History-Driven Genetic Modification Design Technique Using a Domain-Specific Lexical Model for the Acceleration of DBTL Cycles for Microbial Cell Factories

The development of microbes for conducting bioprocessing via synthetic biology involves design–build–test–learn
(DBTL) cycles. To aid the designing step, we developed a computational
technique that suggests next genetic modifications on the basis of
relatedness to the user’s design history of genetic modifications
accumulated through former DBTL cycles conducted by the user. This
technique, which comprehensively retrieves well-known designs related
to the history, involves searching text for previous literature and
then mining genes that frequently co-occur in the literature with
those modified genes. We further developed a domain-specific lexical
model that weights literature that is more related to the domain of
metabolic engineering to emphasize genes modified for bioprocessing.
Our technique made a suggestion by using a history of creating a Corynebacterium glutamicum strain producing shikimic acid
that had 18 genetic modifications. Inspired by the suggestion, eight
genes were considered by biologists for further modification, and
modifying four of these genes proved experimentally efficient in increasing
the production of shikimic acid. These results indicated that our
proposed technique successfully utilized the former cycles to suggest
relevant designs that biologists considered worth testing. Comprehensive
retrieval of well-tested designs will help less-experienced researchers
overcome the entry barrier as well as inspire experienced researchers
to formulate design concepts that have been overlooked or suspended.
This technique will aid DBTL cycles by feeding histories back to the
next genetic design, thereby complementing the designing step.