The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Apr. 06, 2021

Filed:

Oct. 15, 2020
Applicant:

Zymergen Inc., Emeryville, CA (US);

Inventors:

Zach Serber, Sausalito, CA (US);

Erik Jedediah Dean, Lafayette, CA (US);

Shawn Manchester, Oakland, CA (US);

Katherine Gora, Oakland, CA (US);

Michael Flashman, Eureka, CA (US);

Erin Shellman, Seattle, WA (US);

Aaron Kimball, San Francisco, CA (US);

Shawn Szyjka, Martinez, CA (US);

Barbara Frewen, Alameda, CA (US);

Thomas Treynor, Berkeley, CA (US);

Kenneth S. Bruno, Walnut Creek, CA (US);

Assignee:

Zymergen Inc., Emeryville, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
C12N 15/10 (2006.01); C12N 15/80 (2006.01); G16B 20/00 (2019.01); G16B 5/00 (2019.01); B01L 3/02 (2006.01); B01L 3/00 (2006.01); G01N 35/10 (2006.01); G01N 35/00 (2006.01); C12N 15/77 (2006.01); C12N 15/00 (2006.01); G16B 40/00 (2019.01);
U.S. Cl.
CPC ...
C12N 15/1058 (2013.01); B01L 3/0275 (2013.01); B01L 3/5085 (2013.01); C12N 15/00 (2013.01); C12N 15/1075 (2013.01); C12N 15/1079 (2013.01); C12N 15/77 (2013.01); C12N 15/80 (2013.01); G01N 35/00871 (2013.01); G01N 35/10 (2013.01); G16B 5/00 (2019.02); G16B 20/00 (2019.02); G16B 40/00 (2019.02); B01L 2200/025 (2013.01); B01L 2200/0689 (2013.01); B01L 2200/16 (2013.01); B01L 2300/0627 (2013.01); B01L 2300/0672 (2013.01); B01L 2300/0681 (2013.01); B01L 2300/18 (2013.01); B01L 2300/1894 (2013.01);
Abstract

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.


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