Investigating and Modeling the Factors That Affect Genetic Circuit Performance

Shai Zilberzwige-Tal, Pedro Fontanarrosa, Darya Bychenko, Yuval Dorfan, Ehud Gazit, Chris J. Myers

Research output: Contribution to journalArticlepeer-review

Abstract

Over the past 2 decades, synthetic biology has yielded ever more complex genetic circuits that are able to perform sophisticated functions in response to specific signals. Yet, genetic circuits are not immediately transferable to an outside-the-lab setting where their performance is highly compromised. We propose introducing a broader test step to the design-build-test-learn workflow to include factors that might contribute to unexpected genetic circuit performance. As a proof of concept, we have designed and evaluated a genetic circuit in various temperatures, inducer concentrations, nonsterilized soil exposure, and bacterial growth stages. We determined that the circuit’s performance is dramatically altered when these factors differ from the optimal lab conditions. We observed significant changes in the time for signal detection as well as signal intensity when the genetic circuit was tested under nonoptimal lab conditions. As a learning effort, we then proceeded to generate model predictions in untested conditions, which is currently lacking in synthetic biology application design. Furthermore, broader test and learn steps uncovered a negative correlation between the time it takes for a gate to turn ON and the bacterial growth phases. As the synthetic biology discipline transitions from proof-of-concept genetic programs to appropriate and safe application implementations, more emphasis on test and learn steps (i.e., characterizing parts and circuits for a broad range of conditions) will provide missing insights on genetic circuit behavior outside the lab.

Original languageEnglish
Pages (from-to)3189-3204
Number of pages16
JournalACS Synthetic Biology
Volume12
Issue number11
DOIs
StatePublished - 17 Nov 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.

Funding

P.F. and C.M. were supported by DARPA FA8750-17-C-0229 and by the Army Research Office under Cooperative Agreement Number W911NF-22-2-0210. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation herein.

FundersFunder number
Army Research OfficeW911NF-22-2-0210
Defense Advanced Research Projects AgencyFA8750-17-C-0229

    Keywords

    • DBTL
    • genetic circuit
    • model predictions
    • outside-the-lab
    • redesign
    • robustness

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