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Genregulation

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

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Genregulation

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

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

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

  4. E.coli as model system Genregulation allows adaption to changing environmental conditions, and regulation of metabolism E.coli has a single DNA molecule which is 4.6 106 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

  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 Francois Jacob und Jaques Monod, 1961 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

  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 IPTG, TMG LacI

  13. Non-metabolizable inducer are used to induce gene expression IPTG (Isopropyl β-D-1-thiogalactopyranoside)This compound is used as a molecular mimic of allolactose, a lactosemetabolite 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. 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. Campbell, N.A., Biology

  14. Variation of Protein-Concentration with IPTG Northern Blot: measurement of the messenger RNA (mRNA) concentration Long, C et al, J.Bacteriol. 2001 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?

  15. Transkription und Translation in E.coliTypical times and rates 1 Molecule / cell = 1nM Complete mass2.5 106 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 system1 pBAD24 2 ~55 copies/cell Reporter Break down Regulator Uptake [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. DIC tn tn Fluorescence t0 DIC t0 t1 N Time-lapse Fluorescence Microscopy and Quantitative Image Processing automated data aquisition define ROIs measure total intensity background correction calibration and conversion into molecular units

  18. Judith.Megerle@physik.lmu.de

  19. Single cell expression kinetics Fluorescence measurement • Cell outlines are determined using bright field images • The signal is integrated within the outline in each fluorescence image Saturating induction 5min 15min 25min 35min 45min Subsaturating induction 40min 30min 50min 60min 70min Image series correspond to blue curves

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

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

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

  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. DGFP=3-7µm2/s) Probability distribution 1µm Typical timescale for a proteine to find an arbitrary point in an E.coli: tD 0.1s

  28. Diffusion to a target site (binding disc)

  29. Residence times for transcription factors for specific bindings (operon) with 1M-1=1.6nm3 and Gspez=-12.6kcal/mol, =1 follows (from on=20s/N follows, that 1 molecule in 1µm3 occupies half an Operator) for unspecific binding sites with Guspez=-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 D1≈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 Genregulatoric proteine translation transcription

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

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

  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 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 Start: not induced After induction exist 2 populations: green: induced bacteria white, not induced population Ozbudak et al, Nature 2004

  42. modelling of genregulatory networks: example

  43. Modelling in mRNA level

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

  45. Simplification of genregulatory networks Genregulatory protein translation transcription

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

  47. Boolean networks (Kauffman 1989)

  48. Boolean networkmodel • N Genes (nodes) • with 2N 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

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