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Course: Computer Science | Level: graduate
SUBJECT: INTRODUCTION TO BIOINFORMATICS	DURATION - LECTURES: 15h LAB. EXC.: I5H
System: Full-time | Year: 5	Semester: 9
Lecturer: Tomasz Arodź, Ph.D.

Aims

Discoveries in molecular biology make it one of the most rapidly advancing areas of research. This progress would not be possible without sophisticated Computer algorithms. As biological datasets continue to expand, the importance of bioinformatics is set to grow further, making it one of the leading applications of Computer science. The goal of the proposed course is to introduce participants to main families of algorithms used in bioinformatics, as well as to get them acąuainted with their basie applications in solving problems encountered in analyzing biological data.

The course does not reąuire any prior knowledge of molecular biology.

Topics

1.    Basic structures and processes in molecular biology from Computer science perspective

a.    Genes as strings (DNA seąuences)

b.    Proteins as strings, resulting from genes through translation of DNA seąuences according to genetic codę, and as 3D structures

c.    Selected basie cellular processes viewed as information processing

d.    Functional and structural links between molecular entities as graphs and complex networks

2.    Algorithms for analyzing gene and protein seąuences

a.    Model s of evolution of gene/protein seąuences

b.    Dynamie programming in uncovering similarity and homology of genes/proteins

c.    Hidden Markov Models (HMM) in modeling properties of protein families

d.    Heuristic Solutions for speeding up ąueries in large databases of genes/proteins

3.    Graph algorithms for analyzing links between molecular structures and processes

a.    Methods for analyzing similarity of graphs

b.    Algorithms for identifying sensitive points in graphs (e.g. proteins that may result in disturbance of celi functioning when mutated)

c.    Machinę learning methods in bioinformatics

d.    Automatic detection of groups of related genes/proteins

e.    Supervised learning methods (e.g. artificial neural networks) in predicting gene/protein properties, related to e.g. structure or function

4.    Selected applications of the presented algorithms and methods

a.    Discovery of groups of genes related to a given illness based on DNA microarrays

b.    Predicting activity of drugs in computer-aided drug design

Objectives

During the course students should acąuire knowledge allowing them to navigate through essential algorithms and problems of bioinformatics. In result, participants should be able to explore on their own morę advanced methods and applications they may encounter as potential futurę co-developers of software targeted at life Sciences.