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Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER


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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Figure 1: eVOLVER: an integrated framework for high-throughput, automated cell culture.
Figure 2: Design and performance of eVOLVER modules.
Figure 3: High-throughput experimental evolution across a multidimensional selection space.
Figure 4: Genome scale library fitness under temporally varying selection pressure.
Figure 5: Integrated millifluidic devices enable scaling of complex fluidic manipulation.

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We are grateful to B. Stafford for his assistance in design architecture of the system. We thank H. Khalil, A. Soltanianzadeh, A. Sun, S. Pipe, and A. Cavale for help on construction of the system. We are indebted to the Electronics Design Facility (EDF) and Engineering Product Innovation Center (EPIC) at Boston University for their services. We also thank D. Segrè, J. Ngo, J. Tytell, W. Wong, and members of the Khalil lab for insightful comments on the manuscript. This work was supported by a NSF CAREER Award (MCB-1350949 to A.S.K.) and a DARPA grant (HR0011-15-C-0091 to A.S.K.). A.S.K. also acknowledges funding from the NIH Director's New Innovator Award (1DP2AI131083-01), DARPA Young Faculty Award (D16AP00142), and NSF Expeditions in Computing (CCF-1522074).

Author information




B.G.W., C.J.B., and A.S.K. conceived the study. B.G.W. developed the system with guidance and input from all authors. B.G.W. and C.P.M. performed and analyzed experiments. S.K. generated reagents. C.J.B. and A.S.K. oversaw the study. All authors wrote the paper.

Corresponding authors

Correspondence to Caleb J Bashor or Ahmad S Khalil.

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The authors declare no competing financial interests.

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Supplementary Figures 1–37, Supplementary Notes 1–17, Supplementary Tables 1–3 (PDF 13265 kb)

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Wong, B., Mancuso, C., Kiriakov, S. et al. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat Biotechnol 36, 614–623 (2018).

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