Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics.
Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics.
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DOI：
10.7554/elife.73870
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发表时间：
2022-06-23
期刊：
影响因子：
7.7
通讯作者：
Venturelli, Ophelia S.
中科院分区：
文献类型：
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作者：
Baranwal, Mayank;Clark, Ryan L.;Thompson, Jaron;Sun, Zeyu;Hero, Alfred O.;Venturelli, Ophelia S.
关键词：
预测自下而上构建的微生物组的动态和功能是利用它们为我们造福的关键挑战。目前基于生态学理论的模型无法捕捉复杂的社区行为，由于高阶的相互作用，不扩展以及随着复杂性的增加，并考虑到多个功能。我们开发并应用了一个长短期记忆（LSTM）框架，以促进我们对使用合成人类肠道社区的社区组装和健康相关代谢产物的理解。作为递归神经网络的支柱，LSTM学习高维数据驱动的非线性动力系统模型。我们证明了LSTM模型可以优于基于生态理论的广泛使用的广义Lotka-Volterra模型。我们建立了从黑盒模型中破译微生物-微生物和微生物-代谢物相互作用的方法。这些方法强调，放线菌，厚壁菌门和变形菌是代谢产物产生的重要驱动因素，而拟杆菌塑造社区动态。我们使用LSTM模型来导航一个大型多维功能景观，以设计具有独特的健康相关代谢物谱和时间行为的社区。总之，LSTM模型的准确性可以用于实验规划，并指导具有目标动态功能的合成微生物组的设计。
Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.