Computational Prediction of Synthetic Circuit Function Across Growth Conditions
Cummins, B.; Moseley, R. C.; Deckard, A.; Weston, M.; Zheng, G.; Bryce, D.; Nowak, J.; Gameiro, M.; Gedeon, T.; Mischaikow, K.; Beal, J.; Johnson, T.; Vaughn, M.; Gaffney, N. I.; Gopaulakrishnan, S.; Urrutia, J.; Goldman, R. P.; Bartley, B.; Nguyen, T. T.; Roehner, N.; Mitchell, T.; Vrana, J. D.; Clowers, K. J.; Maheshri, N.; Becker, D.; Mikhalev, E.; Biggers, V.; Higa, T.; Mosqueda, L.; Haase, S. B.
Show abstract
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.
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