{
  "abstract": "Bacterial growth and the underlying metabolic networks are highly dissimilar across species, posing a fundamental challenge for various bioengineering tasks. For a given species across nutrient environments, growth is regulated via proteome allocation, which gives rise to linear relationships between growth and the fractions of coarse-grained proteome sectors. However, it remains unclear whether there are coarse-grained predictors that correlate with growth across species monocultures in a given environment. Here, simulating genome-scale metabolic models, we discover two conserved growth predictors: the number of nutrients utilized, and a set of metabolic sectors that we identify by coarse-graining metabolic networks via feature learning. We find evidence supporting both predictors in recent experiments; notably, the proteome fractions of the sectors correlate with growth across species in a gene expression dataset. We further analyze coarse-grained networks to show that interactions between sectors improve growth predictions. Our work provides a framework to investigate how coarse-grained growth predictors emerge across diverse metabolic networks.",
  "abstract_shown": true,
  "affiliations": [
    "Westlake University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "A. Zhu"
    },
    {
      "affs": [
        0
      ],
      "name": "P.-Y. Ho"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0jb2098vf3bb2.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.05.13.725055",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.05.13.725055v2.full",
  "id": "0jb2098vf3bb2",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.05.13.725055v2",
  "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|Microbial Metabolic Engineering and Bioproduction"
    ]
  ],
  "openalex": "W7161264043",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.05.13.725055v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0jb2098vf3bb2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:31Z",
      "native_id": "10.64898/2026.05.13.725055",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.05.13.725055v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:systems biology",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "Coarse-graining metabolic networks via feature learning reveals cross-species growth predictors",
  "title_text": "Coarse-graining metabolic networks via feature learning reveals cross-species growth predictors",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:31Z",
  "url": "https://research.draconitic.com/paper/0jb2098vf3bb2",
  "venue": "bioRxiv",
  "versions": []
}