Zymergen Inc.

Katherine Gora

Shawn Manchester

Kenneth S Bruno

Shawn Szyjka

Erik Jedediah Dean

Michael Flashman

Aaron Kimball

Barbara Frewen

Thomas Treynor

Erin Shellman

Zach Serber

Zachariah Serber

Patrick J Westfall

Kyle Rothschild-Mancinelli

Youngnyun Kim

Erik Jedehiah Dean

Michael Flaschman

Matthew Davis

Prabha Ramakrishnan

Christy Wisnewski

Robert Haushalter

Stacy-Anne Morgan

Michael Blaisse

The present disclosure provides a HTP microbial genomic engineering platform that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols. This integrative platform utilizes a suite of HTP molecular tool sets to create HTP genetic design libraries, which are derived from, inter alia, scientific insight and iterative pattern recognition. The HTP genomic engineering platform described herein is microbial strain host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any microbial host parameter of interest.

HTP genomic engineering platform

The present disclosure provides systems and methods for host cell improvement utilizing epistatic effects. The systems and methods described herein are host cell agnostic and therefore can be implemented across taxa. Furthermore, the disclosed systems and methods can be implemented to modulate or improve any host cell parameter of interest.

Systems and methods for host cell improvement utilizing epistatic effects

Provided are native promoters comprising polynucleotides isolated from, and mutant promoters derived therefrom, which may be used to regulate, i.e., either increase or decrease, gene expression. Also provided are promoter ladders comprising a plurality of the promoters having incrementally increasing promoter activity. Also provided are host cells and recombinant vectors comprising the promoters, and methods of expressing genes of interest and producing biomolecules using the host cells.

Promoters from

Automated system for HTP genomic engineering

The present disclosure provides machine learning techniques for computationally predicting the phenotypic performance of combinations of genetic variations and for designing new improved host cells. The machine learning models and methods described herein are host agnostic and therefore can be implemented across taxa. Furthermore, the disclosed platform can be implemented to modulate or improve any host cell parameter of interest.

Microbial strain improvement by a HTP genomic engineering platform

Growing community of inventors

Oakland, CA, United States of America

Katherine Gora