1.论文题目和关键词
Title: Mapping the ecological networks of microbial communities
绘制微生物群落的生态网络图
no keyword…
2.摘要大意
Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka–Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.
绘制微生物群落的生态网络图是了解其组配规则（assembly rules）和预测时间行为（temporal behavior）的必要步骤。然而，现有方法需要假设一个先验的种群动力学模型。此外，这些方法需要拟合纵向丰度数据（longitudinal abundance data），这些数据往往信息量不足，无法进行可靠的推断。为了克服这种局限性，我们开发了一种基于稳态丰度数据（steady-state abundance data）的新方法。我们的方法不需要假设任何特定的种群动力学模型，就可以推断出网络拓扑结构和类群间的相互作用类型。此外，当假设种群动态遵循经典的广义Lotka-Volterra模型时，我们的方法可以推断出类群间的相互作用强度和内在增长率。我们使用仿真数据系统地验证了我们的方法，然后将其应用于四个实验数据集。我们的方法代表了对复杂的、真实世界的微生物群落（例如人类肠道微生物群）进行可靠建模的关键一步。
3.从不同角度分析该篇论文的创新点，并谈谈有什么学术价值
创新点：
（1）该论文提出了一种基于稳态数据的推理方法来绘制微生物群落的生态网络图。该方法可以定性地推断生态交互作用类型（正、负和中性相互作用）和生态网络的结构，而不需要任何种群动力学模型。该方法代表了对复杂微生物群落（如人类肠道微生物群落）进行可靠生态建模的关键步骤。
（2）该论文证明了当微生物群落遵循GLV（generalized Lotka–Volterra ）动力学模型，那么稳态数据可以精准推断模型参数，定量推断类群间相互作用强度和内在增长率。
（3）该方法与以往的基于稳态数据的网络重构方法不同的是，该方法不需要对系统施加任何扰动，也不需要足够接近稳态。
4.对该篇论文结论的理解及对学习工作的启发
We expect that additional insights into microbial ecosystems will emerge from a comprehensive understanding of their ecological networks. Indeed, inferring ecological networks using the method developed here will enable enhanced investigation of the stability and assembly rules of microbial communities as well as facilitate the design of personalized microbe-based cocktails to treat diseases related to microbial dysbiosis.
我们希望通过对生态网络的全面了解，可以对微生物生态系统有其他的了解。事实上，使用本文开发的方法推断生态网络将有助于加强对微生物群落稳定性和组配规则（assembly rules）的研究，并有助于设计个性化的基于微生物的混合物来治疗与微生态失调有关的疾病。
不足：
Despite the encouraging facts, we emphasize that there are still many challenges in applying our method to infer the ecological network of the human gut microbiota. For example, the assumption of invariant ecological interaction types (i.e., promotion, inhibition, or neutral) between any two taxa needs to be carefully verified. Moreover, our method requires the measurement of absolute abundances of taxa.
尽管有令人鼓舞的事实，但我们强调，应用我们的方法来推断人类肠道微生物群的生态网络仍然存在许多挑战。 例如，需要仔细验证任何两个分类群之间不变的生态相互作用类型（即促进，抑制或中性）的假设。 此外，我们的方法需要测量分类单元的绝对丰度。