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A Stochastic Automata Network Description for Spatial DNA-Methylation Models

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Measurement, Modelling and Evaluation of Computing Systems (MMB 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12040))

Abstract

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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Authors and Affiliations

  1. Department of Computer Science, Saarland University, Saarbrücken, Germany

    Alexander Lück & Verena Wolf

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  1. Alexander Lück
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  2. Verena Wolf
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Correspondence to Alexander Lück .

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  1. Saarland University, Saarbrücken, Germany

    Holger Hermanns

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Lück, A., Wolf, V. (2020). A Stochastic Automata Network Description for Spatial DNA-Methylation Models. In: Hermanns, H. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2020. Lecture Notes in Computer Science(), vol 12040. Springer, Cham. https://doi.org/10.1007/978-3-030-43024-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-43024-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43023-8

  • Online ISBN: 978-3-030-43024-5

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