Estrela Sylvie, Sánchez Álvaro, Rebolleda-Gómez María
Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, United States.
Front Microbiol. 2021 Apr 9;12:657467. doi: 10.3389/fmicb.2021.657467. eCollection 2021.
Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work.
机器人技术和经济实惠的基因组测序技术的最新进展使得在高通量条件下建立并定量追踪富集群落的组装成为可能。通过在多达数千个合成生境中进行群落组装实验，其中重复样本间的外在变异来源可以得到控制，我们现在能够研究微生物群落组装在不同组织水平上的可重复性和可预测性，以及它与营养成分和其他生态驱动因素之间的关系。通过与数学模型的对话，高通量富集群落正使我们更接近建立微生物群落组装定量预测理论的目标。在这篇简短的综述中，我们概述了这个不断发展的领域的最新研究，强调了理论与实验之间的联系，并为未来的工作提出了方向。
