1. Biophysics of Systems Dieter Braun Systems Biophysics Master Program Biophysics: http://www.physik.uni-muenchen.de/studium/ studiengaenge/master_physik/ma_phys_bio/curriculum.html Lecture + Seminar Di 10.15-13.30 Uhr Website of Lecture: http://www.physik.uni- muenchen.de/lehre/vorlesungen/sose_10/ Biophysics_of_Systems/index.html

2. Content: Biophysics of Systems 20.4. Introduction 27.4. Evolution Part 1 4.5. Evolution Part 2 11.5. Gene Regulation and stochastic effects in regulatory networks 18.5. Pattern formation 25.5. Modelling of biochemical networks 1.6. No Lecture (Pfingstdienstag) 8.6. Bacterial Chemotaxis 15.6. Chemotaxis of Eukaryotes 22.6. Regulation using RNA 29.6. High Throughput Methods of Systems Biology 6.7. Game theory and evolution 13.2. Oral exams (15 minutes per student)

3. Macrophage hunts down Bacterium

4. A physical view of the (eukaryotic) cell Macromolecules –5 Billion Proteins 5,000 to 10,000 different species –1 meter of DNA with Several Billion bases –60 Million tRNAs –700,000 mRNAs Organelles –4 Million Ribosomes –30,000 Proteasomes –Dozens of Mitochondria Chemical Pathways –Vast numbers –Tightly coupled How is a useful approach possible? www.people.virginia.edu/~rjh9u/cell1.html

5. Biosystems: Feedback Loops

6. Regulation Cell-Cell Communication RNA Interference Protein-Interactions Reaction Networks Organelles Epigenetics Promotors, Inhibitors Amplification DiffusionNoise Compartments Biosystems: Feedback Loops

7. What is a „Bio-System“ ? Input Out- put * Komponents (Molecules, Proteins, RNA...) * Network-like Connections (kinetic Rates) * Substructures (Knots, Module) * Functional Input-Output-Relations * Finding building principles (reverse engineering) (also: tracking how evolution has build it) Quantitative Models to describe the system Test the model with experimental data Prediction of the System behavior Networks Goal

8. Systems Biology Definition Systems Biology integrates experimental and modeling approaches to study the structure and dynamical properties of biological systems It aims at quantitative experimental results and building predictive models and simulations of these systems. Current primary focus is the cell and its subsystems, but the „systems perspective“ will be extended to tissues, organs, organisms, populations, ecosystems,..

9. b g GaGa Signal Pathway in dictyostelium discoideum PIP 2 PIP 3 CRAC cAMP PI3K* bg PH PTEN Rac/Cdc42 Actin polymerization RAS Cell polarization pleckstrin homology domain + Acetylcholin- Aktivierung

10. Levels of discription of the Signal Transduction Biochemical Rate Equations + Definition of Reaction Compartments + Diffusion Processes (Reakt.-Diff-Eq.) + Stochastic Description

11. Signal-Networks are „complex“ Connection Maps: Signal Transduction Knowledge Environment www.stke.org

12. How to Approach Complexity

13. Classical Approach: System Analysis - Quantitative Data Recording - Mathematical Modeling - Simulation - Comparison with Experiment

14. Useful analogy: Signaltransduktion and Elektronic Circuits

15. Biological Signalnetworks are Combinatorical

16. Modular view of the chemoattractant-induced signaling pathway in Dictyostelium Peter N. Devreotes et al. Annu. Rev. Cell Dev. Biol. 2004. 20:22

17. Hierarchical Structure of biologic Organisms (Z. Oltvai, A.-L. Barabasi, Science 10/25/02)

18. Modular Biology as advocated in the influential paper (Nature 402, Dec 1999)

19. Stochastic Genes From Concentrations to Probabilities

20. Stochastic Genes Inventory of an E-coli: do counting molecules matter? Note the low number of mRNA ! From Concentrations to Probabilities

21. Repetition: Gen-Expression With the Genes fixed: how can a bacteria adapt to the environment? Answer: Regulation of Gen-Expression

22. Repressors & Inducers Inducers that inactivate repressors: –IPTG (Isopropylthio-ß-galactoside)  Lac repressor –aTc (Anhydrotetracycline)  Tet repressor Use as a logical Implies gate: (NOT R) OR I operatorpromoter gene RNA P active repressor operator promoter gene RNA P inactive repressor inducer no transcription transcription Repressor Inducer Output

23. The Effect of Small Numbers e.g. by reducing the transkription rate or the cell volume => Protein levels are constant, but the fluktuations increase

24. Search for differences between intrinsic noise from biochemical processes of e.g. Gen-Expression) and extrinsic noise from fluctuations of other cell compartments, e.g. the conzentration of RNA Polymerase. Idea of Experiment: Gene for CFP (cyan fluorescence protein) und YFP (yellow fluorescence protein) are controlled by the same, equal promotor, i.e. the average concentration of CFP und YFP are the same in a cell: differences are then attributed to intrinsic noise. A: no intrinsic noise => noise is correlated red+green=yellow B: intrinsic noise => Noise is uncorrelated, differenz colors Elowitz, M. et al, Science 2002 Intrinsic Noise Extrinsic Noise Intrinsic Noise Stochastic Gen-Expression

25. Elowitz, M. et al, Science 2002 Unrepressed LacIRepressed LacI+Induced by IPTG Intrinsic NoiseExtrinsic Noise Stochastic Gen-Expression

26. Science, 307:1965 (2005)