1. Genregulation Literature - Alberts/Lehninger - Kim Sneppen & G. Zocchi: Physics in Molecular Biology - E. Klipp et al. : Systems Biology in Practice Systems biophysics 2010/05/11 Physics of transcription control and expression analysis

2. From genetic approach to sytemic approach genregulation mRNA regulation DNA mutations / evolution protein functions spatiotemporal structure formation Morphogenesis signal transduction => Topics of systems biophysics

3. Biological Pattern formation and Morphogenesis 11.05.2010 Enzymatic Reactions Michaelis-Menton-Kinetics Inhibation, Regulation Reaction-Diffusion-Model of Morphogenesis Biochemical Network

4. E.coli as model system E.coli has a single DNA molecule which is 4.6 10 6 basepairs long. It encodes 4226 proteins and a couple of RNA molecules. The information content of the genome is is bigger than the structural information of the encoded Proteins -> regulatory mechanisms are encoded Genregulation allows adaption to changing environmental conditions, and regulation of metabolism

5. Content of this lecture: Basics: Monod Model, Lac Operon Statistical Physics of DNA-binding Proteins Modelling of genregulatory Networks (ODE & Boolian Networks) Dynamics of Protein-DNA binding DNA looping Analysis of gene expression data Synthetic Networks

6. Operon-Modell operon Operon: Genetic subunit, that consists of regulated genes with similar functionality. It includes - Promotor: Binding site for RNA polymerase - Operator: controls access of the RNA-Polymerase structural gene - Structural genes: Polypeptide encoding genes Francois Jacob und Jaques Monod, 1961

7. The Trp Operator as a switch: Within the promotor lies a short DNA region as binding site for a repressor. A bound repressor prevents the Polymerase from binding.

8. The OUTSIDE of proteins can be recognized by proteins Distinct basepairs can be recognized by their margins  DNA binding motivs Small channel Large channel

9. Binding of Tryptophane to the Tryptophane-Repressorproteine changes the conformation of the repressor,  Repressor can bind to the repressor binding site

10. Identification of promotor sequences

11. Transcription-Activation proteins switch on genes

12. Gen-Regulation with Feedback: lac-Operon LacI IPTG, TMG

13. Campbell, N.A., Biology A cis-regulatory element or cis-element is a region of DNA or RNA that regulates the expression of genes located on that same strand. This term is constructed from the Latin word cis, which means "on the same side as". These cis-regulatory elements are often binding sites of one or more trans-acting factors.DNARNAgenesLatinbinding sites IPTG (Isopropyl β-D-1-thiogalactopyranoside)This compound is used as a molecular mimic of allolactose, a lactose metabolite that triggers transcription of the lac operon. Unlike allolactose, the sulfur (S) atom creates a chemical bond which is non-hydrolyzable by the cell, preventing the cell from "eating up" or degrading the inductant. IPTG induces activity of beta- galactosidase, an enzyme that promotes lactose utilization, by binding and inhibiting the lac repressor. In cloning experiments, the lacZ gene is replaced with the gene of interest and IPTG is then used to induce gene expression.allolactoselactose metabolitetranscriptionlac operonbeta- galactosidase Non-metabolizable inducer are used to induce gene expression

14. Variation of Protein-Concentration with IPTG Northern Blot: measurement of the messenger RNA (mRNA) concentration External and internal Inductor-concentration is equal in equilibrium The mRNA concentration increases linear with the concentration of inductor, saturation over 60% The operon enables a variation of Protein concentration. What is missing to make a switch? Long, C et al, J.Bacteriol. 2001

15. Transkription und Translation in E.coli Typical times and rates 1 Molecule / cell = 1nM Complete mass2.5 10 6 Da TRANSKRIPTION rate 1/s - 1/18s Transkriptionsrate: 30bps-90bps TRANSLATION 10.000-15.000 Ribosomes Translation rate 6-22 codons/s (40 Proteine/mRNA)

16. The arabinose system 1 Uptake Reporter Regulator Break down pBAD24 2 ~55 copies/cell [1] R. Schleif. Trends in Genetics, 16(12):559–565, 2000 [2] L. M. Guzman, D. Belin, M. J. Carson, and J. Beckwith. J.Bacteriol., 177(14):4121–4130, 1995 [3] D. A. Siegele and J. C. Hu. Proc. Natl. Acad. Sci. USA, 94(15):8168–8172, 1997

17. automated data aquisition define ROIs measure total intensity background correction calibration and conversion into molecular units Time-lapse Fluorescence Microscopy and Quantitative Image Processing

18. Judith.Megerle@physik.lmu.de

19. Single cell expression kinetics 30min 40min 60min50min70min 5min15min35min45min25min Saturating induction Subsaturating induction Image series correspond to blue curves Fluorescence measurement Cell outlines are determined using bright field images The signal is integrated within the outline in each fluorescence image

20. Gene expression model Deterministic rate modelwith time delay d Reporter moduleUptake module Induction: t=0min

21. Curve Fitting Fixed Parameters Saturating induction Subsaturating induction Fit Parameters Fit expression function Time delay Protein synthesis rate Literature Measured

22. Ohter example: Quorum Sensing

23. Squid with floodlamp Phänomena: Squid (Euprymna scolopes) emmits light due the night  Squid isn´t recognized as prey in the moonlight Explanation: Light organ of the squid collects luminescent bacteria (Vibrio fischerei) Question: Why does V. fischerei emmit light within the lightorgan of the squid, but not in open sea?

24. Quorum sensing Bakterien detect their own cell density  Density regulates the expression of luminescent genes K. Nelson, Cell-Cell Signalling in Bacteria Bacteria increase exponential OD: optical density

25. Molekular picture of QS Bakteria export oligopeptides (Pheromones) Oligopeptides accumulate with increasing cell density Oligopeptide diffuse into cell membrane and regulates the expression of luminescent genes

26. Searching the binding site

27. Searching the binding site: timescales Stokes Einstein equation (z.B. D GFP =3-7µm 2 /s) Probability distribution 1µm Typical timescale for a proteine to find an arbitrary point in an E.coli: t D  0.1s

28. Diffusion to a target site (binding disc)

29. Residence times for transcription factors (from  on =20s/N follows, that 1 molecule in 1µm 3 occupies half an Operator) for specific bindings (operon) with 1M -1 =1.6nm 3 and  G spez =-12.6kcal/mol,  =1 follows for unspecific binding sites with  G uspez =-10 -4 kcal/mol, follows

30. Search of the binding sites on a DNA strand Unspecific binding events of TFs is a problem, since the time to find a binding site is increased. For a infinite staytime, a 1D- random walk over the strand would last: (L=1.5mm und D 1 ≈D)

31. Accelerated search: jumps between strands decrease time to find a binding site. Mit L=1.5mm, l=150nm follows

32. Boolian Networks, or what cells and computers have in common.

33. (Nature, Dec 99)

34. Combinatoric gene regulation: Genetic networks transcription translation Genregulatoric proteine

35. A transcription-activator and a transcription-repressor regulate the lac-Operon

36. Thermodynamicc model of a combinatoric transcription logics  P : binding probability Gerland et al. PNAS, 2005 Gene regulation follows the mechanics of „Boltzmann-machines“

37. Statistical physics of protein - DNA binding Binding-isothermes:

38. Cooperativity due to dimer binding Cooperative binding

39. The statistical weight of the „on“ state The free-energy difference is normalized to 1mol/l. The real change in free energy of the binding event depends on the concentration of TF in solution [Cl] :

40. A model for lac networks Glukose conc. constant GFP: Reportermolekül, Abbildung durch Fluoreszenz-Mikroskopie => je höher das Fluoreszenz-Signal desto mehr LacZ,Y wird exprimiert

41. Experimental proof for a switch Start: not induced After induction exist 2 populations: green: induced bacteria white, not induced population Bistable area (grey) Arrow marks the start state: on-off state of bacteria depend on the on-off state in the beginning!  switch with hysteresis Ozbudak et al, Nature 2004

42. modelling of genregulatory networks: example

43. Modelling in mRNA level

44. Timetrace of mRNA concentrations Problem: kinetic binding constants are usually not known and hard to measure Steady state

45. Simplification of genregulatory networkstranscription translation Genregulatory protein

46. Abstraction of genetic networks Gen X Gen Y Gen Z + -

47. Boolean networks (Kauffman 1989)

48. Boolean networkmodel N Genes (nodes) with 2 N different states with possible rules K is the number of possible inputs per node

49. Boolean rules for N=2 und K=2

50. Back to the example: We learn: if a=0, then follows 0101 stationary if a=1, then follows oscilatory behaviour 1000->1001->1111->1010 ->1000