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肠道微生物群时间序列的生长速率分布

Growth-rate distributions of gut microbiota time series.

作者信息

Brigatti E, Azaele S

机构信息

Instituto de Física, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 149, Cidade Universitária, Rio de Janeiro, RJ, 21941-972, Brazil.

Dipartimento di Fisica "G. Galilei", Università di Padova, Via Marzolo 8, 35131, Padua, Italy.

出版信息

Sci Rep. 2025 Jan 22;15(1):2789. doi: 10.1038/s41598-024-82882-x.

Abstract

Logarithmic growth-rates are fundamental observables for describing ecological systems and the characterization of their distributions with analytical techniques can greatly improve their comprehension. Here a neutral model based on a stochastic differential equation with demographic noise, which presents a closed form for these distributions, is used to describe the population dynamics of microbiota. Results show that this model can successfully reproduce the log-growth rate distribution of the considered abundance time-series. More significantly, it predicts its temporal dependence, by reproducing its kurtosis evolution when the time lag τ is increased. Furthermore, its typical shape for large τ is assessed, verifying that the distribution variance does not diverge with τ. The simulated processes generated by the calibrated stochastic equation and the analysis of each time-series, taken one by one, provided additional support for our approach. Alternatively, we tried to describe our dataset by using a logistic neutral model with an environmental stochastic term. Analytical and numerical results show that this model is not suited for describing the leptokurtic log-growth rates distribution found in our data. These results support an effective neutral model with demographic stochasticity for describing the considered microbiota.

摘要

对数增长率是描述生态系统的基本可观测指标,运用分析技术对其分布特征进行刻画能够极大地增进我们对生态系统的理解。在此,我们使用一个基于带有种群统计噪声的随机微分方程的中性模型(该模型给出了这些分布的封闭形式)来描述微生物群的种群动态。结果表明,该模型能够成功再现所考虑的丰度时间序列的对数增长率分布。更重要的是,通过在时间滞后τ增加时再现其峰度演变,该模型预测了对数增长率分布的时间依赖性。此外,我们评估了τ较大时其典型形状,证实分布方差不会随τ发散。校准后的随机方程生成的模拟过程以及对每个时间序列逐一进行的分析,为我们的方法提供了额外支持。另外,我们尝试使用带有环境随机项的逻辑斯谛中性模型来描述我们的数据集。分析和数值结果表明,该模型不适用于描述我们数据中发现的尖峰态对数增长率分布。这些结果支持了一个具有种群统计随机性的有效中性模型,用于描述所考虑的微生物群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c840/11754794/97c41eae44b1/41598_2024_82882_Fig1_HTML.jpg

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