Introduction to Systems Biology; MSc

• understand and explain the functional levels of biological systems, e.g. genes, transcripts, proteins, protein complexes, pathways, cells, tissues, etc.
• understand and explain the theoretical foundation of network biology driven approaches for analysis of “omics” data.
• understand and apply protein-protein interaction data for functional analysis of biological systems.
• apply network visualizations in R.
• understand, apply, analyze, and interpret different methods for gene set enrichment analysis (GSEA).
• understand and apply the gene ontology (GO) for GSEA.
• understand, apply and analyze relevant information from the UniProt and NCBI databases.
• understand, apply, analyze, and evaluate network-based methods for integration of “omics” data for biomedical research.

Introduction:
* Introduction to systems biology, the motivation for applying network analysis to biological problems.
* Experimental data behind protein-protein interaction networks. Pros and cons of different technologies.
* Introduction to network analysis, including topology-based analysis of biological networks, key topological metrics, and algorithms for identification of communities in graphs.

Basic Systems Biology Research:
* Introduction to core components of functional regulation.
* Visualizing regulatory networks.
* Introduction to transcriptomics data – how to overlay expression data with networks.
* Combining temporal (time-series) expression data with molecular networks, to discover modes of functional regulation.

Applied Systems Biology in Biomedical Research:
* Combining protein-protein interaction data from multiple species to form an inferred human interactome.
* The concepts of virtual pulldowns and relevance scored networks (0th and 1st order filtering).
* The importance of protein isoforms in a systems biology context.
* Analysis of molecular networks related to the functional characteristics of different human diseases.

27002/27008/27022/27026/36611/22111/22100, A working knowledge of the programming language R -OR- parallel participation in course 22100 – R for Bio Data Science. Note that solving exercises both at lectures and the exam requires programming in R.

Knowledge of cell structure, its biological function, subcomponents, biochemical and molecular processes (metabolism, RNA- and protein synthesis), structure of DNA, protein-coding genes, the standard genetic code, the primary (sequence), secondary (helices/coils), tertiary (3D) and quaternary (complex) structure of proteins.

Basic knowledge and ability to apply bioinformatics methods and the knowledge to collect and process relevant biological data from large-scale databases such as UniProt and GenBank.

Lectures and exercises.

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.