Given gut microbiota's important role in human health, a clear understanding of the microbial ecosystem dynamics is vital. Mathematical models for analyzing time-series data of gut microbiotas would, therefore, be highly beneficial. Although a generalized Lotka–Volterra (gLV) model for identifying the interactions between gut microbiota members and quantifying the effects of external factors already exists, it is limited in its practical applications. Therefore, we established a stochastic gLV model for analyzing temporal data about the gut microbiota from a local community perspective and provided a reliable parameter estimation method for our model. Our model has abilities similar to those of the existing gLV model but can also capture emigration/immigration effects and avoid the existing model's inadequacies. To test our model's applicability, we fitted our model on a previously published data set. We found the interactions between the gut microbiota of different individuals and between different periods for the same individual were different in the data sets. Analysis of the random dynamic characteristics of the gut microbiota revealed that the number of microorganisms in the local community tended to decrease as a result of random factors, but the numbers were restored by the immigration of external microbes .

鉴于肠道微生物群在人类健康中的重要作用，清楚地了解微生物生态系统动态至关重要。因此，用于分析肠道微生物群时间序列数据的数学模型将非常有益。尽管用于识别肠道微生物群成员之间相互作用和量化外部因素影响的广义 Lotka-Volterra (gLV) 模型已经存在，但它在实际应用中受到限制。因此，我们建立了一个随机 gLV 模型，用于从当地社区的角度分析肠道微生物群的时间数据，并为我们的模型提供可靠的参数估计方法。我们的模型具有与现有 gLV 模型相似的能力，但也可以捕捉移民/移民效应并避免现有模型的不足。测试我们的模型' 的适用性，我们将我们的模型拟合到先前发布的数据集上。我们发现不同个体肠道微生物群之间的相互作用以及同一个体不同时期之间的相互作用在数据集中是不同的。对肠道菌群随机动态特征的分析表明，当地群落微生物数量因随机因素而趋于减少，但由于外部微生物的迁入，数量有所恢复。