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. I report the design and validation of eVOLVER, a scalable DIY framework that can be configured to carry out high- throughput growth experiments in molecular evolution, systems biology, and microbiology. I perform high-throughput evolution of yeast across systematically varied population density niches to show how eVOLVER can precisely characterize adaptive niches. I describe growth selection using time-varying temperature programs on a genome-wide yeast knockout library to identify strains with altered sensitivity to changes in temperature magnitude or frequency. Inspired by large-scale integration of electronics and microfluidics, I also demonstrate millifluidic multiplexing modules that enable multiplexed media routing, cleaning, vial-to-vial transfers and automated yeast mating.
Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/30737 |
Date | 03 July 2018 |
Creators | Wong, Brandon Gei-Chin |
Contributors | Khalil, Ahmad S. |
Source Sets | Boston University |
Language | en_US |
Detected Language | English |
Type | Thesis/Dissertation |
Rights | Attribution-ShareAlike 4.0 International, http://creativecommons.org/licenses/by-sa/4.0/ |
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