{
  "abstract": "Disease reprograms cells through changes in gene regulation, yet identifying these changes remains a major challenge. We introduce NetDes-Duo, a computational method that jointly infers transcription factor regulatory network models for two related conditions using scRNA-seq data. The networks are optimized to have minimal topological differences, while the associated ODE models recapitulate single-cell gene expression trajectories for both conditions. On synthetic benchmarks, NetDes-Duo outperformed methods that infer each network independently. NetDes-Duo was applied to neutrophil reprogramming in naive and tumor-bearing mice, and the network-simulated dynamics reproduced the observed cell state transitions. The naive landscape had two well-separated basins, whereas the tumor-bearing landscape was more continuous, with three shallower basins. Perturbation and driving simulations also identified Cebpb as a key driver of the tumor-bearing transition, consistent with emergency granulopoiesis literature. We expect NetDes-Duo to be a broadly applicable framework for uncovering the regulatory logic of disease-associated cell state transitions.",
  "abstract_shown": true,
  "affiliations": [
    "Northeastern University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "A. Ren"
    },
    {
      "name": "Y. You"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5gfasfmqw59g5.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.16.752194",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752194v1.full",
  "id": "5gfasfmqw59g5",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752194v1",
  "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": "W7213539229",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752194v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5gfasfmqw59g5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:00Z",
      "native_id": "10.64898/2026.09.16.752194",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752194v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming",
  "title_text": "Joint inference of paired dynamical gene regulatory networks reveals distinct cell-state landscapes of neutrophil reprogramming",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:00Z",
  "url": "https://research.draconitic.com/paper/5gfasfmqw59g5",
  "venue": "bioRxiv",
  "versions": []
}