RT Journal Article
SR Electronic
T1 Computational Prediction of Synthetic Circuit Function Across Growth Conditions
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.06.13.495701
DO 10.1101/2022.06.13.495701
A1 Breschine Cummins
A1 Robert C. Moseley
A1 Anastasia Deckard
A1 Mark Weston
A1 George Zheng
A1 Daniel Bryce
A1 Joshua Nowak
A1 Marcio Gameiro
A1 Tomas Gedeon
A1 Konstantin Mischaikow
A1 Jacob Beal
A1 Tessa Johnson
A1 Matthew Vaughn
A1 Niall I. Gaffney
A1 Shweta Gopaulakrishnan
A1 Joshua Urrutia
A1 Robert P. Goldman
A1 Bryan Bartley
A1 Tramy T. Nguyen
A1 Nicholas Roehner
A1 Tom Mitchell
A1 Justin D. Vrana
A1 Katie J. Clowers
A1 Narendra Maheshri
A1 Diveena Becker
A1 Ekaterina Mikhalev
A1 Vanessa Biggers
A1 Trissha R. Higa
A1 Lorraine A. Mosqueda
A1 Steven B. Haase
YR 2022
UL http://biorxiv.org/content/early/2022/06/13/2022.06.13.495701.abstract
AB A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.Competing Interest StatementSome of the authors are employed by companies that may benefit or be perceived to benefit from this publication.