A Stochastic Model for the Formation of Spatial Methylation Patterns

Lück, Alexander; Giehr, Pascal; Walter, Jörn; Wolf, Verena

doi:10.1007/978-3-319-67471-1_10

Cited by 7 publications

(12 citation statements)

References 20 publications

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“…Interdependence of CpGs in methylation dynamics has also been the subject of previous modeling studies. Multiple mechanisms have been suggested for this interdependence [39][40][41][42][43][44]59], from the enzymatic behavior of DNMT1 itself (e.g., through processivity) or in conjunction with other molecular species. This interdependence was found to play an important role in the stability of methylation inheritance [39,42].…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…Population epigenetic models have explored the interplay between processes including enzyme-mediated de novo methylation, maintenance methylation, demethylation, and replication [33][34][35][36][37][38]. Some models have incorporated various mechanisms of interdependence of CpGs, where, for example, the efficiency of maintenance methylation at a given site depends on the methylation status of its neighbors [39][40][41][42][43][44]. Biochemical studies have enabled the development of enzyme-kinetic models and parameter quantification for methyltransferase activity [17,45,46].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation

et al. 2020

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DNA methylation is a heritable epigenetic modification that plays an essential role in mammalian development. Genomic methylation patterns are dynamically maintained, with DNA methyltransferases mediating inheritance of methyl marks onto nascent DNA over cycles of replication. A recently developed experimental technique employing immunoprecipitation of bromodeoxyuridine labeled nascent DNA followed by bisulfite sequencing (Repli-BS) measures post-replication temporal evolution of cytosine methylation, thus enabling genomewide monitoring of methylation maintenance. In this work, we combine statistical analysis and stochastic mathematical modeling to analyze Repli-BS data from human embryonic stem cells. We estimate site-specific kinetic rate constants for the restoration of methyl marks on >10 million uniquely mapped cytosines within the CpG (cytosine-phosphate-guanine) dinucleotide context across the genome using Maximum Likelihood Estimation. We find that post-replication remethylation rate constants span approximately two orders of magnitude, with half-lives of per-site recovery of steady-state methylation levels ranging from shorter than ten minutes to five hours and longer. Furthermore, we find that kinetic constants of maintenance methylation are correlated among neighboring CpG sites. Stochastic mathematical modeling provides insight to the biological mechanisms underlying the inference results, suggesting that enzyme processivity and/or collaboration can produce the observed kinetic correlations. Our combined statistical/mathematical modeling approach expands the utility of genomic datasets and disentangles heterogeneity in methylation patterns arising from replication-associated temporal dynamics versus stable cell-to-cell differences.

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Section: Plos Computational Biologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation

et al. 2020

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“…We then use this knowledge to choose the correct transition probability depending on our assumptions. For the transition probabilities, we impose, depending on the neighbor states, the following forms [14]: For de novo methylation events (the state of the other strand does not matter) we have transition probabilities for non-boundary cases…”

Section: Cell Divisionmentioning

confidence: 99%

“…Since detailed mechanisms about the interaction of the Dnmts with the DNA remain elusive, we would like to test different assumptions such as processive or distributive methylation mechanisms [8,10,14,16]. For the remainder of this paper, we assume processivity from left to right, which is a reasonable assumption for Dnmt1, due to its link to the replication machinery.…”

Section: Example: Processivitymentioning

confidence: 99%

“…The goal of such models is to increase the understanding of methylation pattern formation. Due to the possible influence of the neighboring CpGs [9,12] generalizations of simple, single-CpG models have recently been developed, which take more than one CpG into account [2,13,14,15]. However, even for a small number of CpGs the size of the transition matrices grows rapidly and they are therefore hard to generate without suitable methods.…”

Section: Introductionmentioning

confidence: 99%

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A Stochastic Automata Network Description for Spatial DNA-Methylation Models

Lück

Wolf

2020

Lecture Notes in Computer Science

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DNA methylation is an important biological mechanism to regulate gene expression and control cell development. Mechanistic modeling has become a popular approach to enhance our understanding of the dynamics of methylation pattern formation in living cells. Recent findings suggest that the methylation state of a cytosine base can be influenced by its DNA neighborhood. Therefore, it is necessary to generalize existing mathematical models that consider only one cytosine and its partner on the opposite DNA-strand (CpG), in order to include such neighborhood dependencies. One approach is to describe the system as a stochastic automata network (SAN) with functional transitions. We show that single-CpG models can successfully be generalized to multiple CpGs using the SAN description and verify the results by comparing them to results from extensive Monte-Carlo simulations.

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A Hybrid HMM Approach for the Dynamics of DNA Methylation

Kyriakopoulos

Giehr

Lück

et al. 2019

Hybrid Systems Biology

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The understanding of mechanisms that control epigenetic changes is an important research area in modern functional biology. Epigenetic modifications such as DNA methylation are in general very stable over many cell divisions. DNA methylation can however be subject to specific and fast changes over a short time scale even in non-dividing (i.e. not-replicating) cells. Such dynamic DNA methylation changes are caused by a combination of active demethylation and de novo methylation processes which have not been investigated in integrated models.Here we present a hybrid (hidden) Markov model to describe the cycle of methylation and demethylation over (short) time scales. Our hybrid model decribes several molecular events either happening at deterministic points (i.e. describing mechanisms that occur only during cell division) and other events occurring at random time points. We test our model on mouse embryonic stem cells using time-resolved data. We predict methylation changes and estimate the efficiencies of the different modification steps related to DNA methylation and demethylation.

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A Stochastic Model for the Formation of Spatial Methylation Patterns

Cited by 7 publications

References 20 publications

Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation

Stochastic modeling reveals kinetic heterogeneity in post-replication DNA methylation

A Stochastic Automata Network Description for Spatial DNA-Methylation Models

A Hybrid HMM Approach for the Dynamics of DNA Methylation

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