ArticleProceedings of the National Academy of Sciences of the United States of America2023
Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Emergent coexistence and the limits of reductionism in ecological communities.PLoS computational biology · 2026Article
- Microbial biotechnology meets ecological theory: a unified framework to design industrial microbial consortia.International microbiology : the official journal of the Spanish Society for Microbiology · 2026Review
- A foundation model for microbial growth dynamics.bioRxiv : the preprint server for biology · 2025Article
- Applying ecological principles to microbiome engineering.Nature microbiology · 2025Review
- Multispecies Coexistence Emerges From Pairwise Exclusions in Communities With Competitive Hierarchy.Ecology letters · 2025Article
- Article
- Hypergraph reconstruction from dynamics.Nature communications · 2025Article
- Competition for shared resources increases dependence on initial population size during coalescence of gut microbial communities.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Synthetic Ecosystems: From the Test Tube to the Biosphere.ACS synthetic biology · 2024Review
- Article
- The sparse macroecology of microbiology.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- An evolution-based framework for describing human gut bacteria.bioRxiv : the preprint server for biology · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.
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Registered trials
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.