{
  "abstract": "Predictive logical models have made substantial contributions to biomedical research by facilitating the identification of regulatory mechanisms driving biological processes. Yet, their construction remains a major bottleneck, as it typically relies on extensive prior knowledge at the genome scale. The emergence of high-throughput omics technologies has opened new opportunities for data-driven logical model construction by providing high-resolution observations of biological states. In parallel, formal constraint-based approaches have emerged, reformulating executable model inference as a constraint-satisfaction problem. However, transforming omics observations into biologically meaningful Boolean state abstractions and dynamical constraints suitable for logical synthesis engines remains challenging, particularly for poorly understood biological systems and multi-condition experimental settings. To address these challenges, we introduce scBOLT, a semi-automated framework for Boolean constraint engineering and logical model inference from multi-condition single-cell transcriptomes. Beyond these methodological contributions, scBOLT substantially improves computational tractability, enabling the inference of Boolean networks comprising several hundred components within practical runtimes.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "T. Roncalli"
    },
    {
      "name": "E. Remy"
    },
    {
      "name": "L. Pauleve"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6wdmae8z74915.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.02.756175",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756175v1.full",
  "id": "6wdmae8z74915",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756175v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "systems biology"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7220420223",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756175v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6wdmae8z74915.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:02Z",
      "native_id": "10.64898/2026.10.02.756175",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.02.756175v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT",
  "title_text": "Scalable and semi-automated Boolean network inference from multi-condition single-cell transcriptomes with scBOLT",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:02Z",
  "url": "https://research.draconitic.com/paper/6wdmae8z74915",
  "venue": "bioRxiv",
  "versions": []
}