{
  "abstract": "Previous research in microbes successfully predicted biculture growth based on monoculture growth curves. Still, the usual model-based approach does not seem to extend to communities involving more than two bacterial strains. Here, we use a model-blind machine-learning approach to predict community-wide yield, area under the growth curve, maximum relative growth rate and its timing in communities involving up to five strains of Escherichia coli. First, we identify the highest per-capita growth rate and its timing in monocultures as major predictors of multi-strain community growth. Next, we show that a random forest trained on communities involving a low number of strains is able to predict the outcomes of communities involving a higher number of strains. Finally, we reveal diminishing returns in using more and more complex communities in order to predict the behaviour of a higher-level community, because monoculture- and biculture-based growth predictions are often already accurate. This finding relativises the need for experiments involving high numbers of strains when studying the growth dynamics of multi-strain communities.",
  "abstract_shown": true,
  "affiliations": [
    "University of Helsinki"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "F. Borse"
    },
    {
      "name": "S. Lord Smits"
    },
    {
      "name": "T. A. Sun"
    },
    {
      "name": "J. Cairns"
    },
    {
      "name": "A. Farewell"
    },
    {
      "affs": [
        0
      ],
      "name": "V. Mustonen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2vwda4mje6gwf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.02.748814",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748814v1.full",
  "id": "2vwda4mje6gwf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748814v1",
  "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",
      "ecology"
    ],
    [
      "openalex",
      "2303|Microbial Community Ecology and Physiology"
    ]
  ],
  "openalex": "W7208768909",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748814v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2vwda4mje6gwf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:28Z",
      "native_id": "10.64898/2026.09.02.748814",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.02.748814v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:ecology",
      "subfield": "ecology"
    }
  ],
  "title": "How far can microbial monocultures predict growth in multi-strain communities?",
  "title_text": "How far can microbial monocultures predict growth in multi-strain communities?",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:28Z",
  "url": "https://research.draconitic.com/paper/2vwda4mje6gwf",
  "venue": "bioRxiv",
  "versions": []
}