1. Stochastische Genexpression
Genetische Schalter und Multistabilität
Vorlesung
System-Biophysik
18. Dez. 2007
Literatur
Kaern et al. Nature Reviews Genetics Vol.6 p.451 (2005)
Ozbudak, Oudenaarden et al (2004) Multistability in the lactose utilization network of Escherichia coli, Nature 427, p737

2. Das Operon-Modell Francois Jacob und Jaques Monod, 1961
Operon: Genetische Funktionseinheit, die aus regulierten Genen mit verwandter Funktion besteht und enthält:
Promotor: Bindungsstelle für RNA-Polymerase
Operator: kontrolliert Zugang der RNA-Polymerase zu Strukturgen
Strukturgene: Polypeptide codierende Gene
zusätzlich:
Regulatorgen: codiert Repressor
Campbell, N.A., Biology

3. A Transkription-Aktivator and a Transkription-Repressor control the lac-Operon

4. Genregulation and boolean networks
from Weiss, 2000

5. Boolean expression of the Lac-Operon

6. Genetische Netze
Genregulatorisches Protein
translation
transcription

7. Transkription factors show cooperativity (e.g. by dimer-formation)
Cooperative binding

8. Wiederholung

9. Genregulation and boolean Network
from Weiss, 2000

10. (Nature, Dec 99) Let us now move on to modules.
A number of well-know biologists published this programmatic essay in Nature about 4 years ago – and it has had a strong influence on further development in the field. I have highlighted a couple of statements – namely,...
They point explicitly to the need to learn from the synthetic sciences such as computer science and engineering, especially in terms of the concept of function.

11. the genetic Toggle Switch (Flip-Flop)

13. Weiss et al.

14. the repressilator (genetic oscillator)

15. Circadian Rhythm – the biological clock
[Latein. circa about + dies a day]
Genetically controlled oscillation of about 24 hours,
adapted to the day-night rythm .
A single gen-mutation is responsible for the familial advanced sleep phase syndrome, FASPS.
Der 24h Rythm is robust, the phase is coupled to the light/dark cycle.

16. Die circadiane Uhr at Drosophila
Two proteins Per (Period) and Tim (timeless) regulate each other and form a dimere complex. Monomeres of Per in the nucleus supresses expression. The kinase DBT (double time) phosphorylates und degrades Per.
The complex fromation Per/Tim supports the entry in the nucleus and stays stable there for 8-10h. This slows down the feedback loop.

17. Decelerated negative feedback via
dclk (dclock) (degradation) and
dbt (doubletime) (Transport)
Synchronization (Entrainment) due to sunlight dependent TIM degradation rate

18. A couple of phosporilation steps are part of deceleration mechanism

19. What is life ?
Schrödinger considered 1943 the consequences of the molecular nature of the genetic code in a lecture about „Physics and biology“
1. How can „biological order“ (life) be explaind by the basic laws of physics?
2. How does life deal with the statistic nature of molecular interactions?
„... wenn wir so empfindliche Organismen wären, daß ein einzelnes Atom oder meinet-wegen ein paar Atome einen wahrnehmbaren Eindruck auf unsere Sinnesorgane machen könnten - du lieber Himmel, wie sähe das Leben dann aus!“

20. The importance of statistical fluctuations in biology
Noise can be increased with „positive feedback loops“
with advandtages:
In a fluctuating environment, heterogeneous cell populations have better chances to grow. (e.g. control of lac.operon, immune system, lysis-networks of lambda-phage)
Diversification in isogene phenotypes und celltypes (e.g. stem cell diversification)
Efficiency increase in signal transduction (e.g. chemotaxis regulation oder stochastic resonance (ears))
Noise can be decreased via „negative feedback loops“
Stabilisation of metabolics / homeostasis

21. Biochemical noise: fluctuation of protein concentration
Noise in the expression:
Small numbers of copies of many components e.g. Polymerases, regolatory proteins,  Stochastic effects in gene expression play an important role for variations of protein concentrations of bacteria wit identical genes
 Asymetries emerge, which are amplified by feedback loops and influence the development of the cell.

22. Deterministic model of gene expression
from JJ Collins, Nature Reviews 2005

23. Definitions for noise Variance Distribution noise
z: number of data points
Noise amplitude decreases with increasing
number of particles!
Rao, Wolf,Arkin, Nature 2002

24. finite size effect : mean value : standard deviation (noise)
0.1µM corresponds to 30 molecules/bacterium
: mean value
: standard deviation
(noise)
Decrease of the transcription rate and cell volume with equal factors keeps the protein level constant, but increases noise

25. „Translational bursting“
beschreibt den Effekt, dass ein Heraufsetzen der translationsrate auch die
Fluktuationen verstärkt.
Herabsetzen
von Transkriptionsrate und
Zellvolumen
Proteinlevel konstant
Fluktuationen erhöht

26. Slow promotors increase noise
low promotor rate
Transcriptional bursting
High transcription rate

27. Noise models Set of differntial equations (deterministic):
Set of differential-equations (stochastic)
Langevin equations:
C: concentrations, t: time, v: stoichiometric matrix, r: rates, x(t): white noise
Probability density function
Simulation for isomerisation :
example isomerisation with
k1 = k2 = 1s-1 k1 k2
state A
state B

28. Experiment: stochastisc Gen-Expression
Distinguish between „intrinsic noise“ (e.g. gene expression) and „extrinsic noise“(e.g. other cell components as RNA polymerase)
Idea for an exeriment:
Gene for CFP (green fluorescent Protein) und YFP (rot fluorecent Protein) are controlled by the same promotor, hence the mean concentration of CFP and YFP is equal
=> Expression probability should differ only due to intrinsic noise
A: no intrinsic noise => noise is correlated red+green=yellow
B: intrinsic noise => noise not correlated, different colors
Elowitz, M. et al, Science 2002

29. Stochastische Genexpression in einer einzelnen Zelle
Elowitz, M. et al, Science 2002
Two distinguishable genes (CFP and YFP) controlled by the same promotor
Low induction:
(low fluorescence)
high noise
High induction :
(high fluorescene)
Low noise

30. Stochastic gene expression
Extrinsic noise:
cell to cell variance of expression
(noise)
Intrinsic noise:
inherent stochasticity at identical external conditions
Intrinsisch:
(Maß für die Abweichung zweier identischer Gene unter gleichen intrazellulären Bedingungen)

31. Elowitz et al. 2002

32. hte „intrinsisc noise“ decreases with increasing protein concentration
Elowitz, M. et al, Science 2002