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Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning
(2019-01-01)
The Design–Build–Test–Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient ...
Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models
Zhang, J.; Petersen, S.; Radivojevic, T.; Ramirez, A.; Pérez, A.
; Abeliuk, E.; Sánchez, B.; Costello, Z.; Chen, Y.; Fero, M.; Garcia Martin, H.; Nielsen, J.; Keasling, J.; Jensen, M. (2019)
; Abeliuk, E.; Sánchez, B.; Costello, Z.; Chen, Y.; Fero, M.; Garcia Martin, H.; Nielsen, J.; Keasling, J.; Jensen, M. (2019)
In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show ...