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Genetic circuit design automation for yeast.

Ye Chen¹, Shuyi Zhang¹, Eric M Young¹

¹Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature Microbiology

|August 5, 2020

Summary

Researchers developed novel genetic logic gates for yeast, enabling predictable cellular control. These engineered biological circuits offer a new platform for synthetic biology applications in bioproduction and therapeutics.

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Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

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Area of Science:

Synthetic Biology
Molecular Biology
Biotechnology

Background:

Genetic circuits allow cells to sense and respond to their environment.
Designing predictable genetic gates is challenging in eukaryotes due to complex transcription and translation.
Existing methods lack modularity and insulation for complex eukaryotic genetic circuit construction.

Purpose of the Study:

To engineer novel, modular, and insulated genetic logic gates for yeast (Saccharomyces cerevisiae).
To establish a design automation framework for eukaryotic genetic circuits.
To enable predictable construction of complex regulatory networks in engineered cells.

Main Methods:

Developed minimal constitutive promoters and operator insertion rules for DNA-binding proteins.
Utilized RNA polymerase flux as the signal carrier between gates.
Implemented ribozymes downstream of terminators for transcriptional insulation.
Employed Cello 2.0 design automation software for circuit construction.

Main Results:

Constructed nine NOT/NOR gates with highly similar response functions and a 400-fold dynamic range.
Achieved transcriptional insulation between gates, preventing RNA polymerase readthrough.
Successfully built genetic circuits with up to 11 regulatory proteins using the developed gates.
Validated a dynamic model for predicting circuit behavior over extended periods.

Conclusions:

The developed gates provide a robust and predictable platform for eukaryotic genetic circuit design.
This work simplifies the construction of complex regulatory networks for cellular engineering.
The approach facilitates applications in bioproduction, environmental monitoring, and living therapeutics.