Zymergen Inc.

III Holdings 2, LLC

Corrino Holdings LLC

Michael Flashman

Kenneth S Bruno

Shawn Manchester

Shawn Szyjka

Aaron Kimball

Erik Jedediah Dean

Barbara Frewen

Katherine Gora

Thomas Treynor

Erin Shellman

Zach Serber

Zachariah Serber

Peter Rinearson

Patrick J Westfall

Kyle Rothschild-Mancinelli

Kristofor Selden

Youngnyun Kim

Michael Blaisse

Kasia Gora

Matthew Davis

Prabha Ramakrishnan

Christy Wisnewski

Robert Haushalter

Stacy-Anne Morgan

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

User-submitted content (e.g., stories) may be associated with descriptive metadata (intersection metadata), such as a timeframe, location, tags, and so on. The user-submitted content may be browed and/or searched using the descriptive metadata. Intersection criteria comprising a prevailing timeframe, a location, and/or other metadata criteria may be used to identify an intersection space comprising one or more stories. The stories may be ordered according to relative importance, which may be determined (at least in part) by comparing story metadata to the intersection criteria.

Systems and methods for identifying intersections using content metadata

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

Eureka, CA, United States of America

Michael Flashman