Mathematical model of transcription loss due to accumulated DNA damage

2024 · doi:10.1101/2024.07.15.603615
preprint OA: closed
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The paper develops a mathematical model describing gene transcription loss over time caused by accumulated DNA damage, using widely accepted biological assumptions and stochastic processes. It derives closed-form expressions for the distribution of transcription loss after specified numbers of DNA damage events and analyzes the asymptotic behavior of the process. The authors also study the distribution of the first hitting time for transcription loss to reach biologically relevant thresholds, using both analytical work and computational analysis on mice data, with an explicit limitation that the model is framed as a simplified mathematical representation rather than a direct measurement study. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

We offer a simple mathematical model of gene transcription loss due to accumulated DNA damage in time based on widely agreed biological axioms. Closed form formulae characterizing the distribution of the underlying stochastic processes representing the transcription loss upon specified number of DNA damages are obtained. Moreover, the asymptotic behavior of the stochastic process was analyzed. Finally, the distribution of the first hitting time of transcription loss to specified biologically relevant levels was studied both analytically and computationally on mice data.
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Abstract We offer a simple mathematical model of gene transcription loss due to accumulated DNA damage in time based on widely agreed biological axioms. Closed form formulae characterizing the distribution of the underlying stochastic processes representing the transcription loss upon specified number of DNA damages are obtained. Moreover, the asymptotic behavior of the stochastic process was analyzed. Finally, the distribution of the first hitting time of transcription loss to specified biologically relevant levels was studied both analytically and computationally on mice data. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵* m.raseta{at}erasmusmc.nl Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

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