SUBJECT
Introduction to Systems Biology
lecture
master
2
Semester 2
Spring semester
1. Introduction. What is system biology? Genomics, proteomics and other “omics” sciences. Genome projects. Hypothesis-driven vs. data-driven (high-throughput) molecular biology.
2. Proteomics as concept and experimental strategy. Central role of proteomics in system biology. Paradigm shift in biology and its consequences. What we can expect from proteomics: basic research, pharma industry, diagnostics.
3. Experimental design: sample preparation, pre-separation techniques, cellular fractionation, laser capture dissection.
4. Protein separation techniques for proteomics application. 2D-HPLC, 2D-electrophoresis, differential electrophoresis.
5. Protein mass spectrometry. Ion sources, ES, MALDI, SELDI, IT. Detectors, Q, Q-TOF, Q-TOF-TOF. Protein fragmentors. MS sequencing. Main properties of equipments used in proteomics: advantages and disadvantages.
6. Protein identification by bioinformatics: MASCOT, TrEmbl. Data interpretation: CYTOSCAPE. Agilent Literature Search Engine, Virtual2D. Protein interaction databases. Validation methods. Common genome-proteome platform. Metabolome-proteome platform.
7. Chip technology in functional genomics. Hybridization techniques. Manufacturing, application, and limits of DNA chips. Transcriptome studies. Protein and small molecule chips. Data evaluation by bioinformatics methods.
8. Interactomics and its methodological ground. Yeast (and other) two- (and three-) hybrid methods. In vitro and in vivo interaction methods based on affinity chromatography.
9. In vitro evolution methods. Introduction to phage display. Preparation of protein and peptide libraries, and their use for localizing interaction surfaces and their energetics.
10. Network science. Definition and characterization and graphs. Erdős-Rényi graph, small world and scale-independent models. Graph motifs.
11. Biological networks. Molecular biology data in graphs. Protein-protein interaction networks. Protein association pathways. Gene ontology. Transcriptional regulation networks.
12. Clustering of biological networks (identification of clusters). K-means, self-organizing maps, hierarchic clusters, network modules.
13. Functional analysis of S. cerevisiae genetic networks. Characterization of metabolic networks: topology and analysis, flux distribution and its consequences, modularity.
14. Proteome analysis in E. coli. Description and analysis of interactions. Fractal networks.
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Richard Twyman: Principles of Proteomics, Garland Science, 2004
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Edda Klipp, et al.: Systems Biology, John Wiley & Sons, 2013