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Weiterführende Bioinformatik

Veranstaltungstyp Vorlesung und Übung
Dozent Prof HW Mewes
Zeit 08:30–11:00
Turnus wöchentlich vom 11.04.2016 bis zum 15.07.2016
Raum D 105, Richard-Wagner-Str. 10
Punkte 6 ECTS

TUM LV-Nr: 920584712

Zeitlicher Ablauf: Die Vorlesung mit Übung findet montags von 8:30 bis 10:00 und freitags 8.30-11.00 Uhr statt.

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Prüfung

  •  Die Prüfung wird mündlich abgehalten.

Inhalt

Der Vorlesungsteil wechselt mit dem Übungsteil ab. Zum Ende des Semesters (Juli)  folgt ein Übungs- und Projektteil. Für diese Lehrveranstaltung besteht Anwesenheitspflicht.

The lecture “Advanced Bioinformatics” aims to link bioinformatics to life science research. It introduces some basic concepts in science such as observation, experiment, induction, and deduction, as well as correlation and causation. The lecture is intended to be complemented by the lecture “Systems Biology of Diseases” that introduces to basic research applied to the challenge of common diseases such as diabetes, cancer, and neurodegeneration.

The lecture asks for your continuous collaboration; each week a topic will be introduced in Monday’s lecture. The lecture will provide material in form of references to publications. Students should compile additional papers, and give short summaries and comments in form of a common workspace available in form of a Blog open for contributions. Friday’s lecture will have 2 sections: the first will be a summary and comments in form of a lecture, the exercise part will have contributions and discussions provided by the students based on the Blog material.

Intended Learning Outcomes:  Understanding biological networks and their use in bioinformatics. Participants understand methods for the analysis of large scale –omics technologies (genomics, proteomics, transcriptomics, metabolomics, epigenomics, etc.). Information based bioinformatics explains the application of graph principles to biological networks such as metabolic, regulatory, or protein/protein interaction networks. Basics of systems biology (qualitative and quantitative models in biology).

The lecture covers i.a. the following contents:

  • Concepts and properties of graph based network analysis
  • Probabilistic networks
  • Sequence based methods for the systematic analysis of genomic information (pro- and eukaryotes)
  • SIMAP
  • PEDANT
  • ENCODE I und II
  • Protein/protein networks
  • Metabolic networks (static, transient, conditional)
  • Regulatory networks / expression analysis
  • Non-coding RNA
  • Epigenetics
  • Genetic variance and population based genomewide studies (GWAS)
  • High-throughput NGS sequence analysis

Voraussetzungen

Vorlesung und Übungen Bioinformatik I & II; Algorithmische Bioinformatik

Zielgruppe

Studenten im Hauptfach Bioinformatik