Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

Author:

Arya Shreya1ORCID,George Ashish B.234ORCID,O’Dwyer James P.24ORCID

Affiliation:

1. Department of Physics, University of Illinois, Urbana-Champaign, Urbana, IL 61801

2. Center for Artificial Intelligence and Modeling, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801

3. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 0214

4. Department of Plant Biology, University of Illinois, Urbana-Champaign, Urbana, IL 61801

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 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.

Funder

Simons Foundation

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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