Showing papers by "John Bechhoefer published in 2008"
TL;DR: It is shown that the Hsk1-Dfp1 replication kinase (the fission yeast Cdc7-Dbf4 homologue) plays a role in origin firing and that increasing or decreasing origin firing rates leads to an increase in genomic instability, demonstrating the biological importance of appropriate origin efficiency.
Abstract: Origins of DNA replication are generally inefficient, with most firing in fewer than half of cell cycles. However, neither the mechanism nor the importance of the regulation of origin efficiency is clear. In fission yeast, origin firing is stochastic, leading us to hypothesize that origin inefficiency and stochasticity are the result of a diffusible, rate-limiting activator. We show that the Hsk1-Dfp1 replication kinase (the fission yeast Cdc7-Dbf4 homologue) plays such a role. Increasing or decreasing Hsk1-Dfp1 levels correspondingly increases or decreases origin efficiency. Furthermore, tethering Hsk1-Dfp1 near an origin increases the efficiency of that origin, suggesting that the effective local concentration of Hsk1-Dfp1 regulates origin firing. Using photobleaching, we show that Hsk1-Dfp1 is freely diffusible in the nucleus. These results support a model in which the accessibility of replication origins to Hsk1-Dfp1 regulates origin efficiency and provides a potential mechanistic link between chromatin structure and replication timing. By manipulating Hsk1-Dfp1 levels, we show that increasing or decreasing origin firing rates leads to an increase in genomic instability, demonstrating the biological importance of appropriate origin efficiency.
85 citations
TL;DR: In this article, the authors investigated the random-completion problem using a type of model first developed to describe the kinetics of first-order phase transitions, and derived the distribution of replication completion times for a finite genome.
Abstract: DNA synthesis in Xenopus frog embryos initiates stochastically in time at many sites origins along the chromosome. Stochastic initiation implies fluctuations in the time to complete and may lead to cell death if replication takes longer than the cell cycle time 25 min. Surprisingly, although the typical replication time is about 20 min, in vivo experiments show that replication fails to complete only about 1 in 300 times. How is replication timing accurately controlled despite the stochasticity? Biologists have proposed two solutions to this “random-completion problem.” The first solution uses randomly located origins but increases their rate of initiation as S phase proceeds, while the second uses regularly spaced origins. In this paper, we investigate the random-completion problem using a type of model first developed to describe the kinetics of first-order phase transitions. Using methods from the field of extreme-value statistics, we derive the distribution of replicationcompletion times for a finite genome. We then argue that the biologists’first solution to the problem is not only consistent with experiment but also nearly optimizes the use of replicative proteins. We also show that spatial regularity in origin placement does not alter significantly the distribution of replication times and, thus, is not needed for the control of replication timing.
50 citations
11 Jun 2008
TL;DR: A simple feedforward technique is developed that increases the effective bandwidth of the positioning stage by a factor of 15 over its standard operation and introduces a model-free variant that is simpler to set up, performs better, and is more robust to system changes.
Abstract: We review basic issues in the control of scanning probe microscopes. To improve the performance of the present generation of instruments, we have developed a simple feedforward technique that nonetheless increases the effective bandwidth of the positioning stage by a factor of 15 over its standard operation. If the desired control signal is known in advance (as it is for a periodic scan signal), the feedforward filter can be non-causal: information about the future can be used to cancel the phase lag produced by the stage response. We compare our design with other control techniques. We show that model-based iterative control algorithms can lead to a substantial performance boost, at the cost of more measurements of the system transfer function. We then introduce a model-free variant that is simpler to set up, performs better, and is more robust to system changes.
27 citations