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Jožef Stefan
International
Postgraduate School

Jamova 39
SI-1000 Ljubljana
Slovenia

Phone: +386 1 477 31 00
Fax: +386 1 477 31 10
Email: info@mps.si

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Course Description

Computational Systems Biology

Program

Information and Communication Technologies, third-level study programme

Lecturers:

prof. dr. Kristina Gruden
prof. dr. Sašo Džeroski

Goals:

The course objective is to familiarize the student with the field of systems biology, including wet and dry lab methodologies.

The competencies of the students successfully completing this course include understanding of basic concepts from both areas, familiarity with state-of-the art methods, and capability of their use in practical problems.

Content:

Introduction:
The concept of systems biology

Wetlab approaches:
Transcriptomics, proteomics,
metabolomics

Measurements of biological systems dynamics

Analysis of " omics" data:
Data analysis tasks, analysis of single-
platform data, integrative data analysis,
machine learning and systems biology

Modeling the structure and dynamics of
biological networks:
Types of biological networks, network
modeling formalisms, modeling dynamic
systems, kinetic modeling in systems biology

Examples and case studies

Course literature:

Izbrana poglavja iz naslednjih knjig: / Selected chapters from the following books:

• O. Demin, and I. Goryanin, Kinetic Modelling in Systems Biology. Chapman & Hall/CRC, 2008. ISBN 978-1-5848-8667-9
• E. Klipp, W. Liebermeister, C. Wierling, and A. Kowald. Systems Biology: A Textbook. Wiley-VCH, 2009. ISBN 978-3-5273-1874-2
• S. Choi, Ed. Systems Biology for Signaling Networks. Springer, 2010. ISBN 978-1-4419-5796-2
• S. Džeroski, B. Goethals and P. Panov, Eds. Inductive Databases and Constraint-Based Data
Mining. Springer, 2010. ISBN 978-1-4419-7737-3

Significant publications and references:

• Radivojac, P., ..., Džeroski, S., et al. A large-scale evaluation of computational protein function prediction, Nature Methods 10(3):221-7, 2013.
• Carotenuto, M., ..., Džeroski, S., et al. Neuroblastoma tumorigenesis is regulated through the Nm23-H1/h-Prune C-terminal interaction. Scientific Reports, 3:1351, 2013.
• Škunca, N., Bošnjak, M., Kriško, A., Panov, P., Džeroski, S., Šmuc, T. Phyletic profiling with cliques of orthologs Is enhanced by signatures of paralogy relationships. PLOS Computational Biology, 9(1): e1002852, 2013.
• Podpečan, V., Lavrač, N., Mozetič, I., Kralj Novak, P., Trajkovski, I., Langhor, L., Kulovesi, K., Toivonen, H., Petek, M., Motaln, H., Gruden, K. SegMine workflows for semantic microarray data analysis in Orange4WS. BMC Bioinformatics, 12: 416-1-416-16, 2011.
• Miljkovic, D., Stare, T., Mozetič, I., Podpečan, V., Petek, M., Witek, K., Dermastia, M., Lavrač, N., Gruden, K. Signalling network construction for modelling plant defence response. PLoS ONE, 7(12): e51822, 2012.
• Ramšak, Ž., Baebler, Š., Rotter, A., Korbar, M., Mozetič, I., Usadel, B., Gruden, K. GoMapMan : integration, consolidation and visualization of plant gene annotations within the MapMan ontology. Nucleic Acids Research, 42: D1167-D1175, 2014.

Examination:

Oral exam (50%)
Seminar work with oral defense (50%)

Students obligations:

Oral exam
Seminar work and oral defense of seminar work.

Links: