Biophysics of Systems Dieter Braun Lecture + Seminar Systems Biophysics 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 Master Program Biophysics: bio.physik.lmu.de

Macrophage hunts down Bacterium

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

Biosystems: Feedback Loops

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

What is a „Bio-System“ ? Networks Input Out- put 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

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,..

Signal Pathway in dictyostelium discoideum cAMP + b g Ga PIP2 PIP3 b g PI3K* PTEN RAS pleckstrin homology domain Rac/Cdc42 Cell polarization PH CRAC Actin polymerization Acetylcholin- Aktivierung

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“ The purpose of the above enumeration is not to showthat it is complex—this is something that all biologists know. The point is to showthat biology has finally reached a stage where it is conceptually possible to describe, define, and analyze cellular signaling at a molecular level. 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 G-protein cascades are routinely regarded as amplifiers, cooperative interactions as thresholding operations, and feedback inhibition is a classical analog configuration to introduce stability and linearity in a circuit response (Horowitz and Hill, 1989). Many of these concepts have been reviewed by Bray (1995). An important conceptual result of this kind of study is that the basic building block of signaling and genetic networks should be considered in terms of feedback loops rather than individual molecules. As expected from systems analysis, negative feedback gives rise to homeostasis or oscillations. Positive feedback loops can give rise to multistability, and this defines the possible states of the system. Feedback loops can be nested to give rise to a multitude of possible states. The process of development, for example, involves many sequential choices between alternative states, each maintained through its own feedback process.

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 mRNA !

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

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

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 Extrinsic Noise 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. 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 LacI Repressed LacI +Induced by IPTG Extrinsic Noise Intrinsic Noise Extrinsic Noise Elowitz, M. et al, Science 2002

Science, 307:1965 (2005)