{
  "abstract": "Shotgun metagenomics routinely resolves microbial community composition down to the strain level via metagenome-assembled genomes (MAGs), but translating these static catalogs into mechanistic, time-resolved understandings of ecosystem dynamics remains a critical computational bottleneck. Here, we present muODE, a fully automated framework that converts MAGs into dynamical simulations of community metabolism equipped with a composable, literature-grounded ecology layer, fundamentally designed to serve as a mechanistic hypothesis-testing engine. It employs dynamic flux balance analysis (dFBA) coupled to a shared extracellular pool and integrates a composable ecology layer capturing pH inhibition, time-varying antibiotic pharmacokinetics, bile-acid transformations, and spore dormancy. To evaluate the core engine, we cross-validated the dFBA machinery to machine precision against an independent reference implementation. We provide native export to the established COMETS engine for third-party verification. Benchmarking against two independent experimentally validated datasets confirmed that while monoculture growth is robustly captured, overflow secretion and directed community composition remain strictly bounded by the flux-routing limitations of max-biomass FBA. However, when applied to rate-modifying ecological feedback, muODE mechanistically and quantitatively reproduces bile-acid-mediated colonization resistance against Clostridioides difficile. With a single rate parameter calibrated so that baseline deoxycholate matched its measured physiological concentration, the framework reproduced the uncalibrated antibiotic-induced collapse of deoxycholate, with the calibrated inhibitor concentration coinciding with the independently set inhibitory threshold of the pathogen and driving a ~44x shift in the simulated pathogen-susceptibility ratio; bile-acid transformation is represented in the composable ecology layer rather than in the genome-scale stoichiometry. Finally, applying muODE end-to-end to a real longitudinal fecal microbiota transplant (FMT) yielding 139 de novo genome-scale metabolic models, the framework successfully recovered the clinical direction of treatment. By transparently diagnosing the structural competitive-exclusion bias that limits precise compositional prediction, muODE motivates the necessity of community-objective extensions while transitioning microbiome modeling from descriptive taxonomy toward testable, quantitatively-validated dynamic hypothesis testing, laying the essential mechanistic groundwork for the predictive engineering of safe, targeted ecological therapeutics. muODE is freely available at https://github.com/cumbof/muODE.",
  "abstract_shown": true,
  "affiliations": [
    "Cleveland Clinic Foundation"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "F. Cumbo"
    },
    {
      "name": "J. Joshi"
    },
    {
      "name": "B. Raubenolt"
    },
    {
      "name": "N. Katzenmeyer"
    },
    {
      "name": "J. Claesen"
    },
    {
      "affs": [
        0
      ],
      "name": "D. Blankenberg"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/46n1g47ws5q67.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.09.758137",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758137v1.full",
  "id": "46n1g47ws5q67",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758137v1",
  "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",
      "microbiology"
    ],
    [
      "openalex",
      "1312|Gut microbiota and health"
    ]
  ],
  "openalex": "W7221557280",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758137v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/46n1g47ws5q67.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "10.64898/2026.10.09.758137",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.09.758137v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "Mechanistic prediction of fecal microbiota transplant engraftment and Clostridioides difficile colonization resistance with μODE",
  "title_text": "Mechanistic prediction of fecal microbiota transplant engraftment and Clostridioides difficile colonization resistance with μODE",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:22Z",
  "url": "https://research.draconitic.com/paper/46n1g47ws5q67",
  "venue": "bioRxiv",
  "versions": []
}