Computational Systems Biology (SS 2014)

From sequencing the human genome and the genomes of other organisms we have a complete inventory of all molecules, which can directly be derived from the genome, i.e. all genes, proteins, and RNAs. Moreover the behaviour of cells on the transcription level can be observed via genome-wide gene expression measurements. The combination with other high-throughput techniques allows to investigate metabolic pathways, protein interactions, gene regulatory networks and signaling cascades.

The wealth of experimental data enables to investigate and model biological systems on the level of pathways and networks and, thus, on a higher level than individual macromolecules. Towards this end, Systems Biology perspectives need to be developed together with the necessary tools for constructing and analysing complex biological models.

The lecture gives an introduction and overview into the spectrum of algorithms and applications as well as the state-of-the-art of the systems biology field. In the associated exercise class the theoretical knowledge will be complemented via practical experiences with the problems and methods.

The following topics will be addressed

• Modelling of biological systems: tools and methods
• Petri Netze as a modelling framework
• Simulation via ordinary differential equations (ODE)
• Stochastic simulation
• Metabolic Control Analysis (MCA) and Flux Balance Analysis (FBA)
• Modelling of biological systems
• Network reconstruction and Learning in networks: Boolean and und Bayesian networks
• Evolution and self organisation

1. regular participation in the exercise class
2. 50% of credits in the (weekly) exercise
3. passing the written exam

Slides and exercise tasks for the lecture can be found here.

Systems Biology

• Edda Klipp, Herwig R., Kowald A., Wierling C., Lehrach H., Systems Biology in Practice, Wiley-VCH, 2005
• Zoltan Szallasi, Jrg Stelling, Vipul Periwal, System Modeling in Cellular Biology, MIT Press, 2006
• James W. Haefner, Modeling Biological Systems, Springer, 2006
• Bernhard O. Palsson, Systems Biology : Properties of Reconstructed Networks, Cambridge University Press, 2006

Complex Networks

• Duncan Watts, Small Worlds, Princeton University Press, 1999
• Albert-Laszlo Barabasi, Linked, Plume Books, 2003
• Mark Buchanan, Nexus, Norton, 2002
• Steven H. Strogatz, Nonlinear Dynamics and Chaos, 2001
• Steven H. Strogatz, Sync, Theia, 2004
• Duncan Watts, Six Degrees, Norton, 2004
• Bornholdt&Schuster, Handbook of Graphs and Networks, Wiley, 2004
  
• Review: R.Albert & A.-L.Barabasi, Statistical mechanics of complex networks, Reviews of modern physics, Vol 74, Jan. 2002

Petri Nets

• Bernd Baumgarten, Petri-Netze, Spektrum, 1996
• Priese&Wimmel, Petri-Netze, Springer, 2002
• Jrg Desel, Petrinetze, lineare Algebra, und lineare Programmierung, Teubner, 1998
• Wolfgang Reisig, Petri-Netze, Eine Einfhrung, Springer, 1985
• Wolfgang Reisig, Petri-Netze, Systementwurf mit Netzen, Springer, 1985
  
• Review: T. Murata, Petri nets: Properties, Analysis, and Applications, Proceedings of the IEEE, 1989

Bayesian Networks

• Finn V. Jensen, Bayesian Nwtworks and Decision Graphs, 1996
• Judea Pearl, Probabilistic Reasoning in Intelleigent Systems, Morgan Kaufman, 1988
• Neapolitan, Learning Bayesian Networks, Prentice Hall, 2003
• (Tom Mitchell, Machine Learning, Mac Graw Hill, 1997)
• (Hastie, Tibshirani, Friedman, The Elements of Statistical Learning, Springer, 2001)
  
• Review: D. Heckerman, A tutorial on Learning with Bayesian Networks, Microsoft Research TR, 1996