Zymergen Inc.

Barbara Frewen

Aaron Kimball

Shawn Manchester

Kenneth S Bruno

Shawn Szyjka

Erik Jedediah Dean

Michael Flashman

Katherine Gora

Thomas Treynor

Erin Shellman

Zach Serber

Zachariah Serber

Patrick J Westfall

Kyle Rothschild-Mancinelli

Tessa Alexanian

Prasad Ganesan

William J Mania

Zachary Palchick

Erin Rhode

Dylan Webster

Youngnyun Kim

Kiyan Ahmadizadeh

Michael Blaisse

Kasia Gora

Erik Jedehiah Dean

Michael Flaschman

Matthew Davis

Prabha Ramakrishnan

Christy Wisnewski

Robert Haushalter

Stacy-Anne Morgan

High-throughput production of modified microbes is achieved through optimization of directed build graph data structures representing biological workflows. Portions of otherwise unrelated workflows may be combined where they share common biological reaction steps, and processed by a genetic manufacturing facility to take advantage of operational efficiencies. Workflows may be mapped to physical laboratory equipment in a manner that optimizes material transfers. Different automated platforms running different machines in different languages are coordinated in a device-agnostic and language-agnostic manner.

Device-agnostic system for planning and executing high-throughput genomic manufacturing operations

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

Automated system for HTP genomic engineering

The generation of a factory order to control production of nucleotide sequences by a gene manufacturing system includes receiving an expression indicating an operation on sequence operands, each representing at least one nucleotide sequence part, evaluating the expression to a sequence specification, wherein the sequence specification comprises a data structure including one or more first-level operations and one or more second-level operations, and generating the factory order based upon execution of the one or more first-level operations and the one or more second-level operations. In a recursive manner, the one or more first-level operations operate on at least one first-level sequence operand, the value of which is resolved by execution of one or more of the second-level operations. The factory order may then be provided to the gene manufacturing system to assemble the sequence parts into nucleotide sequences represented by the sequence specification.

Microbial strain design system and methods for improved large-scale production of engineered nucleotide sequences

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

Alameda, CA, United States of America

Barbara Frewen