Isocost Lines Describe the Cellular Economy of Genetic Circuits

Genetic circuits in living cells share transcriptional and translational resources that are available in limited amounts. This leads to unexpected couplings among seemingly unconnected modules, which result in poorly predictable circuit behavior. In this study, we determine these interdependencies b...

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Bibliographic Details
Main Authors: Yazbek, John, Huang, Hsin-Ho, Chung, Hattie, Weiss, Ron, Del Vecchio, Domitilla, Gyoergy, Andras, Jimenez Zarco, Jose I., Del Vecchio, Domitilla
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering
Format: Article
Language:en_US
Published: Elsevier 2015
Online Access:http://hdl.handle.net/1721.1/99533
https://orcid.org/0000-0003-0396-2443
https://orcid.org/0000-0003-2435-480X
https://orcid.org/0000-0001-6472-8576
https://orcid.org/0000-0002-4784-3772
Description
Summary:Genetic circuits in living cells share transcriptional and translational resources that are available in limited amounts. This leads to unexpected couplings among seemingly unconnected modules, which result in poorly predictable circuit behavior. In this study, we determine these interdependencies between products of different genes by characterizing the economy of how transcriptional and translational resources are allocated to the production of proteins in genetic circuits. We discover that, when expressed from the same plasmid, the combinations of attainable protein concentrations are constrained by a linear relationship, which can be interpreted as an isocost line, a concept used in microeconomics. We created a library of circuits with two reporter genes, one constitutive and the other inducible in the same plasmid, without a regulatory path between them. In agreement with the model predictions, experiments reveal that the isocost line rotates when changing the ribosome binding site strength of the inducible gene and shifts when modifying the plasmid copy number. These results demonstrate that isocost lines can be employed to predict how genetic circuits become coupled when sharing resources and provide design guidelines for minimizing the effects of such couplings.