2013
DOI: 10.1093/nar/gkt854
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Nucleosome positioning and kinetics near transcription-start-site barriers are controlled by interplay between active remodeling and DNA sequence

Abstract: We investigate how DNA sequence, ATP-dependent chromatin remodeling and nucleosome-depleted ‘barriers’ co-operate to determine the kinetics of nucleosome organization, in a stochastic model of nucleosome positioning and dynamics. We find that ‘statistical’ positioning of nucleosomes against ‘barriers’, hypothesized to control chromatin structure near transcription start sites, requires active remodeling and therefore cannot be described using equilibrium statistical mechanics. We show that, unlike steady-state… Show more

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Cited by 34 publications
(51 citation statements)
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“…We incorporated the effect of these proteins as an energetic barrier centered at 150 bp upstream of TSS to penalize nucleosome formation. Similar treatment has been used by Padinhateeri and coworkers to create a nucleosomefree region near TSS (39). As illustrated in Figure 2B, the barrier is symmetric with respect to the center.…”
Section: Methodsmentioning
confidence: 71%
“…We incorporated the effect of these proteins as an energetic barrier centered at 150 bp upstream of TSS to penalize nucleosome formation. Similar treatment has been used by Padinhateeri and coworkers to create a nucleosomefree region near TSS (39). As illustrated in Figure 2B, the barrier is symmetric with respect to the center.…”
Section: Methodsmentioning
confidence: 71%
“…Furthermore, subsequent experimental work of Celona et al [30] showed that only weak nucleosomes are being removed upon core histone depletion, while strong nucleosomes remain at their places, effectively keeping the NRL independent of the core histone concentration. In line with this, the authors of a recent Monte Carlo simulation hypothesize that DNA sequence-specific effects have the primary role on nucleosome positioning near TSSs [72]. To check this hypothesis in a more general context, we have utilized the nucleosome positioning data measured by MNase-seq for mouse embryonic stem cells (mESCs) [28].…”
Section: Resultsmentioning
confidence: 97%
“…Using these set of phenomenological rates, we allow the system to evolve using the exact stochastic simulation method or the Gillespie kinetic Monte Carlo algorithm [20]. This algorithm has previously been used to model biological processes as diverse as gene regulation [33] and cytoskeletal filament growth [34]. In this approach, a set of 'N' rates is initiated for any given current configuration (set of coordinates of each particle and their valencies).…”
Section: Kinetic Monte Carlo Simulationsmentioning
confidence: 99%