Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity
Name
McBrideACS2021.pdf
Description
Accepted version
Size
2.66 MB
Format
Adobe PDF
Checksum (MD5)
62f291e9f5ad501e0dc1c3844f1b34f6
Author(s)
McBride, Cameron D
Del Vecchio, Domitilla
Date Issued
November 15, 2021
Journal
ACS Synthetic Biology
Publisher
American Chemical Society (ACS)
Citation
McBride, Cameron D and Del Vecchio, Domitilla. 2021. "Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity." ACS Synthetic Biology.
Version
Author's final manuscript
Abstract
The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multimodule circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1021/acssynbio.1c00281
