Data normalization. Relative and absolute quantification.
  5. Transcription chips data preprocessing: Variance stabilization. Summarization of intensity values.
  6. High-throughput sequencing: Reading and mapping.
  7. Further applications: Analysis of single nucleotide polymorphisms and chromosomal abberations. DNA methylation.
  8. Expolratory data analysis: Dimensionality reduction. Clustering. Control points.
  9. Linear models. Problem of test multiplicity.
  10. Classification methods.
  11. Design of experiments and randomization. Replication.
  12. Annotation and results archivation: Genomic browsers and expression databases.
  13. Biological interpretation: Gene Set Enrichment Analysis (GSEA). Database of signalling pathways. Gene ontologies.
  14. Integration with interaction data: Network analysis. Database of interaction data.
 2/1/0 z, Zk 4
M143013 Structural Bioinformatics

Annotation

The course introduces structural bioinformatics – a multifields discipline which utilizes biomolecular structural data obtained by a range of experimental methods – X-Ray, NMR and electron microscopy. High numbers of structures in structural databases (ca. 130 000 structures in total) allow the application of computational and statistical methods to extract principles which control the folding process. These principles are used in structure prediction methods or as a basis for structural modelling in combination with physico-chemical rules. Students will get knowledge of basic principles used in structural bioinformatics, statistical methods, and available tools for structural analysis and prediction.

Syllabus

  1. Proteins - basic characteristics, structural building blocks
  2. Protein Databases - structure and knowledge based
  3. Prediction of biomolecular properties based on their sequences
  4. Secondary structure prediction of proteins, utilization and methods
  5. Protein folding – principles, experimental methods, protein code
  6. Tertiary structure prediction of proteins - homology modelling
  7. Tertiary sructure prediction of proteins - threading
  8. Tertiary structure prediction of proteins – ab initio methods
  9. Methods used for prediction and analysis of protein protein interactions
  10. Proteins - a case study
  11. DNA structure and its quantitative description
  12. Basic characteristics of RNA structural motifs
  13. Structural databases of nucleic acids
  14. Nucleic acids – a case study
 2/0/0 Zk 3
M143011 Practical Classes in Bioinformatics II

Annotation

This is the follow-up course for "Practical Classes in Bioinformatics I" and continues in exercising practical bioinformatics programming.

Syllabus

  1. Algorithm complexity II. Problémy třídy složitosti P a NP.
  2. Protein folding. RNA structure.
  3. Graph algorithms. Genome assembly.
  4. Phylogenetics.
  5. Genové regulatory networks.
  6. Neural networks. Machine learning.
 0/2/0 kz 2
M413004 Multivariate data analysis

Annotation

Basic principles of selected statistical methods for analysing multidimensional data will be outlined with focus on reconciliation of the assumptions of the methods and interpretation of their results. Students will learn how to perform corresponding calculations in statistical software R.

Syllabus

  1. Data vector, data matrix and matrix algebra (multiplication, inverse matrix, eigenvalues and eigenvectors), covariance matrix.
  2. Vizualisation of multidimensional data.
  3. Exploratory data analysis (EDA).
  4. Cluster analysis.
  5. Principal component analysis (PCA).
  6. Multidimensional scaling.
  7. Parameter estimation and hypothesis testing. Bayesian statistics.
  8. Multivariate analysis of variance (MANOVA).
  9. Regression methods 1 - multiple linear regression.
  10. Regression methods 2 - principal component regression (PCR), generalized linear models (GLM).
  11. Discriminant analysis.
  12. Canonical correlation analysis.
  13. Factor analysis (FA).
  14. Supplements and summary of multivariate statistical methods, buffer for holidays.
 2/2/0 z, Zk 5
M500002 FIT Data Mining Algorithms

Annotation

In this course, we discuss most popular data mining algoritms and optimization techniques such as decision trees, support vector machines, multilayered perceptrons etc. We also explain theoreticaly basic elements of statistical learning that are essential for all data engineers.

Syllabus

  1. Introduction to data mining, classification, prediction, K-NN algorithm and variants
  2. Model, evaluation, plasticity regularization
  3. Classification and Regression from statistical point of view
  4. Decision Trees (C4.5, CART, MARS algorithms)
  5. Classification by means of perceptrons and its generalization
  6. Linear, polynomial and logistic regression, LMS, MLE algorithms
  7. Nonlinear SVM-classifiers and the SV-regression
  8. Inductive modelling - GMDH MIA, COMBI
  9. Nonlinear regression by multilayered perceptrons
  10. Ensemble models (Adaboost algorithm)
  11. Statistical approach to neural networks
  12. Cluster analysis (K-means, agglomerative clustering, neural gas, SOM)
  13. A statistical approach to number of hidden neurons selection
 2/1/0 z, Zk 4
Compulsorily optional courses
M143008 Probability and Random Processes

Annotation

The course is focused on computer modelling of biological macromolecules (nucleic acids and proteins) and their interactions. With increasing computer power and development of new algorithms, computer modelling now forms an integral part of research in molecular biology, genetics and biochemistry. The first part of the course comprises an introduction to probability theory and stochastic processes, more solid than usually taught in introductory courses. This knowledge is of broader use well outside the domain of biomolecular modelling. We then use the acquired theoretical background to formulate an important simulation method, Brownian dynamics. We finish by discussing its applications to specific biological problems such as protein-ligand interactions or macromolecular dynamics in the cell. The exercises include theoretical topics as well as simple computations which the students are supposed to programm themselves.

Syllabus

  1. Introduction. Length and time scales in biomolecular modelling
  2. Probability
  3. Random variables
  4. Characteristics of random variables
  5. Probability distribution
  6. Normal distribution
  7. Stochastic processes
  8. Langevin equation
  9. Brownian motion
  10. Brownian dynamics simulations
  11. Application I: Diffusion-controlled protein-ligand binding
  12. Application II: Dynamics of nucleosomes and of the chromatin fibre
  13. Application III: Movement and interactions of biomolecules in the cell
  14. Application IV: Bownian simulations of DNA and RNA nanostructures
 2/1/0 z, Zk 4
M320010  Enzymology

Annotation

The general part of the course presents enzyme nomenclature, their covalent structure and structures of cofactors, enzyme kinetics, enzyme assays, regulation of enzymes in the cell, medicinal enzymology, enzyme engineering and mechanisms of enzymatic reactions. The second part will present applied enzymology, namely industrial production of enzymes, application of enzymes in food and non-food industry, enzyme-associated changes in food and raw materials and the role of enzymes in analytical chemistry, clinical diagnostics molecular biology research and organic synthesis.

Syllabus

  1. Enzyme structure, cofactors, coenzymes and prosthetic groups
  2. Enzyme nomenclature, overview of enzyme classes, formation of systematic and accepted names of enzymes
  3. Enzyme kinetics, Michaelis-Menten kinetics, inhibition, multi-substrate reactions, evaluation of kinetic parameters by non-linear regression
  4. Enzyme assays, application of optical, separation, electrochemical and other methods, enzyme activity calculation
  5. Regulation of enzyme activity by non-covalent interactions and covalent modifications, methods of enzyme research
  6. Enzymes as drug targets, drug design, protein-ligand interaction thermodynamics
  7. Explanation of enzyme catalysis, mechanisms of enzymatic reactions, methods of mechanism studies, enzyme engineering, rational and directed evolution
  8. General properties of technological enzymes, their sources, searching and production of new enzymes
  9. Characteristics of the most important technological enzymes
  10. Application of enzymes in food technologies
  11. Application of enzymes in non-food technologies
  12. Biochemical changes in food and food raw materials
  13. Enzymes as analytical sensors and diagnostic tools
  14. Biotransformations, enzymes in organic synthesis
 2/1/0 z, Zk 4

2. grade

winter semester

CodeCourse nameP/C/LExaminationCredits
Compulsory courses
M320022 Structural Bioinformatics seminar

Annotation

The subject gives practical training in computational methods for prediction of structures of proteins, their application in modeling their complexes with small-molecule ligands and for simulation of their dynamics. The subject follows introduction to bioinformatics and structural biology and develops practical skills based on these subjects. These skills will help students in further praxis in biochemical and pharmaceutical research and development.

Syllabus

  1. Cloud computing - installation of a virtual machine
  2. Cloud computing - administration and use of the virtual machine
  3. 3D structures of G protein-coupled receptors
  4. Homology modeling - prediction of the structure of histamine H2 receptor based on H1 receptor
  5. Homology modeling - prediction of the structure of histamine H2 receptor based on other receptors
  6. Application of a homology models - docking of known ligands
  7. Application of a homology models - preparation for virtual screening
  8. Application of a homology models - virtual screening
  9. Application of a homology models - analysis of the results of virtual screening
  10. Application of a homology models - dynamics of histamine H2 receptor
  11. Application of a homology models - dynamics of histamine H2 receptor with a ligand
  12. Dynamics of histamine H2 receptor - acceleration by metadynamics
  13. Dynamics of histamine H2 receptor - acceleration by parallel tempering
  14. Evaluation of student projects
 0/2/0 kz 2
M342018 Scientific Communication

Annotation

The course is aimed at acquisition of knowledge of presentation methods of own results and public performance at an Ms degree level. It covers also practicing of presented topics.

Syllabus

  1. Forms of communication, characterization of written and oral communication
  2. Structured CV, motivation letter, personal archive
  3. Oral speech forms and their characteristics, retorics, orator excercises
  4. Rules for creation of poster communication, graphical arrangement, working with colors, poster presentation
  5. Rules for oral short scientific communication, creation of accompanying illustrations, lecture presentation
  6. Plenary and invited lecture on a conference, preparation, presentation, most frequent mistakes
  7. Appearance in a discussion, query formulation, discussion control, session control
  8. Preparation of a scientific publication, formatting of tables and charts, editorial process, referee’s opinion
  9. Style, content and formal aspect of diploma thesis
  10. Characterization of basic chapters of thesis, examples
  11. Project preparation, creation of opinions
  12. Non-verbal communication, emotional intelligence
  13. Science results evaluation, bibliometry, scientometry, webometry
 2/1/0 z, Zk 4
M143009 Phylogenetics and Applied Genomics

Annotation

The course focuses on comparative genomics both in the vertical direction, between species, with an emphasis on biological evolution, and in the horizontal direction, interspecies, with emphasis on diversity of populations. It describes the main evolutionary principles and their application to genomic and proteomic data with a focus on second and third generation high-throughput sequencing methods. It describes methods and algorithms of construction of phylogenetic trees from genic, genomic and protein sequences. It shows the differences between genome and species trees. It also describes known mutation processes, genome evolution, the molecular clock concept, selection and genetic drift on the molecular level, nucleotide composition, polymorphisms. One lecture is devoted to evaluating the quality and reliability of genetic trees. Furthermore, the course describes genetic variability within populations, ways of measuring this variability, and various models of population genetics. Special attention is paid to the main evolutionary forces that shape this population variability (genetic drift, isolation, migration, selection). Clinical applications include the detection of mutations in both germline and somatic lineage and their link to human diseases.

Syllabus

  1. Molecular phylogenetics analysis
  2. Molecular evolution, evolution of genomes
  3. Molecular clocks, UPGMA, WPGMA, neighbour joining
  4. Phylogenetic tree, evolution models
  5. Methods for phylogenetic tree reconstruction
  6. Tree quality assessment
  7. Human genome, population and genetic variability
  8. Levels of genetic variability, Hardy-Weinberg equilibrium
  9. Genetic drift, migration, isolation
  10. Computational methods for detection of selection
  11. Short genetic variation and their detection
  12. Genetic diseases, mutations in germinal and somatic cells
  13. Large structural variants
  14. Annotation and interpretation of genetic variants
 2/0/0 Zk 3
M143005 Bioinformatics Applications

Annotation

The aim of the course is to describe applications of bioinformatics in various scientific fields, and to defined their synergy. Discussed will be bioinformatics projects in imunology, pharmacy, and in other life science disciplines. Students will get acquainted with legal, ethical and commercial issues in bioinformatics, and will be informed about authorship and intellectual property rights. Important international bioinformatics integration proojects will also be introduced.

Syllabus

  1. Bioinformatics in biology
  2. Bioinformatics in Life Sciences - conventions, data resources, projects
  3. Bioinformatics in pharmacology
  4. Bioinformatics in imunology
  5. Bioinformatics in agriculture
  6. Bioinformatics in forestry and in environment protection
  7. Geoinformatics
  8. Legal, ethical, and commercial aspects of bioinformatics I
  9. Legal, ethical, and commercial aspects of bioinformatics II
  10. Biosensing
  11. Bioinformatics applications in medicine
  12. European integrative bioinformatics projects
  13. Worldwide integrative bioinformatics projects
  14. Bioinformatics in education
 2/0/0 Zk 3
M143006 Case Studies in Bioinformatics

Annotation

This course summarizes acquired bioinformatics knowledge, and puts bioinformatics skills into a context. Lecturers will demonstrate individual methods and approaches using well documented and well performed experiments. Possible solutions will be compared, and their efficiency will be discussed.

Syllabus

  1. Introduction
  2. Bacterial genome assembly - Achromobacter
  3. Eukaryotic genome project I - Mastigamoeba, assembly from z 454, Illumina and PacBio sequences
  4. Eukaryotic genome project II - repeats and other problematic stretches, assembly evaluation
  5. Eukaryotic genome project III - gene prediction, annotation
  6. RNA-Seq of the known genome, analysis of gene expression
  7. Exploratory RNA-Seq of unknown genomes - Cobitis
  8. Analysis of gene expression of tumorous tissues using microarrays
  9. Metagenomics, analysis of poluted ground water
  10. Phylogenetic analysis of the SARS epidemic
  11. Comparative genomics, Burkholderia
  12. Epigenetic analysis, melanomes
  13. ENSEMBLE API
  14. Genotyping analysis, porphyria
 2/0/0 z 3
M143012 Pred-diploma Project

Annotation

The content of the subject is an idependent work on a selected research project at the Department of Informatics and Chemistry. The topic of the project is chosen by the student from the current offer of the institute. The student usually continues on this subject during the work on his diploma thesis. The aim of the subject is to involve students into the research of the department, to familiarize them with the fundamentals of scientific work and with the methods of data processing and analysis and to teach them how to present results of their work.

Syllabus

  1. Independent research work on a selected scientific project.
 0/5/0 kz 3
M500003 FIT Computational Intelligence Methods

Annotation

Students will understand methods and techniques of computational intelligence that are mostly nature-inspired, parallel by nature, and applicable to many problems. They will learn how these methods work and how to apply them to problems related to data mining, control, intelligen games, optimizations, etc.

Syllabus

  1. Introduction to computational intelligence, its uses.
  2. Algorithms of machine learning.
  3. Neural networks.
  4. Evolutionary algorithms, evolution of neural networks.
  5. Computational intelligence methods: for clustering, for classification, for modeling and prediction.
  6. Fuzzy logic.
  7. Swarms (PSO, ACO).
  8. Ensemble methods.
  9. Inductive modeling.
  10. Quantum and DNA computing.
  11. Case studies, new trends.
 2/1/0 z, Zk 4
M500004 FIT Data Preprocessing

Annotation

Students learn to prepare raw data for further processing and analysis. They learn what algorithms can be used to extract parameters from various data sources, such as images, texts, time series, etc., and learn the skills to apply these theoretical concepts to solve a specific problem in individual projects - e.g., parameter extraction from image data or from Internet.

Syllabus

  1. Data exploration, exploratory analysis techniques, visualization of raw data.
  2. Descriptive statistics.
  3. Methods to determine the relevance of features.
  4. Problems with data ? dimensionality, noise, outliers, inconsistency, missing values, non-numeric data.
  5. Data cleaning, transformation, imputing, discretization, binning.
  6. Reduction of data dimension.
  7. Reduction of data volume, class balancing.
  8. Feature extraction from text.
  9. Feature extraction from documents, web. Preprocessing of structured data.
  10. Feature extraction from time series.
  11. Feature extraction from images.
  12. Data preparation case studies.
  13. Automation of data preprocessing.
 2/1/0 z, Zk 4
Compulsorily optional courses
M320001 Biophysical Chemistry

Annotation

The lectures are focused on description of physical-chemical aspects of biological systems and use of physical-chemical terms to describe and solve the problems in biological systems. Students will have an overview of the most important concepts and methodology of modern biophysical chemistry as well as structural methods for description of biomacromolecules.

Syllabus

  1. Position of biophysical chemistry in the system of natural science
  2. Bioenergetics I
  3. Bioenergetics II (non-equilibrium thermodynamics)
  4. The importance of noncovalent interactions for biological systems
  5. The general characteristics of protein structure
  6. Methods of kinetics measurement of biological processes
  7. Pharmacokinetics
  8. Structure and function of biological membranes
  9. Biological processes associated with membranes: biochemistry of vision, electron transport chain and photosynthesis
  10. Electrochemical processes in biological systems
  11. The use of spectrophotometry in biochemical laboratory
  12. The use of radiochemistry for the study of biological processes
  13. Methods for the study of protein structure I: NMR and X-ray crystallography
  14. Methods for the study of protein structure II: circular dichroism and fluorescence spectroscopy
 2/1/0 Zk 4
M320034 Forensic genetics

Annotation

This course introduces students to the forensic DNA analysis as the dominant method of determining the origin of biological material. It describes biological principles on which the field is based, application circuits (criminology, kinship analysis, identification analysis, bioarcheology, recreational genetics), principles of analysis from the collection of biological material to final evaluation. It brings an overview of non-ID analysis and includes the basics of forensic statistics in the field of genetic analysis.

Syllabus

  1. Introduction to forensic genetics, forensic genetics history, relationship to forensic biology and serology
  2. Collection of biological traces and extraction of DNA from forensic samples
  3. charakterization of DNA - DNA quantification, determination of quality and species specificity
  4. Individuality as a complex concept, the principles of personal identification
  5. Repetitive sequences of the human genome, STR markers
  6. Methods of analysis on STR markers, the genetic profile analysis of composite samples
  7. Forensic analysis of gonozomal markers and mitochondrial DNA
  8. Population forensic genetics, forensic statistics
  9. Non-human typing - forensic analysis of fauna and flora, forensic microbiology
  10. Prediction of phenotype via DNA analysis and "non-ID" forensic DNA analysis
  11. Forensic genetics laboratory, DNA databasing
  12. Genogeography, genogenealogy, paleogenetics and biomolecular archaeology
  13. Quality in the laboratory
  14. Genetics as part of the forensic and investigative sciences and the Czech legal system
 2/2/0 z, Zk 5

summer semester

CodeCourse nameP/C/LExaminationCredits
Compulsory courses
M963001 Diploma Thesis 0/30/0 z 30