TY - JOUR
T1 - Automated design of thousands of nonrepetitive parts for engineering stable genetic systems
AU - Hossain, Ayaan
AU - Lopez, Eriberto
AU - Halper, Sean M.
AU - Cetnar, Daniel P.
AU - Reis, Alexander C.
AU - Strickland, Devin
AU - Klavins, Eric
AU - Salis, Howard M.
N1 - Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.
PY - 2020/12
Y1 - 2020/12
N2 - Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters’ transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.
AB - Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters’ transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.
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U2 - 10.1038/s41587-020-0584-2
DO - 10.1038/s41587-020-0584-2
M3 - Article
C2 - 32661437
AN - SCOPUS:85087840664
SN - 1087-0156
VL - 38
SP - 1466
EP - 1475
JO - Nature Biotechnology
JF - Nature Biotechnology
IS - 12
ER -