{
  "abstract": "Sparse and irregular longitudinal metabolomic measurements can make dynamical quantities numerically estimable without making them scientifically identifiable. We formulate effective-landscape inference as an observation-timescale inverse problem and evaluate its operating characteristics using stochastic systems with known ground truth and longitudinal stool metabolomics from the Integrative Human Microbiome Project. Across simulation ensembles, recovery of a known double-well topology declined sharply as observations became sparse, whereas the tested monostable controls did not generate spurious secondary minima. Findings establish an identifiability hierarchy: global topology requires sufficient within-subject state-space coverage, whereas local return descriptors remain vulnerable to bias from the observation process. The framework therefore shifts the emphasis from assigning stability labels to determining what a sampling design can support. It provides a basis for designing denser longitudinal experiments and for validating clinical and machine-learning studies in which sparse observations might otherwise be overinterpreted as evidence about underlying dynamics.",
  "abstract_shown": true,
  "affiliations": [
    "UC Berkeley"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "J. Aminian-Dehkordi"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Mofrad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61tpkq3e5bwr6.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.01.742207",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742207v2.full",
  "id": "61tpkq3e5bwr6",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742207v2",
  "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|Metabolomics and Mass Spectrometry Studies"
    ]
  ],
  "openalex": "W7172284007",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742207v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/61tpkq3e5bwr6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:14Z",
      "native_id": "10.64898/2026.08.01.742207",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.01.742207v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "Identifiability of metabolic resilience from sparse longitudinal metabolomics",
  "title_text": "Identifiability of metabolic resilience from sparse longitudinal metabolomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:14Z",
  "url": "https://research.draconitic.com/paper/61tpkq3e5bwr6",
  "venue": "bioRxiv",
  "versions": []
}