Study programme: Undergraduate study “Biotechnology and drug research”

Course code: BIL103

ECTS points: 3

Course language: Croatian

Course prerequisites: None

Status: Required/Elective

Teacher: Vedran Miletić, Ph.D.

Teaching assistant: Slobodan Beliga, Ph.D.

Required reading

  1. Špoljarić, Pavle. Programski alati na Unix računalima (zbornik seminarskih radova), Sveučilište u Splitu, Zagreb, 2006.

  2. Content prepared for learning through a learning system with your own notes and course materials.

  1. Sterling, T., Anderson, M. & Brodowicz, M. High performance computing: modern systems and practices. (Morgan Kaufmann, an imprint of Elsevier, 2018).

  2. Eijkhout, V., Chow, E. & van de Geijn, R. Introduction to High Performance Scientific Computing. (Lulu, 2015).

  3. Cramer, C. J. Essentials of Computational Chemistry: Theories and Models, 2nd Edition. (Wiley, 2004).

  4. Jensen, F. Introduction to Computational Chemistry, 3rd Edition. (Wiley, 2017).

  5. Doleželová, M., Muehlfeld, M., Svistunov, M., Wadeley, S., Čapek, T., Hradílek, J., Silas, D., Heves, J., Kovář, P., Ondrejka, P., Bokoč, P., Prpič, M., Slobodová, E., Kopalová, E., Svoboda, M., O’Brien, D., Hideo, M., Domingo, D. & Ha, J. System administrator’s guide. (Red Hat, 2018).

  6. Original manuals from manufacturers and tutorials for the operating systems and software packages used on the exercises.

Course description

The course is designed to prepare biotechnology students for processing of the scientific data. As such, the course content is focused on:

  1. Identifying and upgrading basic IT knowledge acquired by students during secondary education regardless of the type of educational profile with the aim of equalizing IT knowledge

  2. Acquiring knowledge about the basic concepts of modern information technology: principles of computer system operation (hardware and software), computer networks and databases, supercomputers and parallel data processing, and licensing

  3. Introduction to advanced computer systems for computer chemistry, molecular modeling and bioinformatics that are used in biotechnology

Learning outcomes

Upon the completion of the course the students will be able to:

  1. Distinguish and explain the basic concepts of information and computer technology

  2. Define and distinguish between elements of a networked computer system (computer hardware and software)

  3. Explain how parallel data processing works on a supercomputer

  4. Categorize commonly used software and dataset licenses

  5. Search the databases used in computational chemistry, molecular modeling and bioinformatics

  6. Use basic operating system functions

  7. Distinguish between numerical and symbolic computation and choose the appropriate one for a given problem

Course content


  1. Historical development of computers. Hardware and software

  2. Historical development of the Internet and operating systems. Scientific software

  3. Supercomputers. Trends and directions of information and communication technology development

  4. Intellectual property over software and data. Licensing

  5. Computational chemistry, molecular modeling and bioinformatics


  1. Databases and existing software in computational chemistry, molecular modeling and bioinformatics (KEGG, ChEMBL, RCSB PDB, UniProt, GPCRdb, Avogadro, Open Babel, PyMOL, UGENE)

  2. Working with documents containing chemical molecular formulae and equations (LaTeX, Beamer, mhchem). Basics of programming (Python). Drawing structural molecular formulae (RDKit)

  3. Symbolic computation (SymPy). Numerical computation (NumPy)

  4. Drawing graphs (Matplotlib). Processing of structured data (pandas)

  5. Working with basic features of and setting up an operating system (Ubuntu, Fedora, Anaconda)