Computational Systems Biology: Integrative Analysis of Genomes, Proteomes, and Beyond

Type Lecture
ECTS 4.0  (to be approved)
Lecturer Dr. Arthur Dong, Dr. Shaila Roessle, Dr. Edda Kloppmann
Time Thursday, 10:30 - 12:00
Room  MI 01.09.034
Language English

 

Announcements:

No lecture on 03.11.2011

No lecture on 01.12.2011

Content

Traditional molecular biology follows the reductionist paradigm of "one gene, one protein, one function". However, with the ever increasing amount of data generated through genome-scale experiments, it becomes clear that "the whole is more than the sum of the parts".

In this lecture, we introduce the exciting new field of computational systems biology, which attempts to integrates orthogonal data to understand biological systems as a whole. The lecture can be divided into three parts.

In part one, we overview the field and present the big picture and motivation. A primer of molecular biology is included to make sure that all course participants, especially those with purely computer science background, have the requisite vocabulary. To conclude, we show how traditional sequence analysis can play a role in systems biology.

Part two focuses on protein structure. While systems biology itself looks at the big picture, for a complete understanding one still has to go down to the individual building blocks. "Parts" and "whole" are really the two sides of the same coin. With the facility to freely navigate between the two, one will understand both at a deeper level.

Part three is "proper" network-based systems biology. The prime focus is protein-protein interaction networks, but others such as gene-regulatory and metabolic networks are also presented. While we provide the necessary theoretical background on networks, the focus is on addressing significant biological questions and conducting cutting-edge research. Finally, we introduce pathogen-host interactions as a first example of integrating multiple systems.

The course is aimed at Master students, but should be accessible to Bachelor students as well. 

Topics and Readings

2011.10.20 Lecture 1: Overview and Motivation

 

2011.10.27 Lecture 2: From regular graphs to complex networks

Literature:

  • Collective dynamics of 'small-world' networks. Nature 393, 440-442 (4 June 1998)
  • Emergence of Scaling in Random Networks. Science 15 October 1999: Vol. 286. no. 5439, pp. 509 - 512

 

2011.11.10 Lecture 3: Further properties of complex (biological) networks

Literature:

  • Error and attack tolerance of complex networks. Nature 406, 378-382 (27 July 2000)
  • Specificity and Stability in Topology of Protein Networks. Science 3 May 2002: Vol. 296 no. 5569 pp. 910-913

 

2011.11.17 Lecture 4: Hierarchical Networks and Network Motifs

Literature:

  • Hierarchical Organization of Modularity in Metabolic Networks. Science 30 August 2002: Vol. 297 no. 5586 pp. 1551-1555
  • Network Motifs: Simple Building Blocks of Complex Networks. Science 2002 Oct 25;298(5594):824-7

 

2011.11.24 Lecture 5: Systems Biology of Virus-Host Interactions

Literature:

  • Herpesviral Protein Networks and Their Interaction with the Human Proteome. Science 13 January 2006: Vol. 311 no. 5758 pp. 239-242
  • Epstein-Barr virus and virus human protein interaction maps. PNAS May 1, 2007: vol. 104 no. 18 7606-7611

 

2011.12.08 Lecture 6: The dynamics of protein interaction networks

Literature:

  • Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 2004 Jul 1;430(6995):88-93
  • Dynamic Complex Formation During the Yeast Cell Cycle. Science 4 February 2005: Vol. 307 no. 5710 pp. 724-727

Lecture Slides WS 2011/2012

CSB_TUM_Arthur20111020_Lec01.pdf

Lecture 1 (2011.10.20)

3.8 M

CSB_TUM_Arthur20111027_Lec02.pdf

Lecture 2 (2011.10.27)

0.9 M

CSB_TUM_Arthur20111110_Lec03.pdf

Lecture 3 (2011.11.10)

599 K

CSB_TUM_Arthur20111117_Lec04.pdf

Lecture 4 (2011.11.17)

409 K