Department of Biology, Boston College , Chestnut Hill, Massachusetts, USA.
Department of Molecular Virology & Microbiology, Alkek Center for Metagenomics & Microbiome Research and Division of Infectious Diseases, Texas Children's Hospital, Department of Pediatrics, Baylor College of Medicine , Houston, Texas, USA.
mSystems. 2023 Jun 29;8(3):e0075722. doi: 10.1128/msystems.00757-22. Epub 2023 Jun 6.
To alter microbial community composition for therapeutic purposes, an accurate and reliable modeling framework capable of predicting microbial community outcomes is required. Lotka-Volterra (LV) equations have been utilized to describe a breadth of microbial communities, yet, the conditions in which this modeling framework is successful remain unclear. Here, we propose that a set of simple experiments-growing each member in cell-free spent medium obtained from other members-can be used as a test to decide whether an LV model is appropriate for describing microbial interactions of interest. We show that for LV to be a good candidate, the ratio of growth rate to carrying capacity of each isolate when grown in the cell-free spent media of other isolates should remain constant. Using an community of human nasal bacteria as a tractable system, we find that LV can be a good approximation when the environment is low-nutrient (i.e., when growth is limited by the availability of nutrients) and complex (i.e., when multiple resources, rather than a few, determine growth). These findings can help clarify the range of applicability of LV models and reveal when a more complex model may be necessary for predictive modeling of microbial communities. IMPORTANCE Although mathematical modeling can be a powerful tool to draw useful insights in microbial ecology, it is crucial to know when a simplified model adequately represents the interactions of interest. Here, we take advantage of bacterial isolates from the human nasal passages as a tractable model system and conclude that the commonly used Lotka-Volterra model can represent interactions among microbes well when the environment is complex (with many interaction mediators) and low-nutrient. Our work highlights the importance of considering both realism and simplicity when choosing a model to represent microbial interactions.
为了达到治疗目的而改变微生物群落组成,需要一个能够准确可靠地预测微生物群落结果的建模框架。Lotka-Volterra(LV)方程已被用于描述广泛的微生物群落,但该建模框架成功的条件仍不清楚。在这里,我们提出一组简单的实验——在从其他成员获得的无细胞废弃培养基中培养每个成员——可以用作决定 LV 模型是否适合描述感兴趣的微生物相互作用的测试。我们表明,对于 LV 作为一个良好的候选者,当在其他分离物的无细胞废弃培养基中生长时,每个分离物的增长率与承载能力的比值应该保持不变。使用人类鼻腔细菌群落作为一个易于处理的系统,我们发现当环境是低营养(即生长受营养供应的限制)和复杂(即,当多种资源而不是少数资源决定生长时)时,LV 可以很好地近似。这些发现可以帮助阐明 LV 模型的适用范围,并揭示何时需要更复杂的模型来对微生物群落进行预测建模。
重要性
虽然数学建模可以成为微生物生态学中提取有用见解的有力工具,但知道何时简化模型可以充分代表感兴趣的相互作用至关重要。在这里,我们利用人类鼻腔中的细菌分离物作为一个易于处理的模型系统,并得出结论,当环境复杂(有许多相互作用介质)且营养水平低时,常用的 Lotka-Volterra 模型可以很好地代表微生物之间的相互作用。我们的工作强调了在选择代表微生物相互作用的模型时考虑现实性和简单性的重要性。
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