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Open AccessJournal ArticleDOI

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

TLDR
This model is the first to suggest a detailed, testable, biochemically plausible mechanism for the regulation of replication timing in eukaryotes and demonstrates how initiation can be stochastic and yet occur at defined times during S phase, without an explicit timing program.
Abstract
Microarrays are powerful tools to probe genome-wide replication kinetics. The rich data sets that result contain more information than has been extracted by current methods of analysis. In this paper, we present an analytical model that incorporates probabilistic initiation of origins and passive replication. Using the model, we performed least-squares fits to a set of recently published time course microarray data on Saccharomyces cerevisiae. We extracted the distribution of firing times for each origin and found that the later an origin fires on average, the greater the variation in firing times. To explain this trend, we propose a model where earlier-firing origins have more initiator complexes loaded and a more accessible chromatin environment. The model demonstrates how initiation can be stochastic and yet occur at defined times during S phase, without an explicit timing program. Furthermore, we hypothesize that the initiators in this model correspond to loaded minichromosome maintenance complexes. This model is the first to suggest a detailed, testable, biochemically plausible mechanism for the regulation of replication timing in eukaryotes.

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Citations
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Journal ArticleDOI

DNA Replication Timing

TL;DR: In this article, a correlation between replication timing and the three-dimensional structure of chromosomes has been found, which suggests that replication timing is controlled at the level of chromosomal domains, and this conclusion dovetails with parallel work on the heterogeneity of origin firing and the competition between origins for limiting activators to suggest a model in which the stochastic probability of individual origin firing is modulated by chromosomal domain structure.
Journal ArticleDOI

Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective

TL;DR: This work reviews not only the structural design and stochastic kinetics of individual single motors, but also their coordination, cooperation and competition as well as the assembly of multi-module motors in various intracellular kinetic processes.
Journal ArticleDOI

OriDB, the DNA replication origin database updated and extended

TL;DR: The OriDB database and web site have been revamped to improve user accessibility to curated data sets, to greatly increase the number of curated origin mapping studies, and to include the collation of replication origin sites in the fission yeast Schizosaccharomyces pombe.
Journal ArticleDOI

Evidence for Sequential and Increasing Activation of Replication Origins along Replication Timing Gradients in the Human Genome

TL;DR: A quantitative genome-wide analysis of DNA replication kinetics in several human cell types that contradicts the view that mammalian chromosomes consist of megabase-scale domains of coordinated origin firing separated by large originless transition regions and sheds a new light on the replication timing program of mammalian genomes.
Journal ArticleDOI

Quantitative, Genome-Wide Analysis of Eukaryotic Replication Initiation and Termination

TL;DR: In this article, the authors comprehensively document replication fork directionality throughout the S. cerevisiae genome, which permits the systematic analysis of initiation, origin efficiency, fork progression, and termination.
References
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Book

Spikes: Exploring the Neural Code

TL;DR: Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory about the representation of sensory signals in neural spike trains and a quantitative framework is used to pose precise questions about the structure of the neural code.
Book

Extreme Value Distributions: Theory and Applications

TL;DR: Univariate extreme value distributions (UVD) as discussed by the authors, generalized extreme value distribution (GVD) and multivariate Extreme Value Distribution (MVDD) have been used to estimate the EVD.
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