Nucleation and growth in one dimension. I. The generalized Kolmogorov-Johnson-Mehl-Avrami model

Jun, Suckjoon; Zhang, Haiyang; Bechhoefer, John

doi:10.1103/physreve.71.011908

Cited by 69 publications

(77 citation statements)

References 25 publications

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“…They were all implemented using the programming language of Igor Pro [30]. The work presented in this chapter has been published [21] and the discussion here closely follows that work. Bechhoefer contributed the method discussed in Sec.…”

Section: Numerical Simulation Algorithmmentioning

confidence: 99%

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Reconstructing DNA replication kinetics from small DNA fragments

Zhang

Bechhoefer

2006

Phys. Rev. E

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Section: Numerical Simulation Algorithmmentioning

confidence: 99%

“…Once initiation is done, the eyes grow by Ax, namely, by one lattice size at each end. For more details see [21].…”

Section: The Lattice-model Algorithmmentioning

confidence: 99%

Reconstructing DNA replication kinetics from small DNA fragments

Zhang

Bechhoefer

2006

Phys. Rev. E

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“…[1][2][3][4] The investigation of such systems is a very active research field that includes the theory of nucleation in 1D, [5][6][7][8][9][10] the investigation of atomistic diffusion [11][12][13][14][15][16][17] and epitaxial growth [1][2][3][18][19][20][21] in the presence of steps, the catalytic activity of step sites, 22 electron confinement in 1D states, [23][24][25][26] and the behavior of 1D magnetic materials. [27][28][29][30][31][32][33][34][35] Owing to the increase of binding energy at step sites, and depending on the substrate temperature, adatoms deposited on vicinal surfaces can self-assemble into chainlike structures by decorating the step edges.…”

Section: Introductionmentioning

confidence: 99%

Equilibrium island-size distribution in one dimension

et al. 2006

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“…The disadvantage of measuring interorigin distances is that as bubbles fuse, the center of the fused bubbles is incorrectly assumed to be an origin and the location of the actual origins is obscured (Herrick et al, 2002). The advantage of measuring interbubble distances is that it avoids the fusion problem and, if origins are randomly distributed, the fusion of bubbles does not affect the exponential character of the distribution of the remaining gaps (Jun et al, 2005). The exponential and Gaussian fits of interbubble distances in the whole genome experiments and the reanalysis of the published budding yeast data were performed using KaleidaGraph (Synergy Software, Reading, PA).…”

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DNA Replication Origins Fire Stochastically in Fission Yeast

Patel

Arcangioli

Baker

et al. 2006

MBoC

179

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DNA replication initiates at discrete origins along eukaryotic chromosomes. However, in most organisms, origin firing is not efficient; a specific origin will fire in some but not all cell cycles. This observation raises the question of how individual origins are selected to fire and whether origin firing is globally coordinated to ensure an even distribution of replication initiation across the genome. We have addressed these questions by determining the location of firing origins on individual fission yeast DNA molecules using DNA combing. We show that the firing of replication origins is stochastic, leading to a random distribution of replication initiation. Furthermore, origin firing is independent between cell cycles; there is no epigenetic mechanism causing an origin that fires in one cell cycle to preferentially fire in the next. Thus, the fission yeast strategy for the initiation of replication is different from models of eukaryotic replication that propose coordinated origin firing. INTRODUCTIONEukaryotic DNA replication begins at discrete origins distributed along chromosomes. Although much progress has been made in understanding the mechanisms that establish and activate individual origins, the manner in which origin firing is coordinately regulated spatially along the chromosomes and temporally throughout S phase remains unclear (Kelly and Brown, 2000;Gilbert, 2001;Schwob, 2004). These questions are of particular interest because in most cases origin firing is not efficient; that is, a particular origin will fire in only a fraction of cell cycles. The observation that origins do not fire in every cell cycle raises the question of how individual origins are selected to fire and if that selection is distributed in a coordinated manner. In the absence of such coordination, origin firing would be randomly distributed, leading to the random gap problem; some cells would have large gaps between origin firing that would take a long time to replicate (Lucas et al., 2000;Herrick et al., 2002;Hyrien et al., 2003;Jun et al., 2004). Simple models of replication kinetics predict that if replication origins are randomly distributed, ϳ5% of the cells will have such large gaps between active origins that they will take four times longer than average to replicate (see Materials and Methods for calculations). Alternatively, cells could have a mechanism that evenly distributes origin firing across the genome; they would thus avoid the random gap problem, and replication would be efficient. Models for such mechanisms have been proposed; for instance, origins within specific clusters could be selected to fire, or active origins could suppress their neighbors by lateral inhibition (Mesner et al., 2003;Shechter and Gautier, 2005). However, little direct evidence exists to either support or refute these models.Origin structure and function has been well characterized in the budding yeast Saccharomyces cerevisiae (Kelly and Brown, 2000;Gilbert, 2001). Budding yeast have small (ϳ100 base pairs) origins characterized by a 17-b...

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Nucleation and growth in one dimension. I. The generalized Kolmogorov-Johnson-Mehl-Avrami model

Cited by 69 publications

References 25 publications

Reconstructing DNA replication kinetics from small DNA fragments

Reconstructing DNA replication kinetics from small DNA fragments

Equilibrium island-size distribution in one dimension

DNA Replication Origins Fire Stochastically in Fission Yeast

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