Computational Methods to Study Kinetics of DNA Replication

Yang, Scott Cheng‐Hsin; Gauthier, Michel G.; Bechhoefer, John

doi:10.1007/978-1-60327-815-7_32

Cited by 8 publications

(8 citation statements)

References 28 publications

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“…An advantage of simulation is that, with modest computer resources (especially if simulations keep track of only positions of forks and origins rather than use a lattice for each point on the genome [95]), one can recreate in silico not only the ideal experimental scenario envisaged, but also any relevant experimental details. For example, it is straightforward to include the effects of asynchrony in the cell population, finite microscope resolution, labeling artifacts, and the like [96]. Once the artifacts and the replication scenario are chosen correctly, the simulation can reproduce, within statistical error, the data from any given scenario.…”

Section: Box 1 F and I: Mathematical Functions That Describe Replicamentioning

confidence: 99%

Replication timing and its emergence from stochastic processes

Bechhoefer

Rhind

2012

Trends in Genetics

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The temporal organization of DNA replication has puzzled cell biologists since before the mechanism of replication was understood. The realization that replication timing correlates with important features, such as transcription, chromatin structure and genome evolution, and is misregulated in cancer and aging has only deepened the fascination. Many ideas about replication timing have been proposed, but most have been short on mechanistic detail. However, recent work has begun to elucidate basic principles of replication timing. In particular, mathematical modeling of replication kinetics in several systems has shown that the reproducible replication timing patterns seen in population studies can be explained by stochastic origin firing at the single-cell level. This work suggests that replication timing need not be controlled by a hierarchical mechanism that imposes replication timing from a central regulator, but instead results from simple rules that affect individual origins.

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Section: Box 1 F and I: Mathematical Functions That Describe Replicamentioning

confidence: 99%

Replication timing and its emergence from stochastic processes

Bechhoefer

Rhind

2012

Trends in Genetics

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“…These works provide a solid foundation to formulate more complex models appropriate to cells where origin location is less random and fork velocity is more variable (Yang et al 2009). The fact that a large genome can be faithfully duplicated while initiating origins randomly raises the question why initiation is random neither in space nor in time in many other organisms or in later developmental stages.…”

Section: Relating Replication Initiation Rate To Replication End Timementioning

confidence: 99%

Mathematical modelling of eukaryotic DNA replication

Hyrien

Goldar

2009

Chromosome Res

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Eukaryotic DNA replication is a complex process. Replication starts at thousand origins that are activated at different times in S phase and terminates when converging replication forks meet. Potential origins are much more abundant than actually fire within a given S phase. The choice of replication origins and their time of activation is never exactly the same in any two cells. Individual origins show different efficiencies and different firing time probability distributions, conferring stochasticity to the DNA replication process. High-throughput microarray and sequencing techniques are providing increasingly huge datasets on the populationaveraged spatiotemporal patterns of DNA replication in several organisms. On the other hand, singlemolecule replication mapping techniques such as DNA combing provide unique information about cell-to-cell variability in DNA replication patterns. Mathematical modelling is required to fully comprehend the complexity of the chromosome replication process and to correctly interpret these data. Mathematical analysis and computer simulations have been recently used to model and interpret genome-wide replication data in the yeast Saccharomyces cerevisiae and Schizosaccharomyces pombe, in Xenopus egg extracts and in mammalian cells. These works reveal how stochasticity in origin usage confers robustness and reliability to the DNA replication process.

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“…With large data sets comes the potential to construct a much more detailed picture of how replication occurs and is regulated ( Hyrien and Goldar, 2010 ). For molecular-combing experiments, previous work has shown that quantitative information such as replication-origin firing rates and replication-fork velocities can be extracted ( Yang et al, 2009 ). Such information has led to an appreciation of the function of stochastic effects in initiation ( Herrick and Bensimon, 1999 ), to a greater understanding of the ‘random-completion problem' for embryonic replication ( Yang and Bechhoefer, 2008 ), to models highlighting searching and binding kinetics in initiation timing ( Gauthier and Bechhoefer, 2009 ), and to suggestions that initiation patterns may be universal across species ( Goldar et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Modeling genome‐wide replication kinetics reveals a mechanism for regulation of replication timing

Yang

Rhind

Bechhoefer

2010

Molecular Systems Biology

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We developed analytical models of DNA replication that include probabilistic initiation of origins, fork progression, passive replication, and asynchrony.We fit the model to budding yeast genome-wide microarray data probing the replication fraction and found that initiation times correlate with the precision of timing.We extracted intrinsic origin properties, such as potential origin efficiency and firing-time distribution, which cannot be done using phenomenological approaches.We propose that origin timing is controlled by stochastically activated initiators bound to origin sites rather than explicit time-measuring mechanisms.

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Computational Methods to Study Kinetics of DNA Replication

Cited by 8 publications

References 28 publications

Replication timing and its emergence from stochastic processes

Replication timing and its emergence from stochastic processes

Mathematical modelling of eukaryotic DNA replication

Modeling genome‐wide replication kinetics reveals a mechanism for regulation of replication timing

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