70 research outputs found
Acta Crystallographica Section A Foundations of
No full textCopyright © International Union of Crystallography electronic reprint Author(s) of this paper may load this reprint on their own web site provided that this cover page is retained. Republication of this article or its storage in electronic databases or the like is not permitted without prior permission in writing from the IUCr. Acta Cryst. (2002). A58, 574–579 Rowicka et al. ¯ Crystallographic FFT. I research paper
Stochasticity of replication forks’ speeds plays a key role in the dynamics of DNA replication
No full textEukaryotic DNA replication is elaborately orchestrated to duplicate the genome timely and faithfully. Replication initiates at multiple origins from which replication forks emanate and travel bi-directionally. The complex spatio-temporal regulation of DNA replication remains incompletely understood. To study it, computational models of DNA replication have been developed in S. cerevisiae. However, in spite of the experimental evidence of forks’ speed stochasticity, all models assumed that forks’ speeds are the same. Here, we present the first model of DNA replication assuming that speeds vary stochastically between forks. Utilizing data from both wild-type and hydroxyurea-treated yeast cells, we show that our model is more accurate than models assuming constant forks’ speed and reconstructs dynamics of DNA replication faithfully starting both from population-wide data and data reflecting fork movement in individual cells. Completion of replication in a timely manner is a challenge due to its stochasticity; we propose an empirically derived modification to replication speed based on the distance to the approaching fork, which promotes timely completion of replication. In summary, our work discovers a key role that stochasticity of the forks’ speed plays in the dynamics of DNA replication. We show that without including stochasticity of forks’ speed it is not possible to accurately reconstruct movement of individual replication forks, measured by DNA combing.</div
An illustration of the derivation of <i>μ</i><sub>Δ<i>x</i></sub> for each individual origin.
No full text(a) As an example we use the DNA copy number of an early origin located at 147 kb from the beginning of the chromosome I. The distribution of DNA tracks measured from BrdU data [31] is normalized based on the BrdU micro-array DNA copy number of origin ARS305, which was verified by quantitative PCR in the same experimental condition. For smoothing the data, the Savitzky-Golary filter (working through the convolution process) is utilized, because it minimally distorts the original data. Maximum of the smoothed peak indicates the origin position. (b) The data are transformed into probability distribution function of Δx and fitted with a Gaussian distribution which peak is assumed to be μΔx.</p
Model selection by Repli-Sim for models with constant and stochastic forks’ speeds.
No full text(a) Some models (parameter sets) considered. Stochasticity of replication forks’ speeds () is shown on the horizontal axis, residual sum of squares (RSS) with experimental data (the lower the better) is shown on the vertical axis, the average forks’ speed (v) is color coded, as shown in the color-bar. Best models (smallest RSS value) are more stochastic. The best selected constant speed model had parameters (, texp = 50min, v = 1.4kb/min, ) and the best variable speed model was (, texp = 42min, v = 1.5kb/min, ). The texp and fork speed from experimental data are 40min and v = 1.6(kb/min), which are more compatible with the variable fork progression model. (b, c) The distribution of DNA tracks for both best stochastic (b, orange) and constant (c, blue) forks’ speeds models are shown along with the distribution of DNA tracks from experimental data (gray), which shows a better fit for the stochastic forks’ speeds model. The average distance traveled in the stochastic forks’ speeds model is compatible with the experimental data (∼105kb).</p
Acta Crystallographica Section A Foundations of
No full textelectronic reprint The crystallographic fast Fourier transform. Recursive symmetry reductio
Replication fork speed adjustment based on the distance to the approaching fork.
No full text(a) Schematic representation of D, the distance from an emerging fork to an approaching fork. (b) The strong correlation between D and emerging fork speed is observed in three independent data sets [12, 13, 23] (c) dependence of the slope on the inter-origin distance observed in the experimental data (red) cannot be reproduced by our model where forks’ speed varies stochastically but does not depend on the genomic location (blue).</p
Replication profile derived from our best model: Stochastic forks’ speed not depending on genomic location and stochastic origin firing (<i>σ</i><sub><i>t</i></sub> ≠ 0).
No full textNote that the replication profile slope is highly variable (as indicated by the red arrow) even though the fork speed is constant. Here, the variation in slope is due to the origin firing with different probabilities at different times, although such variation can be also caused by local variability of forks’ speeds.</p
Acta Crystallographica Section A Foundations of
No full textelectronic reprint The crystallographic fast Fourier transform. IV. FFT-asymmetric units in the reciprocal spac
Comparison of DNA track distributions predicted by different models with experimental data.
No full text(a) Our model (random origin activation times, but stochastic forks’ speeds), (b) Hawkins et al. model. Our simplified model (a, orange) reproduces distribution of distances travelled by replication forks measured by DNA combing (gray) much better than previous model (b, blue).</p
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