Reconstructing DNA replication kinetics from small DNA fragments

Zhang, Haiyang; Bechhoefer, John

doi:10.1103/physreve.73.051903

Cited by 19 publications

(20 citation statements)

References 22 publications

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“…Application of the KJMA model to DNA replication kinetics Bechhoefer and colleagues have adapted theories of phase transition kinetics to extract an analytical expression for the time-dependent changes in origin activation probability from the DNA fibre data (Herrick et al 2002;Zhang and Bechhoefer 2006). Some phase transition phenomena, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The extracted I(t) was found to markedly increase halfway through S phase then decrease sharply (Herrick et al 2002;Zhang and Bechhoefer 2006). Initially, the decreasing part of I(t) was not taken in consideration because the numerical inversion procedure tends to amplify noise and the possibility of systematic errors at the end of S phase could not be discarded.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of eukaryotic DNA replication

Hyrien

Goldar

2009

Chromosome Res

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“…Still, one can show that the obvious work-around -simply to omit L oversized from Eq. 16 -reduces the bias of the naive estimator interior for domain size by including information about the edge domains [25]. For more accurate results, then, one should use this "interior-edge" estimator.…”

Section: Finite Fragment Sizesmentioning

confidence: 99%

“…While such a rule of thumb keeps the uncertainty of estimated parameters bounded, it implies that little information will be gathered about the first and last stages of replication. In order to increase the information extracted from experiments in those regimes, one can do a more sophisticated analysis [25]. This analysis begins by recognizing that there are three classes of domains (either holes or eyes): interior, exterior, and over-sized (Fig.…”

Section: Finite Fragment Sizesmentioning

confidence: 99%

Computational Methods to Study Kinetics of DNA Replication

Yang

Gauthier²,

Bechhoefer³

2009

Methods in Molecular Biology

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New technologies such as DNA combing have led to the availability of large quantities of data that describe the state of DNA while undergoing replication in S phase. In this chapter, we describe methods used to extract various parameters of replication -fork velocity, origin initiation rate, fork density, numbers of potential and utilized origins -from such data. We first present a version of the technique that applies to "ideal" data. We then show how to deal with a number of real-world complications, such as the asynchrony of starting times of a population of cells, the finite length of fragments used in the analysis, and the finite amount of DNA in a chromosome.

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“…Experimental data collected by Cope, [12] [13], and others [14] [15], show a certain universality of the Avrami equation to describe the real processes. The universality makes it possible to study different processes without knowing the exact structure and dynamics of a given system.…”

Section: Introductionmentioning

confidence: 99%

On the Self-Similarity in Biological Processes

Szász¹,

Szigeti²,

Szász³

2017

OJBIPHY

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We show that the processes described by Avrami functions are self-similar. A comparative function characterizes a self-similar process by a certain Avrami exponent. We define the self-similar categories of some well-known biological processes. The method to determine the Avrami exponent by choosing the comparative function is demonstrated on the diffusion model of the growth of nuclei. We generalize the results.

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Reconstructing DNA replication kinetics from small DNA fragments

Cited by 19 publications

References 22 publications

Mathematical modelling of eukaryotic DNA replication

Mathematical modelling of eukaryotic DNA replication

Computational Methods to Study Kinetics of DNA Replication

On the Self-Similarity in Biological Processes

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