{
  "abstract": "Stochastic models of gene expression typically begin with a microscopic model of transcription and propagate its statistics to the RNA distribution. Here we develop a doubly stochastic framework in which RNA production is Poisson conditional on a time-varying transcription rate $λ(t)$. For RNA lifetimes drawn independently from an arbitrary finite-mean distribution, we derive the RNA Fano factor in terms of the lifetime survival function and the autocovariance of $λ(t)$. For a Poisson degradation process with rate $μ$, the survival kernel provides an exponential temporal filter, and the result depends only on the mean, variance, and normalized autocorrelation of $λ(t)$. These quantities may be calculated from an explicit rate model or, under ergodicity and adequate sampling, estimated from a sufficiently long rate trajectory. We validate the data-driven estimator using simulated transcription-rate trajectories, without supplying the known autocorrelation to the estimator. We also obtain exact analytical results for transcription-rate dynamics modeled by a discrete M/M/1 process and by drift-diffusion with reflecting, periodic, or first-passage-reset boundaries, and verify each result by direct simulation of the coupled transcription-rate and RNA copy-number processes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.07993",
  "author_total": 4,
  "authors": [
    {
      "name": "Amara McCune"
    },
    {
      "name": "Ido Golding"
    },
    {
      "name": "Shlomi Reuveni"
    },
    {
      "name": "Sarah Kostinski"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ajh095y70n2fh.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.07993",
  "fulltext_url": null,
  "id": "ajh095y70n2fh",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.07993",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.stat-mech"
    ],
    [
      "arxiv",
      "physics.bio-ph"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7212029531",
  "pdf_url": "https://arxiv.org/pdf/2609.07993",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ajh095y70n2fh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:49Z",
      "native_id": "2609.07993",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.07993"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.bio-ph",
      "subfield": "biological-medical"
    },
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.stat-mech",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "Transcription rate dynamics and RNA copy number noise: General relations and data-driven predictions",
  "title_text": "Transcription rate dynamics and RNA copy number noise: General relations and data-driven predictions",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:49Z",
  "url": "https://research.draconitic.com/paper/ajh095y70n2fh",
  "venue": "arXiv",
  "versions": []
}