{
  "abstract": "Uncovering stochastic reaction networks from data is central to single-cell biology. Simulation-based inference has evolved into a flexible paradigm for this purpose, circumventing the computational intractability of the likelihood. Notably, approximate Bayesian computation performs this inference by carrying out extensive Gillespie simulations, usually at a high computational cost. Here, we propose a simulation-free approximate Bayesian computation (SFABC) framework that avoids both simulation and explicit likelihood solutions of the network dynamics. Instead, SFABC exploits the Chemical Master Equation to formulate a tractable convex optimization with ensuing constraints, which are then used to accept sampled parameters consistent with the observed data. The resulting posterior approximation comes with theoretical guarantees: it can be tightened systematically, and it provably converges to the exact Bayesian posterior distributions. Using synthetic benchmarks, we show that SFABC matches the accuracy of standard simulation-based algorithms at a substantially reduced computational cost. We then showcase the applicability of SFABC to experimental systems biology by analyzing gene transcription bursting kinetics from snapshot, time-resolved, and cell-cycle-resolved single-cell data.",
  "abstract_shown": true,
  "affiliations": [
    "Imperial College London"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Z. Li"
    },
    {
      "name": "M. Barahona"
    },
    {
      "affs": [
        0
      ],
      "name": "P. Thomas"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c8fr1xc005ng1.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.19.760104",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760104v1.full",
  "id": "c8fr1xc005ng1",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760104v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7223051461",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760104v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/c8fr1xc005ng1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:45Z",
      "native_id": "10.64898/2026.10.19.760104",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.19.760104v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Simulation-free approximate Bayesian computation for stochastic reaction networks",
  "title_text": "Simulation-free approximate Bayesian computation for stochastic reaction networks",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:45Z",
  "url": "https://research.draconitic.com/paper/c8fr1xc005ng1",
  "venue": "bioRxiv",
  "versions": []
}