作者: Kenneth S. Bruno , Jed Dean , Kasia Gora , Michael Flashman , Erin Shellman
DOI:
关键词: Computational biology 、 Computer science 、 Microbial host 、 Strain (biology) 、 Host (network) 、 Automation 、 Genomic engineering 、 Pattern recognition (psychology) 、 Genetic design
摘要: The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, advanced machine learning protocols. This integrative utilizes suite of tool sets to create genetic design libraries, which are derived from, inter alia, scientific insight iterative pattern recognition. described herein strain host agnostic therefore can be implemented across taxa. Furthermore, the disclosed modulate or improve any parameter interest.