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An efficient and scalable top-down method for predicting structures of microbial communities

Abstract

Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.

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Fig. 1: Schematic of EPICS.
Fig. 2: Structure prediction of in silico microbial communities.
Fig. 3: Robustness to parameter variations.
Fig. 4: Structure prediction of large communities and subcommunities.
Fig. 5: Capturing the abundances in a five-species gut microbial community of Drosophila melanogaster.
Fig. 6: Capturing the abundances in an eight-species oral microbial community.

Data availability

Source Data are provided with this paper.

Code availability

The computer code used to implement EPICS is available on GitHub (https://github.com/narendradixit/EPICS/tree/v1) and on Zenodo62.

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Acknowledgements

We thank K. Raman and S. Jhunjhunwala for insightful comments.

Author information

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Authors

Contributions

A.F.A. and N.M.D. developed the theory. A.F.A. performed the numerical simulations and analyzed the empirical data. Y.B.S.R. and J.R. performed the experiments. All authors interpreted the results and wrote the manuscript.

Corresponding author

Correspondence to Narendra M. Dixit.

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The authors declare no competing interests.

Additional information

Peer review information Nature Computational Science thanks Boyang Ji and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Editor recognition statement Handling editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Consistency of effective pairwise interactions from leave-one-out and leave-two-out subcommunities.

The RMS relative error between the interactions estimated using n − 1 species subcommunities and n − 2 species subcommunities for different n using parameter settings corresponding to the first four points in the 3D diagonal in Fig. 3A, indicated as 1, 2, 3, and 4 above.

Source data

Extended Data Fig. 2 Exactness of EPICS with purely pairwise interactions.

Comparisons between effective pairwise interactions estimated using EPICS and true pairwise interactions underlying in silico 5-species communities following the GLV model (see Methods). The interaction strengths are depicted in the panels. (A) Pairwise interactions alone are present. (B) Pairwise and ternary interactions present; (C) Pairwise and quaternary interactions present; (D) All three interaction types present. Each dot represents one pairwise interaction in one realization. The red dashed line represents y=x.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, and Figs. 1–15.

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Source Data Fig. 6

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 2

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Ansari, A.F., Reddy, Y.B.S., Raut, J. et al. An efficient and scalable top-down method for predicting structures of microbial communities. Nat Comput Sci 1, 619–628 (2021). https://doi.org/10.1038/s43588-021-00131-x

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