Zymergen Inc.

Kenneth S Bruno

Shawn Manchester

Shawn Szyjka

Aaron Kimball

Michael Flashman

Erik Jedediah Dean

Barbara Frewen

Katherine Gora

Erin Shellman

Thomas Treynor

Zach Serber

Zachariah Serber

Edyta Szewczyk

Sean Poust

Sara Da Luz Areosa Cleto

Brian Chaikind

Arthur Muir Fong, Iii

Patrick J Westfall

Hassan Abdulla

Vytas SunSpiral

Jennifer Fredlund

Paolo Boccazzi

Dylan Vaughan

Kyle Rothschild-Mancinelli

Sachin Jain

Brandon Pfannenstiel

Kasia Gora

Erik Jedehiah Dean

Michael Flaschman

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

The present disclosure provides a microbial genomic engineering method and system for transforming, screening, and selecting filamentous fungal cells that have altered morphology and/or growth under specific growth conditions. The method and system utilize high-throughput (HTP) methods to produce filamentous fungal production strains with a desired morphological phenotype.

Manipulation of genes involved in signal transduction to control fungal morphology during fermentation and production

A HTP genomic engineering platform for improving filamentous fungal cells that is computationally driven and integrates molecular biology, automation, and advanced machine learning protocols is provided. 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. Methods for isolating clonal populations derived from individual fungal spores are also provided.

HTP genomic engineering platform for improving fungal strains

HTP genomic engineering platform for improving filamentous fungal cells 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. Methods can be carried out within optofluidic devices.

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

Walnut Creek, CA, United States of America

Kenneth S Bruno