460-6029/02 – Biomarkers and Computational proteomics (VP)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantordoc. MUDr. Vít Procházka, Ph.D.Subject version guarantordoc. MUDr. Vít Procházka, Ph.D.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
DYS0006 Mgr. Tereza Dýšková, Ph.D.
FIL0152 Ing. Regina Fillerová, Ph.D.
KRI0203 doc. Dr. Ing. Eva Kriegová
MIK0424 Mgr. Zuzana Mikulková, Ph.D.
PRO0283 doc. MUDr. Vít Procházka, Ph.D.
SCH0341 Mgr. Petra Schneiderová
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

Subject aims expressed by acquired skills and competences

The aim of the course is to deepen students' knowledge in the field of proteomics, with special emphasis to biomarkers, analysis and interpretation of data generated in proteomic experiments. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of its Ph.D. studies and dissertation work.

Teaching methods

Individual consultations
Project work
Other activities


The course focuses on biomarkers, their validation and verification and their use in biomedicine. Students will be acquainted with methods and tools used in computational biology and proteomics (alignment of sequences and structures, protein structure prediction, protein folding, protein-protein interaction, protein design and modeling). Qualitative and quantitative methods of protein detection, their importance and use in biomedicine will be discussed and the impact of changes on selected diseases and complications discussed. The aim is to help students quickly cope with proteomics, their clinical use and interpretation of proteomic data, and be able to use computational tools to solve problems in their own research. Examples and practical examples of analyzes of relevant data sets and practical use of proteomics and proteome analyzes in biomedicine will be discussed.

Compulsory literature:

• Barh D, Carpi A, Verma M, Gunduz M. Cancer Biomarkers: Minimal and Noninvasive Early Diagnosis and Prognosis. 1st Edition (2017) CRC Press • Series Editors: Cohen IR, Lajtha A, Lambris JD, Pailetti R, Rezaei N. Advances in Experimental Medicine and Biology. Springer Nature International Publishing AG. ISSN: 0065-2598 • Twyman, R. M. Principles of Proteomics, 2nd Edition (2013), Garland Science, New York • Lovaric, J. Introducing Proteomics (2011), Wiley-Blackwell, Hoboken, New Jersey

Recommended literature:

• Goh W.W., Wong, L. Computational proteomics: designing a comprehensive analytical strategy. Drug Discov Today. 2014.

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.


Other requirements

The student prepares and presents the work on a given topic.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

• biomarkers and their use in medicine • validation and verification of biomarkers • the use of proteomics and proteome analyzes in clinical applications • identification and characterization of proteins (detection methods, de novo sequencing, significance testing, post-translational modifications, protein folding and degradation, protein complexes) • qualitative and quantitative protein analysis, protein detection methods • protein structure prediction, protein structure modeling • clinically important proteins in health and disease • selected chapters from clinical haemato-oncology and immunology, non-invasive biomarkers • proteomic databases • analysis and interpretation of data generated in proteomic experiments

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner