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„Virtuelle Strahlen-Biophysik: Einflüsse der Zellkernarchitektur„

„Virtuelle Strahlen-Biophysik: Einflüsse der Zellkernarchitektur„. Monte Carlo modeling of the genome structure of the cell nucleus. Virtual radiation biophysics. Comparison with experimental data. Living cells. H. Bornfleth, D. Zink, T. Cremer. Chromosome Painting experiments. Fixed cells.

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„Virtuelle Strahlen-Biophysik: Einflüsse der Zellkernarchitektur„

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  1. „Virtuelle Strahlen-Biophysik: Einflüsse der Zellkernarchitektur„ • Monte Carlo modeling of the genome structure of the cell nucleus • Virtual radiation biophysics • Comparison with experimental data

  2. Living cells H. Bornfleth, D. Zink, T. Cremer Chromosome Painting experiments Fixed cells I. Solovei, F. Habermann, M. Cremer, T. Cremer (Institute of Anthropology and Human Genetics University of München)

  3. Spherical 1-Mbp Chromatin Domain (SCD) model

  4. „Importance Sampling“ Monte Carlo method Expectation value of a canonic ensemble normalized Boltzmann factor Simple Sampling Monte Carlo method: choose randomly N states x1, x2, ...., xN from phase space Importance Sampling Monte Carlo method: choose states x1, x2, ...., xN with a probability P(xi) from phase space with P(xi)~exp(-H(xi)/kBT) idea of Metropolis: consecutive states are generated by a transition probability (Markov process). The choice of the transition probability has to be made in such a way that the probability function P(xi) of the states convergence against the equilibrium distribution

  5. „Metropolis algorithm“ 1. Choose randomly a state from phase space . 2. An accessible state from phase space is chosen. 4. The energy difference DH between the new and the old state is computed. 4. If DH<0 the new state will be accepted. 5. If DH>0 the new state is accepted with the probability exp(- DH/kT). That means, when a random number from [0;1] < exp(- DH/kT) than the new state is accepted. 6. Go back to 2.

  6. Monte Carlo Relaxation (no real time dependency) 0 MC 1000 MC 20mm

  7. Additional constraints of the higher order nuclear architecture: • distribution of chromosome territories in the nuclear volume • the morphology of the active and inactive X-chromosome

  8. Positioning of chromosomes in lymphocyte cell nuclei

  9. Positioning of chromatin homologous to human CTs #18(red, gene poor) and #19(green, gene rich) in lymphocyte nuclei of higher primates (Tanabe et al. 2002) Gorilla Human Orangutan Chimpanzee Marmoset Tamarin

  10. 3D mapping algorithm

  11. Mapping of CTs: comparison (#18) and (#19) Human Chimpanzee Gorilla Orang Utan

  12. Summary of primate #18 and #19 sequencies distribution in lymphocyte nuclei <r18>,<r19> : mean value of radial distributions of #18, 19 sd : standard deviation Mean of mean: 75,7 +- 3,2 | 53,3 +-4,3

  13. Virtual Microscopy

  14. simulated gene density correlated distribution Virtual microscopy reconstructions of simulated CTs #18(red) and #19 (green)

  15. Radial distribution of chromosome territories Simulation (statistical distribution) Experiment (lymphocyte cell nuclei)

  16. Radial distribution of chromosome territories Simulation (gene density correlated distribution) Experiment (lymphocyte cell nuclei)

  17. Morphology of X-chromosomes Visualization: C. Dartu, W. Jäger ( IWR, University of Heidelberg)

  18. Convolution with the measured PSF Simulation of the Xa and Xi chromosome Xa Convolution with the measured PSF Segmentation, Visualization

  19. Interchange frequencies: comparison of Observation and Simulation

  20. DSB 1Mbp domain Chromosome territory j Inter-change d Intra-change Chromosome territory i Virtual radiation algorithm • Random distribution of DSBs within DNA • number of DSBs increases linearly with dose and is proportional to the DNA content • probability p n of an individual modeled 1Mbp domain containing n DSBs: • D - dose of radiation in (Gy) • q - size of a domain (=1000000bp) • g - yield of DSBs (=8.07 ·10-9Gy-1bp-1) • interchange is counted when the distance d of two DSBs in two directly neighbored domains followed:

  21. Influence of chromosome distribution on interchange frequencies absolute interchange frequencies in %, examples: (4;18) (4;19) (19;18) Exp. (1600 cells): 0.3% 0.3% 0.1% statis. Simul. (50000cells): 0.6% 0.7% 0.3% gendens. corr. Simul. (50000cells): 0.6%0.2% 0.1%

  22. Influence of chromosome distribution on interchange frequencies Relative one-chromosome yield (normalized to 1000) E Error bars: E ± (E)1/2

  23. Influence of chromosome distribution on interchange frequencies* Relative one-chromosome yield (normed to one) scattered (experimental) scattered (simulated) Regression: t-rate~(DNA content)2/3 *1,600 cells (50simulated nuclei were virtually iradiated 32 times)

  24. Senthilkumar Pazahanisamy Constance Grossmann Johann von Hase Christian Carl Andreas Schweitzer Margund Bach Claudia Batram Werner Stadter Hans Mathée Stefan Stein Heinz Eipel Nick Kepper Susanne Fenz Udo Spöri Gregor Kreth Helmut Schneider Jürgen Reymann David Baddeley Jutta Finsterle Christian Wagner

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