Structural Bioinformatics and Proteomics

1.

Subject title

Structural Bioinformatics and Proteomics

Структурна биоинформатика и протеомика

2.

Code

m23_w_006

3.

Study program

Cloud Computing, Data science in computer science and engineering, IT management, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, Bioinformatics, Bioinformatics,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Кире Триводалиев

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The student will be capable of analyzing protein structures, using them and extracting additional knowledge that would be applied to the prediction of the function and structure of unknown protein molecules. The student will be familiar with the basic principles of proteomics and ways to determine protein function using protein interaction networks.

11.

Subject content:

Proteins play a major role in the functioning of organisms. Knowing the protein structure and ways protein interacts can allow the development of additional knowledge and methods that would be used to predict the structure and function of protein. The structure of the subject is as follows: techniques for experimental determination of protein Structure (NMR spectroscopy, X-ray crystallography), protein structure formats, PDB files, structural-classification schemes (Cath, SCOP), prediction and alignment by structure, determination of structure function, comparative modeling, recognition. Experimental and computational methods for determining the interaction between protein. Modular analysis of protein interaction networks. Topological analysis of protein interaction networks. Statistical analysis and analysis based on machine learning on protein interaction networks. Integration of Geneontology in the analysis of networks of protein interactions.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 points

17.4.

Final exam

100 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

реализирани активности 15

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature