{
  "abstract": "Machine learning and genome-scale metabolic modeling have accelerated the discovery of metabolic-regulatory relationships in model microbes and mammalian cells. This review provides a roadmap for integrating omics with computational methods for studying systems-level transcriptional, post-translational, and allosteric regulation. Developing methods to infer and integrate multiple regulatory mechanisms simultaneously is essential for discovering novel modes of metabolic regulation and precise engineering of metabolic networks.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.",
    "Department of Pharmacology, University of Michigan, Ann Arbor, MI, USA.",
    "Fred Hutchinson Cancer Center, Seattle, WA, USA.",
    "Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. csriram@umich.edu.",
    "Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI, USA. csriram@umich.edu.",
    "Program in Chemical Biology, University of Michigan, Ann Arbor, MI, USA. csriram@umich.edu.",
    "Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, USA. csriram@umich.edu.",
    "Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, USA. csriram@umich.edu."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Ryan Schildcrout",
      "orcid": "0009-0008-9502-1504"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Nicole Rhoads"
    },
    {
      "affs": [
        3,
        4,
        5,
        6,
        7
      ],
      "name": "Sriram Chandrasekaran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3ertv7v1a9kjs.bib",
  "cited_by": 0,
  "doi": "10.1038/s44324-026-00138-w",
  "fulltext_url": "https://europepmc.org/articles/PMC13728217",
  "id": "3ertv7v1a9kjs",
  "issns": [
    "2948-2828"
  ],
  "landing_url": "https://europepmc.org/article/MED/42972011",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Microbial Metabolic Engineering and Bioproduction"
    ]
  ],
  "openalex": "W7225089056",
  "pdf_url": "https://europepmc.org/articles/PMC13728217?pdf=render",
  "pmcid": "PMC13728217",
  "pmid": "42972011",
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3ertv7v1a9kjs.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:20:43Z",
      "native_id": "MED:42972011",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42972011"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Microbial Metabolic Engineering and Bioproduction",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling.",
  "title_text": "Roadmap for inferring metabolic regulation using multiomics, machine learning, and genome-scale modeling.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T03:20:55Z",
  "url": "https://research.draconitic.com/paper/3ertv7v1a9kjs",
  "venue": "Npj metabolic health and disease",
  "versions": []
}