Compositional Lotka-Volterra describes microbial dynamics in the simplex
Compositional Lotka-Volterra describes microbial dynamics in the simplex
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DOI：
10.1371/journal.pcbi.1007917
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发表时间：
2020-05-01
影响因子：
4.3
通讯作者：
Peer, Itsik
中科院分区：
文献类型：
--
作者：
Joseph, Tyler A.;Shenhav, Liat;Peer, Itsik
微生物群落的动态变化在人类健康和疾病中起着重要作用。具体来说，破译群落中的微生物物种如何相互作用及其环境可以阐明疾病的机制，这是一个通常使用群落生态学工具研究的问题。然而，这种方法需要测量绝对密度，而典型的数据集只提供相对丰度的估计。在这里，我们系统地研究模型的微生物动力学的相对丰度的单纯形。我们推导出一个新的非线性动力学系统的微生物动力学，被称为“组成”的Lotka-Volterra（cLV），统一的方法使用广义Lotka-Volterra（gLV）方程从社区生态学和组成数据分析。在三个真实的数据集上，我们证明了cLV概括了gLV所暗示的相对丰度之间的相互作用。此外，我们表明，cLV是准确的gLV在预测微生物的相对丰度的轨迹。我们进一步比较cLV的其他两个模型的相对丰度动力学的共同假设在文献中的对数比变换空间的线性模型，和相对丰度的空间中的线性模型，并提供证据表明，cLV更准确地描述社区的轨迹随着时间的推移。最后，我们调查的直接影响的信息时，可以从相对数据，天真地提供信息，只有间接影响。我们的研究结果表明，强大的影响可能是可恢复的相对数据，但更微妙的影响是具有挑战性的identified.Author summaryDynamic微生物群落的变化在人类健康和疾病中发挥着重要作用。具体来说，破译群落中的微生物物种如何相互作用及其环境可以阐明疾病的机制，这是一个通常使用群落生态学工具研究的问题。然而，这些方法需要测量绝对密度，而典型的方法仅提供相对丰度的估计。我们研究的方法来描述微生物动力学的相对丰度，使用机器学习和动力系统的方法。在三个真实的数据集，我们表明，相对丰度足以描述成分动态。此外，我们还发现，仅在相对丰度上训练的模型可以预测未来的组合物，以及在绝对丰度上训练的模型。最后，我们提供了标准时，通常只能从绝对丰度的直接影响，是可恢复的相对数据。作为概念的证明，我们概括了以前提出的C的交互网络。艰难的殖民
Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed "compositional" Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature-a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances-and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify.Author summaryDynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical only provide estimates of relative abundances. We investigate methods for describing microbial dynamics in terms of relative abundances using approaches from machine learning and dynamical systems. Across three real datasets, we show that relative abundances are sufficient to describe compositional dynamics. Additionally, we show that models trained on relative abundances alone predict future compositions as well models trained on absolute abundances. Finally, we provide criteria for when direct effects, which typically can only be learned from absolute abundances, are recoverable for relative data. As a proof of concept, we recapitulate a previously proposed interaction network for C. difficile colonization.