LMU, Department of Statistics, Summer 2006:

Modeling, Simulation and Inference of Complex Biological Systems

Korbinian Strimmer
Statistics and Computational Biology


First meeting: Friday 28 April 2006, 11:15am.
Presentations: Session A: 9 June 2006
Session B: 23 June 2006
Session C: 7 July 2006
Session D: 14 July 2006
(Friday afternoon, 2-6pm).
Place: Seminar room, Dept. of Statistics, Ludwigstr. 33.
Registration: Send email to Korbinian Strimmer.



In this seminar we discuss challenges and problems in the statistical modeling and inference of complex high-dimensional dynamical systems. The main topics are:

  1. High-dimensional models for complex dynamic systems (time series models, graphical models, stochastic differential equations etc.),
  2. statistical approaches for their efficient (regularized) inference and model selection, and
  3. application to biological data (mostly related to systems biology).

See the PDF flyer and the detailed reading list for further information.


Prerequisites and Requirements:

Knowledge of multivariate analysis, time series and some bits of Bayesian inference and penalized likelihood inference is advantageous.

In order to obtain a certificate ("Schein") you need to

  • present a talk (45 mins) on the chosen topic ("Referat"),
  • give a short talk (10 mins) on somebody else's topic ("Koreferat"),
  • hand in a written expose ("Ausarbeitung") about your topic (10-15 pages),
  • write all slides in English (preferentially using the LaTeX "beamer" class),
  • attend at all sessions, and
  • actively participate in the discussion following each talk.


Schedule and Program:

Click on the name of the main speaker for download of the slides!

Session A: Regularized inference (9 June 2006):

Main Speaker Co-Speaker Topic Materials
Christoph Knappik Sebastian Petry Stein-estimation, empirical Bayes Paper 1
Daniela Birkel Verena Zuber Regularized classification Papers 2 and 3
Simon Rückinger Elisabeth Gnatowski Computational tricks Paper 4, R code

Session B: Multiple testing and model selection (23 June 2006):

Main Speaker Co-Speaker Topic Materials
Susanne Sax Ewan Donnachie Local fdr theory Papers 5 and 6
- - Large-scale differential expression Paper 7
Elisabeth Gnatowski Katharina Schneider FDR and model selection Paper 8

Session C: Dynamic models (7 July 2006):

Main Speaker Co-Speaker Topic Materials
Katharina Schneider Susanne Sax Inference of high-dimensional VAR model Paper 9
Jean Hausser - State-space models inferred by variational Bayes Paper 10
Verena Zuber Daniela Birkel Random walk and diffusion models Paper 11 including supplements 1, 2, 3, and 4

Session D: Graphical models (14 July 2006):

Main Speaker Co-Speaker Topic Materials
Sebastian Petry Simon Rückinger Graphical models for time series data Paper 12
- - Constructing large-scale graphical models Paper 13
Ewan Donnachie Christoph Knappik Spirtes PC algorithm Paper 14



  1. Efron, B., and C. Morris. 1975. Data analysis using Stein's estimator and its generalizations. JASA 70:311-319.
  2. Friedman, J.H. 1989. Regularized discriminant analysis. JASA 84:165-175.
  3. Tibshirani, R, et al. 2002. Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 99:6567-6572.
  4. Hastie, T, and R. Tibshirani. 2004. Efficient quadratic regularization for expression arrays. Biostatistics 5:329-340.
  5. Efron, B. 2004. Large-scale simultaneous hypothesis testing: the choice of a null-hypothesis. JASA 99:96-104.
  6. Efron, B. 2005. Local false discovery rates. Preprint.
  7. Lönnstedt, I., and T. Britton. 2005. Hierarchical Bayes models for cDNA microarray gene expression. Biostatistics 6:279-291.
  8. Gosh, D., W. Chen, and T. Raguhathan. 2004. The false discovery rate: a variable selection procedure. Preprint.
  9. Ni, S. and D. Sun. 2005. Bayesian estimates for vector autoregressive models. J. Business. Economic Statistics. 23:105-117
  10. Beal, M.J., F. Falciani, Z. Gharamani, C. Rangel, and D. Wild. 2005. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21:349-356
  11. Brockmann, D., L. Hufnagel, and T. Geisel. 2006. The scaling laws of human travel. Nature 439:462-465.
  12. Bach, F. R., and M. I. Jordan. 2004. Learning graphical models for stationary time series. IEEE Transactions on Signal Processing 52:2189-2199.
  13. Jones, B., C. Carvalho, A. Dobra, C. Hans, C. Carter, and M. West. 2005. Experiments in stochastic computation for high-dimensional graphical models. Statistical Science 20:388-400
  14. Kalisch, M, and P. Bühlmann. 2006. Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Preprint.


Internet resources:

Further links regarding systems biology:


Last modified:
July 10, 2006