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Model-guided mechanism discovery and parameter selection for directed evolution

  • Sarah C. Stainbrook
  • Keith E. J. TyoEmail author
Methods and protocols

Abstract

Directed evolution is frequently applied to identify genetic variants with improvements in a single or multiple properties. When used to improve multiple properties simultaneously, a common strategy is to apply alternating rounds of selection criteria to enrich for variants with each desirable trait. In particular, counterselection, or selection against undesired traits rather than for desired ones, has been successfully employed in many studies. Although the sequence and stringency of alternating selective pressures for different traits are known to be highly consequential for the outcome of the screen, the effects of these parameters have not been systematically evaluated. We developed a method for producing a statistical modeling framework to elucidate these effects. The model uses single-cell fluorescence intensity distributions to estimate the proportions of phenotypic populations within a library and then predicts the changes in these proportions depending on specified positive selective or counterselective pressures. We validated the approach using recently described systems for metabolite-responsive bacterial transcription factors and yeast G-protein-coupled receptors. Finally, we applied the model to identify biological sources that exert undesirable selective pressure on libraries during sorting. Notably, these pressures produce substantial artifacts that, if unaddressed, can lead to failure of the screen. This method for model generation can be applied to FACS-based directed evolution experiments to create a quantitative framework that identifies subtle population effects. Such models can guide the choice of experimental design parameters to better enrich for true positive genetic variants and improve the chance of successful directed evolution.

Keywords

Counterselection Directed evolution Computational modeling Fluorescence-activated cell sorting 

Notes

Acknowledgments

We are grateful to Joshua Leonard for the gift of the library and to Joshua Leonard, Joseph Muldoon, and Peter Su for helpful discussion during the preparation of this manuscript.

Authors’ contributions

SCS collected and analyzed data and performed simulations; SCS and KT wrote the manuscript.

Funding

This work is funded by the National Science Foundation Grant DGE-1324585, the Bill and Melinda Gates Foundation Grant OPP1061177, and the National Institutes of Health (NIH) Common Fund [5R01MH103910-02]. This work was supported by the Northwestern University – Flow Cytometry Core Facility supported by Cancer Center Support Grant (NCI CA060553). Flow Cytometry Cell Sorting was performed on a BD FACSAria SORP system, purchased through the support of NIH 1S10OD011996-01.

Compliance with ethical standards

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

All data generated or analyzed during this study are included in this published article [and its supplementary information files]. The code used to analyze these datasets and to simulate sorting is available in the Github repository, https://github.com/tyo-nu/ModelingDirectedEvolution.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

253_2019_10179_MOESM1_ESM.pdf (1.9 mb)
ESM 1 (PDF 1931 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Interdisciplinary Biological Sciences ProgramNorthwestern UniversityEvanstonUSA
  2. 2.Chemical and Biological EngineeringNorthwestern UniversityEvanstonUSA

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