Htp genomic engineering platform

Patent metadata

Field	Value
Publication number	US-2021261950-A1
Application number	US-202117314215-A
Country	US
Kind code	A1
Filing date	May 7, 2021
Priority date	Dec 7, 2015
Publication date	Aug 26, 2021
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A method, comprising:
(a) generating, by a processor, a promoter swap host cell library defining a first plurality of engineered host cells, each engineered host cell from the first plurality of engineered host cells having a genetic variation such that the plurality of engineered host cells have a plurality of genetic variations, each genetic variation from the plurality of genetic variations having one or more promoters from a promoter ladder operably linked to a target gene present in a base host cell from a plurality of base host cells, the promoter ladder including a plurality of promoters exhibiting different expression profiles in the plurality of base host cells; (b) determining, by the processor and based on screening and selecting engineered host cells from the first plurality of engineered host cells based on a phenotypic performance metric, a set of genetic variations (1) from the plurality of genetic variations and (2) that confer a greater degree of a desired phenotype associated with the phenotypic performance metric than the remaining genetic variations from the plurality of genetic variations; and (c) generating, by the processor, a subsequent promoter swap host cell library to define a second plurality of engineered host cells that each have a combination of genetic variations selected from the set of genetic variations and present in at least two engineered host cells from the first plurality of engineered host cells.

2 . The method of claim 1 , further comprising:
sending, by the processor, instructions to automated liquid and particle handling robotics to cause the automated liquid and particle handling robotics to manipulate liquid or particles added to or removed from cultures having the plurality of base host cells to create the second plurality of engineered host cells.

3 . The method of claim 1 , further comprising:
identifying, by the processor and prior to generating the promoter swap host cell library and based on expression profiles across multiple genomic loci, the plurality of promoters for forming the promoter ladder in which the plurality of promoters is ranked based on a strength of each promoter from the plurality of promoters.

4 . The method of claim 1 , further comprising:
repeating steps (b) and (c) until determining that an engineered host cell defined by a subsequent promoter swap host cell library has acquired a degree of the desired phenotype that is greater than a predetermined threshold.

5 . The method of claim 1 , wherein the promoter ladder includes one or more heterologous promoters or constitutive promoters.

6 . The method of claim 1 , wherein the second plurality of engineered host cells includes at least one engineered host cell with at least a 10% increase in the degree of the desired phenotype compared to that an engineered host cell from the first plurality of engineered host cells.

7 . The method of claim 1 , wherein the second plurality of engineered host cells includes at least one engineered host cell with a one-fold level increase in the phenotypic performance metric compared to that of an engineered host cell from the first plurality of engineered host cells.

8 . The method of claim 1 , wherein the first plurality of engineered host cells includes at least one engineered host cell with at least a 10% increase in the degree of the desired phenotype compared to that of a base host cell from the plurality of base host cells.

9 . The method of claim 1 , further comprising identifying genomes of the plurality of base host cells from a metabolic pathway associated with production of a product of interest associated with the phenotypic performance metric,
the promoter swap host cell library being generated based on the genomes of the plurality of base host cells.

10 . The method of claim 9 , wherein the product of interest is selected from the group consisting of: a small molecule, enzyme, protein, peptide, amino acid, organic acid, synthetic compound, fuel, alcohol, primary extracellular metabolite, secondary extracellular metabolite, intracellular component molecule, and combinations thereof.

11 . The method of claim 1 , wherein the phenotypic performance metric includes at least one of: increased volumetric productivity of a product of interest, increased specific productivity of a product of interest, increased yield of a product of interest, increased titer of a product of interest, or a combination thereof.

12 . The method of claim 1 , wherein determining the set of genetic variations that confer the greater degree of the desired phenotype includes utilizing a machine learning model.

13 . The method of claim 12 , wherein the machine learning model includes at least one of: linear regression, kernel ridge regression, logistic regression, neural networks, support vector machines (SVMs), decision trees, hidden Markov models, Bayesian networks, a Gram-Schmidt process, reinforcement-based learning, cluster-based learning, hierarchical clustering, genetic algorithms, or combinations thereof.

14 . The method of claim 1 , wherein generating the promotor swap host cell library includes using a machine learning model trained to predict an expected phenotypic performance.

15 . The method of claim 14 , wherein the machine learning model includes at least one of: linear regression, kernel ridge regression, logistic regression, neural networks, support vector machines (SVMs), decision trees, hidden Markov models, Bayesian networks, a Gram-Schmidt process, reinforcement-based learning, cluster-based learning, hierarchical clustering, genetic algorithms, or combinations thereof.

16 . The method of claim 1 , wherein each of the first plurality of engineered host cells and the second plurality of engineered host cells includes thousands of engineered host cells each engineered to have a genetic variation or combination of genetic variations from the plurality of genetic variations.

17 . A method, comprising:
(a) generating, by a processor, a promoter swap host cell library having a plurality of engineered host cells, each engineered host cell from the plurality of engineered host cells having a promoter-gene combination such that the plurality of engineered host cells having a plurality of promoter-gene combinations, each promoter-gene combination from the plurality of promoter-gene combinations having one or more promoters from a promoter ladder operably linked to a target gene present in a base host cell from a plurality of base host cells, the promoter ladder including a plurality of promoters exhibiting different expression profiles in the plurality of base host cells; (b) sending, by the processor, instructions to automated liquid and particle handling robotics to cause the automated liquid and particle handling robotics to manipulate liquid or particles added to or removed from cultures having the plurality of base host cells to create the plurality of engineered host cells; (c) determining, by the processor and based on screening and selecting engineered host cells from the plurality of engineered host cells based on a phenotypic performance metric, a set of promoter-gene combinations (1) from the plurality of promoter-gene combinations and (2) that confer a greater degree of a desired phenotype associated with the phenotypic performance metric than the remaining promoter-gene combinations from the plurality of promoter-gene combinations; and (d) generating, by the processor, an output identifying the set of promoter-gene combinations.

18 . The method o

Assignees

• Zymergen Inc

Inventors

• Serber Zachariah
• Dean Erik Jedediah
• Manchester Shawn
• Gora Katherine
• Flashman Michael
• Shellman Erin
• Kimball Aaron
• Szyjka Shawn
• Frewen Barbara
• Treynor Thomas
• Bruno Kenneth S

Classifications

• C12N15/1079

  Screening libraries by altering the phenotype or phenotypic trait of the host (reporter assays C12N15/1086) · CPC title

• G16B40/20

  Supervised data analysis · CPC title

• G01N2035/1032

  Dilution or aliquotting · CPC title

• G16B35/20

  Screening of libraries · CPC title

• B01L2200/0689

  Sealing · CPC title