Department of Biomedical Engineering, Duke University, Durham, NC 27710, USA.
Exp Biol Med (Maywood). 2019 Apr;244(6):445-458. doi: 10.1177/1535370219836771. Epub 2019 Mar 16.
This review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome's role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.
本文综述了用于人类肠道微生物组网络分析的实验和统计工具。了解微生物相互作用的系统动态可能有助于改进管理与微生物组相关疾病的治疗方法。微生物组网络推断工具已被开发并应用于横断面和纵向实验设计,以及多组学数据集,其目的是理清微生物-宿主、微生物-环境和代谢介导的微生物相互作用的复杂网络。这些相互作用网络的特征描述可能有助于更好地理解人类肠道微生物组的系统动态,增加我们对微生物组在人类健康中的作用的认识,并指导优化有效、精确和合理的治疗策略,以管理与微生物组相关的疾病。