Abstract
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.
Highlights
Designing at the first or earlier rounds of DBTL cycles is welladdressed by computational techniques, including those for metabolic pathways,[5−9] genetic modifications,[10−12] and regulatory elements on gene expression.[13−15] Flux balance analysis,[10−12] for example, can suggest a stoichiometrically optimized set of genetic modifications under arbitrary objective functions based on well-curated models of metabolic networks
The R-precision, a measure for evaluating information retrieval, was 92.8%, indicating that literature in the domain of metabolic engineering ranked well above that in other domains that would generate noise. These results demonstrate the efficiency of using literature scores determined by the domain-specific lexical model in order to evaluate relatedness to the domain of metabolic engineering
The suggestion proposed by our technique in consideration of a design history for shikimic acid-producing C. glutamicum provided eight candidate genes that experts regarded as being worth subsequent analysis
Summary
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 genetic design, thereby complementing the designing step. Storage has been welladdressed by repositories, such as JBEI-ICE,[16] the iGEM
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