Biophysics of Systems Dieter Braun Systems Biophysics Lecture
Biophysics of Systems Dieter Braun Systems Biophysics Lecture + Seminar Di 10. 15 -13. 30 Uhr Website of Lecture: http: //www. physik. unimuenchen. de/lehre/vorlesungen/sose_10/ Biophysics_of_Systems/index. html Master Program Biophysics: http: //www. physik. uni-muenchen. de/studium/ studiengaenge/master_physik/ma_phys_bio/curriculum. html
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)
Macrophage hunts down Bacterium
A physical view of the (eukaryotic) cell • • www. people. virginia. edu/~rjh 9 u/cell 1. html Macromolecules – 5 Billion Proteins • 5, 000 to 10, 000 different species – 1 meter of DNA with Several Billion bases – 60 Million t. RNAs – 700, 000 m. RNAs Organelles – 4 Million Ribosomes – 30, 000 Proteasomes – Dozens of Mitochondria Chemical Pathways – Vast numbers – Tightly coupled How is a useful approach possible?
Biosystems: Feedback Loops
Biosystems: Feedback Loops Promotors, Protein-Interactions Inhibitors Regulation RNA Interference Compartments Epigenetics Reaction Networks Amplification Organelles Cell-Cell Communication Noise Diffusion
What is a „Bio-System“ ? Input Networks * Komponents (Molecules, Proteins, RNA. . . ) * Network-like Connections (kinetic Rates) * Substructures (Knots, Module) * Functional Input-Output-Relations Goal * 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 Output
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, organisms, populations, ecosystems, . .
Signal Pathway in dictyostelium discoideum c. AMP + b g Ga b g PI 3 K* PIP 3 PIP 2 PTEN RAS Cell polarization pleckstrin homology domain PH CRAC Acetylcholin. Aktivierung Rac/Cdc 42 Actin polymerization
Levels of discription of the Signal Transduction Biochemical Rate Equations + Definition of Reaction Compartments + Diffusion Processes (Reakt. -Diff-Eq. ) + Stochastic Description
Signal-Networks are „complex“ Connection Maps: Signal Transduction Knowledge Environment www. stke. org
How to Approach Complexity
Classical Approach: System Analysis - Quantitative Data Recording - Mathematical Modeling - Simulation - Comparison with Experiment
Useful analogy: Signaltransduktion and Elektronic Circuits
Biological Signalnetworks are Combinatorical
Modular view of the chemoattractant-induced signaling pathway in Dictyostelium Peter N. Devreotes et al. Annu. Rev. Cell Dev. Biol. 2004. 20: 22
Hierarchical Structure of biologic Organisms (Z. Oltvai, A. -L. Barabasi, Science 10/25/02)
Modular Biology as advocated in the influential paper (Nature 402, Dec 1999)
Stochastic Genes From Concentrations to Probabilities
Stochastic Genes From Concentrations to Probabilities Inventory of an E-coli: do counting molecules matter? Note the low number of m. RNA !
Repetition: Gen-Expression With the Genes fixed: how can a bacteria adapt to the environment? Answer: Regulation of Gen-Expression
Repressors & Inducers active repressor RNAP inactive repressor inducer no transcription RNAP promoter operator gene • Inducers that inactivate repressors: – IPTG (Isopropylthio-ß-galactoside) Lac repressor – a. Tc (Anhydrotetracycline) Tet repressor • Use as a logical Implies gate: Repressor Inducer Output (NOT R) OR I transcription
The Effect of Small Numbers e. g. by reducing the transkription rate or the cell volume => Protein levels are constant, but the fluktuations increase
Stochastic Gen-Expression Intrinsic Noise 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. Extrinsic Noise 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
Stochastic Gen-Expression Unrepressed Lac. I Extrinsic Noise Repressed Lac. I Intrinsic Noise +Induced by IPTG Extrinsic Noise Elowitz, M. et al, Science 2002
Science, 307: 1965 (2005)
- Slides: 26