吞吐量
酵母
微流控
多路复用
可扩展性
自动化
高通量筛选
酿酒酵母
计算生物学
生物
纳米技术
生物系统
计算机科学
材料科学
生物信息学
遗传学
数据库
工程类
机械工程
电信
无线
作者
Brandon G. Wong,Christopher P. Mancuso,Szilvia Kiriakov,Caleb J. Bashor,Ahmad S. Khalil
摘要
Precise control over microbial cell growth conditions could enable detection of minute phenotypic changes, which would improve our understanding of how genotypes are shaped by adaptive selection. Although automated cell-culture systems such as bioreactors offer strict control over liquid culture conditions, they often do not scale to high-throughput or require cumbersome redesign to alter growth conditions. We report the design and validation of eVOLVER, a scalable do-it-yourself (DIY) framework, which can be configured to carry out high-throughput growth experiments in molecular evolution, systems biology, and microbiology. High-throughput evolution of yeast populations grown at different densities reveals that eVOLVER can be applied to characterize adaptive niches. Growth selection on a genome-wide yeast knockout library, using temperatures varied over different timescales, finds strains sensitive to temperature changes or frequency of temperature change. Inspired by large-scale integration of electronics and microfluidics, we also demonstrate millifluidic multiplexing modules that enable multiplexed media routing, cleaning, vial-to-vial transfers and automated yeast mating.
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