Now showing items 1-2 of 2

    • Chemoinformatic-guided engineering of polyketide synthases 

      Zargar, A.; Lal, R.; Valencia, L.; Wang, J.; Backman, T.; Cruz-Morales, P.; Kothari, A.; Werts, M.; Wong, A.; Bailey, C.; Loubat, A.; Liu, Y.; Benites, V.; Chang, S.; Hernández, A.; Barajas, J.; Thompson, M.; Barcelos, C.; Anayah, R.; Garcia Martin, H.; Mukhopadhyay, A.; Baidoo, E.; Katz, L.; Keasling, J. (2019)
      Polyketide synthase (PKS) engineering is an attractive method to generate new molecules such as commodity, fine and specialty chemicals. A central challenge in PKS design is replacing a partially reductive module with a ...
    • Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning 

      Opgenorth, P.; Costello, Z.; Okada, T.; Goyal, G.; Chen, Y.; Gin, J.; Benites, V.; de Raad, M.; Northen, T.R.; Deng, K.; Deutsch, S.; Baidoo, E.E.K.; Petzold, C.J.; Hillson, N.J.; Garcia-Martin, H.; Beller, H.R. (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 ...