Computational Systems Biology: Integrative analysis of genomes, proteomes, and beyond (WS 12/13)

​

Type  Lecture
ECTS  3.0  (to be approved)
Lecturer Dr. Arthur Dong
Time Tuesday, 10:30 - 12:00
Room  MI 01.09.034 (Seminar Room)
Language English

 

Announcements:

No lecture on 2012.11.13 (SVV)

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. While we provide the necessary theoretical background on networks, the focus is on addressing significant biological questions and conducting cutting-edge research. You will critically read landmark papers in the field and learn to see the big picture and formulate your own questions.

The lecture can be divided into three parts:

In Part I, we present the basic notions of complex networks such as scale-free, small-world, degree correlation, and network motifs. For simplicity and biological intuition, we focus on protein interaction networks. The seminal papers we read for this section not only introduce concepts and methods used in most later papers, but also provide good examples of ground-breaking research. 

While the notions from Part I are fundamental, the days are long gone when one could publish a paper just based on "network X is scale-free". To gain deeper insights into the biological systems, one has to integrate multiple genome-scale datasets, which is the focus of Part II of the course. In this section, we go beyond single protein interaction networks by either docking multiple systems as in the case of virus-host interactions or integrating orthogonal information such mRNA profiling, genetic interactions, and SNPs. As a major application, we explore how networks can help us better understand human diseases.

In Part III, you will have the opportunity to conduct your own research. You will conceive your own project idea, carry out a preliminary feasibility study, and work out the key points/steps of your project.

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

Topics and Readings (tentative)

2012.10.16 Lecture 1: Overview and Motivation

 

2012.10.23 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

 

2012.10.30 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

 

2012.11.06 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

 

2012.11.20 Lecture 5: When proteomes collide -- 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

 

2012.11.27 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

 

2012.12.04 Lecture 7: Networks in human diseases

Literature:

  • The human disease network. PNAS 2007 May 22;104(21):8685-90
  • Network-based classification of breast cancer metastasis. Mol Syst Biol. 3:140 (2007)

 

2013.01.08 Guest Lecture (Andrea)

 

2013.01.15 Lecture 8: Chimps, Neandertals, and 1000 genomes

Literature:

  • A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (28 October 2010)
  • A Draft Sequence of the Neandertal Genome. Science 7 May 2010: Vol. 328 no. 5979 pp. 710-722
  • Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437, 69-87 (1 September 2005)

 

2013.01.22 Lecture 9: Hunting disease SNPs

Literature:

  • Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature447, 661-678 (7 June 2007)
  • IFITM3 restricts the morbidity and mortality associated with influenza. Nature 484, 519–523 (26 April 2012)

 

2013.01.29 Lecture 10: Genetic interactions and beyond

Literature:

  • Global Mapping of the Yeast Genetic Interaction Network. Science 6 February 2004: Vol. 303 no. 5659 pp. 808-813
  • Rewiring of Genetic Networks in Response to DNA Damage. Science 2010 December 3; 330(6009): 1385–1389

 

2013.02.12 (10:30 - 13:30) Student Project Presentations

 

Lecture Slides

CSB_TUM_Arthur20121016_Lec01.pdf

Lecture 1 (2012.10.16)

 4.6 M

CSB_TUM_Arthur20121023_Lec02.pdf

Lecture 2 (2012.10.23)

 0.9 M

CSB_TUM_Arthur20121030_Lec03.pdf

Lecture 3 (2012.10.30)

 598 K

CSB_TUM_Arthur20121106_Lec04.pdf

Lecture 4 (2012.11.06)

 408 K

CSB_TUM_Arthur20121120_Lec05.pdf

Lecture 5 (2012.11.20)

 1.6 M

CSB_TUM_Arthur20121127_Lec06.pdf

Lecture 6 (2012.11.27)

 1.1 M

CSB_TUM_Arthur20121211_Lec07.pdf

Lecture 7 (2012.12.11)

 1.9 M

Sample student projects from previous years

  • Mapping protein 3D structures to interaction networks
  • Functional prediction on protein 2D-structure similarity networks
  • Regulation of hubs by miRNAs and TFTs
  • Alternative splicing and post-translational modification of hubs
  • SNP frequencies in the human disease gene network
  • Comparison of synonymous and non-synonymous SNPs
  • Comparison of host-pathogen networks
  • Topological analysis of the human stem cell network

Lecture Videos WS 2010/2011

csb_20101021.wmv

Lecture 1 (10-21-2010)

 356 M

csb_20101028.wmv

Lecture 2 (10-28-2010)

 372 M

csb_20101104.mp4

Lecture 3 (11-04-2010)

 119 M

csb_20101118.mp4

Lecture 5 (11-18-2010)

 275 M

csb_20101125.mp4

Lecture 6 (11-25-2010)

 396 M

csb_20101209.wmv

Lecture 7 (12-09-2010)

 316 M

csb_20101216.wmv

Lecture 8 (12-16-2010)

 283 M

csb_20110113.wmv

Lecture 9 (01-13-2011)

 482 M

csb_20110120.mp4

Lecture 10 (01-20-2011)

 390 M