Structural representations of macromolecular structures, the Protein Data Bank and the PDB file format. Quality assessment of experimentally solved macromolecular structures. Assignment of secondary structural elements in 3D structures. Detection of domains. Algorithms for 3D structure comparison and alignment, structural classification databases. Predicting protein function from structure, identifying functionally important residues and contacts. Introduction to protein structure prediction methods, from ’1D’ to full 3D predictions. Basics of protein:ligand docking. Inclusion of internal dynamics in structural representations. Principles of protein design. 

Advanced course in Bioinformatics. The aim of the course is to provide deeper and hands onknowledge in the fields of bioinformatics working with big data, especiallydealing with large scale sequencing data. DNA sequencing, covering the topicsof metagenomics, exome sequencing, RNA-seq, etc. Algorithms and statisticalmethods to gain novel information of biological data such as NGS algorithms(BWT, FM-index). Modern data representations and frameworks (i.e. networkanalysis docker containers, working in HPC environment, SPARK).  Understanding and designing capability ofcomplex pipelines. Modern machine learning algorithms in biology (CART, LASSO,T-SNE, PCA, permanova). R and Python data science platforms. Data visualisations. 

This course covers the following fundamentals of data mining: 1.  Input and output of data mining process; 2.  Task types (e.g., clustering, classification, numeric prediction, association rule mining); 3.  Evaluation; 4.  Selected algorithms; 5.  Pre-processing and post processing; 6.  Ensemble learning methods.  

The course is focusing on how to use a two-photon laser scanning microscope in research. This is a multitask subject, when the students are not just learn the theoretical basics of multifoton microscopy but also get some knowledge of the engineering side. Moreover they have to improve the retorical skills (presenting scientific articles) and do some labwork in the Two-photon Laboratory. During the semester the students will learn how a microscope build up and how to use in a neurobiological project.

The aim of the course is to understand the basics needed to understand and manage random fluctuations in natural phenomena. Introduction of the methodology of evaluation of research and measurement results. Acquire the knowledge needed to understand the scientific literature. 

In this course the students will become familiar with the new developments of neural engineering in the field of neuroprosthetic devices that can restore lost neural functions. These devices require direct interfaces with the peripheral and central nervous system. Some of these devices are already routinely used in the clinical practice like the cochlear prostheses for restoring hearing, others are still in the developmental or experimental phase. 

The goal of this course is to highlight recent results in systems biology-oriented applications.Local and guest lecturers will present the basic concepts and advanced researchtopics in key directions of systems biology. Discussed topics include:biological networks, network motifs, stochastic simulations, logical modelling,whole-cell models, metabolic networks, circadian rhythms, riboswitches, cancer and others.

Content of the course: Basic concepts of the theory of continuous time and discrete time dynamical systems (induced by ordinary differential equations and continuous mappings, respectively): well-posedness of problems in differential equations, linearization  near hyperbolic equilibria, stability and attraction for compact invariant sets, structural stability and bifurcations, chaos and fractals with indicators and applications, synchronization between two chaotic Chua circuits, elements of time-series analysis.

Objective of the course: Dynamical systems as a basic model for describing spatiotemporal processes, their numerics, and related computer exercises. In addition to basic concepts of nonlinear dynamics, the emphasis is laid on error estimates between exact and approximate solutions, on the preservation of  qualitative properties of the dynamics by numerical approximations as well as on developing a critical attitude to results provided by the computer.