Specific programme courses of 2nd stage Bioinformatics (course group defined by Faculty of Mathematics, Informatics, and Mechanics)

• Classes - 45 hours
• Lecture - 15 hours

The subject is devoted to the presentation of the research strategy of biomolecular systems using the methods of theoretical physics and various computer simulation techniques. In particular, issues related to computer-aided drug design will be discussed. Basic and advanced methods such as molecular docking, molecular dynamics, quantum-classical molecular dynamics, free energy computation methods and non-equilibrium molecular-dynamics-based methods will be discussed. During the laboratory, students will carry out three projects, covering the most common problems raised in research in the field of computational biophysics.

• Classes - 30 hours
• Lecture - 30 hours

• Classes - 30 hours
• Lecture - 30 hours

The course is focused on using intelligent methods for solving problems that are difficult or impractical to solve with other methods. Accordingly, we discuss, among the others, various approaches based on heuristics, approximations, randomized, as well as deductive and inductive schemes of reasoning, often designed by analogy to the human way of problem solving. The main topics include also intelligent search through large spaces of states and solutions, intelligent game strategies, reasoning in logic and logical foundations of planning, foundations of machine learning in relation to artificial intelligence, foundations of modeling of uncertainty, as well as various specialized applications.

• Lab - 30 hours
• Lecture - 30 hours

• Lab - 30 hours
• Lecture - 30 hours

The aim of the course is to introduce the techniques of data analysis and visualization to the students.

• Lab - 30 hours
• Lecture - 30 hours

• Lab - 45 hours
• Lecture - 30 hours

The goal of the course is to show usage cases for deep neural networks. During the course state-of-the-art techniques, algorithms and tools will be presented. Among others two main blocks of the course will concern image classification and text processing.

• Lab - 30 hours
• Lecture - 30 hours

• Lab - 30 hours
• Lecture - 30 hours

• Lab - 30 hours
• Lecture - 30 hours

The aim of this course is to introduce students with a computer science and mathematics background to the problems of modern computational biology. The topics are focused on analysis of protein and nucleic acid sequences. Fundamental mathematical models and computational methods used in the description of molecular sequences will be presented.

• Classes - 30 hours
• Lecture - 30 hours

Lecture and exercises introduce the students to the subject and methodology of machine learning and modeling of artificial neural networks, and to solving practical problems with these tools. The lecture is intended for third-year students of Neuroinformatics.

• Lab - 60 hours
• Lecture - 30 hours

Cancer epidemiology as well as environmental and endogenous factors, which favor formation of tumors. Mechanisms of induction of mutations as causative factor of carcinogenic process will be discussed, as well as epigenetic mechanisms (DNA methylation, miRNA). DNA repair mechanisms will be presented as well as tumor cell characteristics, stages of carcinogenesis, major critical genes, dysfuction of which triggers carcinogenic process (oncogenes, tumor suppressor genes), apoptosis, proteolysis, angiogenesis and metastasis. Cancer stem cells. Role of viruses in cancer induction. Diagnostics and the basis of modern treatment methods; personal therapy.

• Lab - 90 hours

(in Polish) Zajęcia mają za zadanie zaznajomienie studentów z podstawami filogenetyki molekularnej oraz metagenomiki (analiza amplikonów), ze szczególnym uwzględnieniem ich zastosowania w badaniach środowiskowych. W ramach bloku dotyczącego filogenetyki studenci nauczą się przyrównywania sekwencji nukleotydowych i białkowych, poznają podstawowe metody rekonstrukcji drzew filogenetycznych oraz oceny ich mocy, a także zapoznają się z podstawami datowania filogenezy oraz metodami szacowania stanów ancestralnych. A ramach drugiej części zajęć nacisk zostanie położony na analizę danych środowiskowych z sekwencjonowania nowej generacji (NGS). Podczas zajęć samodzielnie przeprowadzą analizę od etapu uzyskania surowych danych, poprzez analizę ich jakości, łączenie odczytów, oraz klasyfikację taksonomiczna sekwencji, kończąc na analizach statystycznych umożliwiających powiązanie czynników środowiskowych z otrzymanymi wynikami składu gatunkowego i wskaźnikami różnorodności biologicznej.

• Lab - 90 hours

• Lab - 90 hours

In this course students get acquainted with the proteomic methodology

• Classes - 30 hours
• Lecture - 30 hours

• Classes - 30 hours
• Lecture - 30 hours

Lectures on theoretical foundations of stochastic analysis (Markov chains, Poisson process, birth and death processes, Master and Fokker-Planck equations will be integrated with concrete biological models on the micro level (gene expression and regulation, ion channels) and on the macro level (evolutionary game theory).

• Lab - 30 hours
• Lecture - 30 hours

• Lab - 30 hours
• Lecture - 30 hours

The goal of the course is to present deep learning architectures as well as to teach implementation, training and debugging own neural networks dedicated to visual recognition. Students gain theoretical knowledge, information on the state of the current research in the domain and obtain practical skills in visual recognition.