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

2. Macrophage hunts down Bacterium

3. 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?

4. Biosystems: Feedback Loops

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

6. 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

7. 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,..

8. 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

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

10. 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

11. How to Approach Complexity

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

13. 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.

14. Biological Signalnetworks are Combinatorical

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

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

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

18. Stochastic Genes
From Concentrations to Probabilities

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

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

21. 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

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

23. 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

24. Stochastic Gen-Expression
Unrepressed LacI
Repressed LacI
+Induced by IPTG
Extrinsic Noise
Intrinsic Noise
Extrinsic Noise
Elowitz, M. et al, Science 2002

25. Science, 307:1965 (2005)