Structured community transitions explain the switching capacity of microbial systems

Author:

Long Chengyi1ORCID,Deng Jie1ORCID,Nguyen Jen23ORCID,Liu Yang-Yu45ORCID,Alm Eric J.123,Solé Ricard6789,Saavedra Serguei79ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139

2. Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139

3. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139

4. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115

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

6. Complex Systems Lab, Universitat Pompeu Fabra, Barcelona 08003, Spain

7. Institució Catalana de Recerca i Estudis Avançats, Barcelona 08010, Spain

8. Institute of Evolutionary Biology, Spanish National Research Council (CSIC)-Universitat Pompeu Fabra, Barcelona 08003, Spain

9. Santa Fe Institute, Santa Fe, NM 87501

Abstract

Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space—which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions: one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.

Funder

MIT-Takeda Graduate Fellowship

MIT-MATLAB

NSF Postdoctoral Research Fellowships in Biology Program

MathWorks

Generalitat de Catalunya

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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