Structured community transitions explain the switching capacity of microbial systems

• 成果类型: Article
• 署名作者: Long, Chengyi; Deng, Jie; Nguyen, Jen; Liu, Yang-Yu; Alm, Eric J.; Sole, Ricard; Saavedra, Serguei
• 署名单位: Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Harvard Medical School; University of Illinois System; University of Illinois Urbana-Champaign; Pompeu Fabra University; ICREA; Consejo Superior de Investigaciones Cientificas (CSIC); CSIC-UPF - Institut de Biologia Evolutiva (IBE); Pompeu Fabra University; The Santa Fe Institute
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11665
• DOI: 10.1073/pnas.2312521121
• 发表日期: 2024-01-29
• 关键词: NETWORKS

摘要

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.