Boddu Satya Spandana, Martini K Michael, Nemenman Ilya, Vega Nic M
Department of Physics, Emory University, Atlanta, Georgia, United States of America.
Initiative for Theory and Modeling of Living Systems, Emory University, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2025 Jun 9;21(6):e1013110. doi: 10.1371/journal.pcbi.1013110. eCollection 2025 Jun.
Variation in bacterial composition inside a host is a result of complex dynamics of microbial community assembly, but little is known about these dynamics. To deconstruct the factors that contribute to this variation, we used a combination of experimental and modeling approaches. We found that demographic stochasticity and stationary heterogeneity in the host carrying capacity or bacterial growth rate are insufficient to explain quantitatively the variation observed in our empirical data. Instead, we found that the data can be understood if the host-bacteria system can be viewed as stochastically switching between high and low growth rates phenotypes. This suggests the dynamics are significantly more complex than logistic growth used in canonical models of microbiome assembly. We develop mathematical models of this process that can explain various aspects of our data. We highlight the limitations of snapshot data in describing variation in host-associated communities and the importance of using time-series data along with mathematical models to understand microbial dynamics within a host.
宿主体内细菌组成的变化是微生物群落组装复杂动态过程的结果，但对这些动态过程了解甚少。为了解构导致这种变化的因素，我们结合了实验和建模方法。我们发现，宿主承载能力或细菌生长速率中的人口统计学随机性和静态异质性不足以定量解释我们实证数据中观察到的变化。相反，我们发现，如果宿主 - 细菌系统可以被视为在高生长速率表型和低生长速率表型之间随机切换，那么这些数据是可以理解的。这表明其动态过程比微生物群落组装经典模型中使用的逻辑斯谛增长要复杂得多。我们开发了该过程的数学模型，它可以解释我们数据的各个方面。我们强调了快照数据在描述宿主相关群落变化方面的局限性，以及使用时间序列数据和数学模型来理解宿主体内微生物动态的重要性。
