Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA.
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA.
Anal Bioanal Chem. 2021 Apr;413(9):2331-2344. doi: 10.1007/s00216-020-03085-7. Epub 2020 Nov 26.
Aquatic microbial communities contribute fundamentally to biogeochemical transformations in natural ecosystems, and disruption of these communities can lead to ecological disasters such as harmful algal blooms. Microbial communities are highly dynamic, and their composition and function are tightly controlled by the biophysical (e.g., light, fluid flow, and temperature) and biochemical (e.g., chemical gradients and cell concentration) parameters of the surrounding environment. Due to the large number of environmental factors involved, a systematic understanding of the microbial community-environment interactions is lacking. In this article, we show that microfluidic platforms present a unique opportunity to recreate well-defined environmental factors in a laboratory setting in a high throughput way, enabling quantitative studies of microbial communities that are amenable to theoretical modeling. The focus of this article is on aquatic microbial communities, but the microfluidic and mathematical models discussed here can be readily applied to investigate other microbiomes.
水生微生物群落从根本上促进了自然生态系统中的生物地球化学转化，而这些群落的破坏可能导致生态灾难，如有害藻类水华。微生物群落具有高度动态性，其组成和功能受到周围环境的生物物理（如光照、流体流动和温度）和生化（如化学梯度和细胞浓度）参数的严格控制。由于涉及到大量的环境因素，因此对微生物群落与环境相互作用的系统理解还很缺乏。在本文中，我们表明，微流控平台提供了一个独特的机会，可以在实验室环境中以高通量的方式重现定义明确的环境因素，从而能够对微生物群落进行定量研究，这些研究适合理论建模。本文的重点是水生微生物群落，但这里讨论的微流控和数学模型可以很容易地应用于研究其他微生物组。
