1. Deterministic and Stochastic Analysis of Simple Genetic Networks Adiel Loinger MS.c Thesis of under the supervision of Ofer Biham

2. Outline Introduction The Auto-repressor The Toggle Switch  The general switch  The BRD and PPI switches  The exclusive switch The Repressillator

3. Introduction Mice and men share 99% of their DNA Are we really so similar to mice?

4. Introduction The origin of the biological diversity is not only due the genetic code, but also due differences in the expression of genes in different cells and individuals Muscle Cells Bone Cells

5. Introduction The DNA contains the genetic code It is transcribed to the mRNA The mRNA is translated to a protein The proteins perform various biochemical tasks in the cell The Basics of Protein Synthesis

6. Introduction The transcription process is initiated by the binding of the RNA polymerase to the promoter If the promoter site is occupied by a protein, transcription is suppressed This is called repression Transcriptional Regulation In this way we get a network of interactions between proteins genetic network

7. Introduction The E. coli transcription network Taken from: Shen- Orr et al. Nature Genetics 31:64- 68(2002)

8. The Auto-repressor Protein A acts as a repressor to its own gene It can bind to the promoter of its own gene and suppress the transcription

9. Rate equations – Michaelis-Menten form Rate equations – Extended Set The Auto-repressor n = Hill Coefficient = Repression strength

10. The Auto-repressor Problems - Wrong dynamics Very crude formulation (unsuitable for more complex situations) Rate equations – Michaelis-Menten form Rate equations – Extended set Better than Michaelis-Menten But still has flaws: A mean field approach Does not account for fluctuations caused by the discrete nature of proteins and small copy number of binding sites

11. The Auto-repressor The Master Equation Probability for the cell to contain N A free proteins and N r bound proteins P(N A,N r ) :

12. The Auto-repressor The master and rate equations differ in steady state The differences are far more profound in more complex systems For example in the case of … Lipshtat, Perets, Balaban and Biham, Gene 347, 265 (2005)

13. The Toggle Switch

14. The General Switch A mutual repression circuit. Two proteins A and B negatively regulate each other’s synthesis

15. The General Switch Exists in the lambda phage Also synthetically constructed by collins, cantor and gardner (nature 2000)

16. The General Switch Rate equations Master equation – too long …

17. The General Switch A well known result - The rate equations have a single steady state solution for Hill coefficient n=1: Conclusion - Cooperative binding (Hill coefficient n>1) is required for a switch Gardner et al., Nature, 403, 339 (2000) Cherry and Adler, J. Theor. Biol. 203, 117 (2000) Warren an ten Wolde, PRL 92, 128101 (2004) Walczak et al., Biophys. J. 88, 828 (2005)

18. The Switch Stochastic analysis using master equation and Monte Carlo simulations reveals the reason: For weak repression we get coexistence of A and B proteins For strong repression we get three possible states:  A domination  B domination  Simultaneous repression (dead- lock) None of these state is really stable

19. The Switch In order that the system will become a switch, the dead-lock situation (= the peak near the origin) must be eliminated. Cooperative binding does this – The minority specie has hard time to recruit two proteins But there exist other options…

20. Bistable Switches The BRD Switch - Bound Repressor Degradation The PPI Switch – Protein-Protein Interaction. A and B proteins form a complex that is inactive

21. Bistable Switches The BRD and PPI switches exhibit bistability at the level of rate equations. BRDPPI

22. The Exclusive Switch An overlap exists between the promoters of A and B and they cannot be occupied simultaneously The rate equations still have a single steady state solution

23. The Exclusive Switch But stochastic analysis reveals that the system is truly a switch The probability distribution is composed of two peaks The separation between these peaks determines the quality of the switch Lipshtat, Loinger, Balaban and Biham, Phys. Rev. Lett. 96,188101 (2006) k=1 k=50

24. The Exclusive Switch

25. The Repressillator A genetic oscillator synthetically built by Elowitz and Leibler Nature 403 (2000) It consist of three proteins repressing each other in a cyclic way

26. The Repressillator Rate equation results MC simulations results

27. Summary We have studied several modules of genetic networks using deterministic and stochastic methods Stochastic analysis is required because rate equations give results that may be qualitatively wrong Current work is aimed at extending the results to other networks, such as oscillators and post-transcriptional regulation