Evidence map›Paper›PMID 42385706›Full record

ArticleCell systems2026

A data-driven modeling framework for mapping genotypes to synthetic microbial community functions.

Yili Qian, Sarvesh D Menon, Hanchen Huang, Nick Quinn-Bohmann, Sean M Gibbons, Ophelia S Venturelli

Abstract read
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In one paragraph

Article in Cell systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Learning functional groups in complex microbiomes.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yili QianDepartment of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA.
Sarvesh D MenonDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Hanchen HuangDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Nick Quinn-BohmannInstitute of Systems Biology, Seattle, WA 98109, USA.
Sean M GibbonsInstitute of Systems Biology, Seattle, WA 98109, USA; Department of Bioengineering, University of Washington, Seattle, WA 98195, USA; Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; eScience Institute, University of Washington, Seattle, WA 98195, USA; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA.
Ophelia S VenturelliDepartment of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, USA. Electronic address: ophelia.venturelli@duke.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synthetic microbial communities offer valuable insights into the mechanisms that govern community functions, and they can be designed to achieve desired functions in order to address societal challenges in precision medicine and agriculture. Existing computational models for predicting synthetic community functions use species abundances as inputs; this makes it impossible to predict the effects of species not included in training data. We bridge this gap using a data-driven community genotype-function (dCGF) modeling framework. By lifting the representation of each species to a high-dimensional genetic feature (GF) space, dCGF learns a mapping from community GF matrices to community functions. Using in silico and experimental data, we demonstrate that dCGF can accurately predict community functions that are composed partly or entirely of new species. In addition, dCGF can generate hypotheses about the contribution of specific GFs to community functions. In sum, dCGF uses genetic information to model synthetic microbial communities in order to empower their model-driven design. A record of this paper's transparent peer review process is included in the supplemental information.

Indexed as

MicrobiotaModels, BiologicalSynthetic BiologyComputer SimulationGenotypeMachine LearningPrediction Algorithmscommunity functiongenetic informationmachine learningsynthetic microbial communitiessystems biology

Identifiers

PMID42385706

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.