LMU, Department of Statistics, Summer 2004:

Stochastic Models of Complex Biological Systems

organized by Korbinian Strimmer and Leonhard Held

 
Time: Every Thursday in term, 4pm-6pm
Place: Seminar room, Dept. of Statistics, first floor, Ludwigstrasse 33 (near "Siegestor").
Info: Simply show up on introduction day (22 April) or email Korbinian Strimmer.

 

Overview:

This seminar is about a very hot topic in bioinformatics: statistical models for genetic networks and systems biology. In the style of a journal club we aim to discuss current papers about stochastic models dealing with complex and high-dimensional biological systems.

Please browse the list of the topics and the links to articles below for detailed information (please download PDF seminar flyer and list of references).

 

Prerequisites:

A background mathematical modeling, statistics and computer science is advantageous, as well as some knowledge about gene expression analysis (e.g. see our microarray analysis course in the winter term).

 

Schedule:

Download PDF file with the time table and literature list.

Day Topic Speaker
22.4.2004 Introduction and overview K. Strimmer, L. Held
29.4.2004 Structural equations models (ref. 1) A.-L. Boulesteix
06.5.2004 Bayesian networks (ref. 2) J. Schäfer
27.5.2004 State space models (ref. 3) L. Fahrmeir
03.6.2004 Large-scale graphical models (ref. 4) C. Best
17.6.2004 Network statistics (ref. 5 and ref. 6) R. Opgen-Rhein
24.6.2004 Evolutionary game theory in biochemical networks
(ref. 7 and ref. 8)		 B. Hellriegel
01.7.2004 Constraint-based modeling of bacterial networks
(ref. 9 and ref. 10)		 E. Mendoza
08.7.2004 Biochemical systems theory (ref. 11 and ref. 12) M. Höhle
15.7.2004 Reaction network inference (ref. 13) K. Thierfelder
22.7.2004 Evolution of protein networks (ref. 14 and ref. 15) G. Jobb

 

References:

All papers are available online:

  1. M. Xiong, J. Li and X. Fang. 2004. Identification of genetic networks. Genetics 166:1037-1052.
  2. N. Friedman. 2004. Inferring cellular networks using probabilistic graphical models. Science 303:799-805.
  3. C. Rangel et al. 2004. Modelling T-cell activation using gene expression profiling and state space models. Bioinformatics in press (go to "advance access" section).
  4. A. Dobra et al., M. West. 2004. Sparse graphical models for exploring gene expression data. J. Multiv. Analysis. in press (preprint).
  5. A.-L. Barabasi. 2004. Network biology: understanding the cell's functional organization. Nature Reviews Genetics 5:101-113.
  6. R. Albert and A.L. Barabas. 2002. Statistical mechanics of complex networks. Rev. Mod. Phys. 74:47-97.
  7. T. Pfeiffer et al. 2001. Cooperation and competition in the evolution of ATP-producing pathways. Science 292:504-507.
  8. T. Frick and S. Schuster. 2003. An example of the prisoner's dilemma in biochemistry. Naturwissenschaften 90:327-331.
  9. M.W. Covert et al. 2004. Integrating high-throughput and computational data elucidates bacterial networks. Nature 429:92-96
  10. J. Reed and B. 0. Palsson. 2003. Thirteen yeers of building constraint- based in silico models of Escherichia coli. J. Bact. 185:2692-2699
  11. D.J. Wilkinson. 2004. Stochastic systems biology (lecture notes)
  12. P.J.E. Goss and J. Peccoud. 2000. Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. PNAS 95:6750-6755.
  13. X.-J. Feng and H. Rabitz. 2004. Optimal identification of reaction networks. Biophys. J. 86:1270-1281.
  14. G. D. Amoutzias et al. 2004. Convergent evolution of gene networks by single-gene duplications in higher eukaryotes. EMBO Reports 5:274-279
  15. A. Wagner. 2003. How the global structure of protein interaction networks evolves. Proc. R. Soc. Lond B 270:457-466.

 

Internet resources:

Further links regarding systems biology models:

 

Last modified:
May 17, 2004