Microbial strain improvement by a HTP genomic engineering platform

Patent metadata

Field	Value
Publication number	US-10808243-B2
Application number	US-202016838409-A
Country	US
Kind code	B2
Filing date	Apr 2, 2020
Priority date	Dec 7, 2015
Publication date	Oct 20, 2020
Grant date	Oct 20, 2020

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 computer-implemented method for engineering a candidate host cell to have a beneficial combination of genetic alterations; said method comprising the steps of:
a) populating a predictive machine learning model with a training data set, containing: i) a plurality of genetic alteration input variables, representing a plurality of genetic alterations that have been introduced into a host cell, and ii) a plurality of measured phenotypic performance output variables, representing phenotypic performance measurements associated with the plurality of introduced genetic alterations;
b) generating, in silico, a pool of design candidate host cells incorporating the plurality of genetic alterations; and
c) utilizing the predictive machine learning model to predict the expected phenotypic performance of members of the pool of design candidate host cells that comprise a combination of genetic alterations selected from step (a) that are uncharacterized for improving phenotypic performance at the time of carrying out step (c);

wherein the predicted expected phenotypic performance is production of a product of interest, said product of interest 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.

2. The method of claim 1 , wherein the predictive machine learning model incorporates at least one of the following: 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.

3. The method of claim 1 , wherein the predictive machine learning model incorporates epistatic effects.

4. The method of claim 1 , wherein the predictive machine learning model is supervised, semi-supervised, or unsupervised.

5. The method of claim 1 , wherein the plurality of genetic alterations comprise a genetic alteration selected from the group consisting of: a single nucleotide polymorphism, nucleotide sequence insertion, nucleotide sequence deletion, and nucleotide sequence replacements.

6. The method of claim 1 , wherein the plurality of genetic alterations comprise a genetic alteration comprising one or more heterologous promoters from a promoter ladder operably linked to an endogenous target gene.

7. The method of claim 1 , comprising step d) manufacturing a member of the pool of design candidate host cells from a previous step to create an engineered host cell.

8. The method of claim 7 , comprising step e) measuring, in an in vitro assay, phenotypic performance of the engineered host cell from the previous step; and f) adding to the training data set of (a)
i. one or more genetic alteration input variables representing one or more genetic alterations that were introduced into the engineered host cell that was measured in the previous step, and
ii. one or more measured phenotypic performance output variables representing the phenotypic performance measurements of the engineered host cell that was measured in the previous step.

9. The method of claim 8 , wherein steps (a)-(f) are repeated until an engineered host cell exhibits a desired level of improved phenotypic performance, provided that on the last repetition, step (f) need not be performed.

10. A computer-implemented method for engineering a candidate host cell to have a beneficial combination of genetic alterations, said method comprising the steps of:
a) populating a predictive machine learning model with a training data set, containing: i) a plurality of genetic alteration input variables representing a plurality of genetic alterations that have been introduced into a host cell, and ii) a plurality of measured phenotypic performance output variables representing phenotypic performance measurements associated with the plurality of introduced genetic alterations;
b) generating, in silico, a pool of design candidate host cells incorporating the plurality of genetic alterations; and
c) utilizing the predictive machine learning model to predict the expected phenotypic performance of members of the pool of design candidate host cells that comprise a combination of genetic alterations selected from step (a) that are uncharacterized for improving phenotypic performance at the time of carrying out step (c);

wherein the predictive machine learning model incorporates at least one of the following: 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.

11. The method of claim 10 , wherein the predictive machine learning model incorporates epistatic effects.

12. The method of claim 10 , wherein the predictive machine learning model is supervised, semi-supervised, or unsupervised.

13. The method of claim 10 , wherein the plurality of genetic alterations comprise a genetic alteration selected from the group consisting of: a single nucleotide polymorphism, nucleotide sequence insertion, nucleotide sequence deletion, and nucleotide sequence replacements.

14. The method of claim 10 , wherein the plurality of genetic alterations comprise a genetic alteration comprising one or more heterologous promoters from a promoter ladder operably linked to an endogenous target gene.

15. The method of claim 10 , comprising step d) manufacturing a member of the pool of design candidate host cells from a previous step to create an engineered host cell.

16. The method of claim 15 , comprising step e) measuring, in an in vitro assay, phenotypic performance of the engineered host cell from the previous step; and f) adding to the training data set of (a)
i. one or more genetic alteration input variables representing one or more genetic alterations that were introduced into the engineered host cell that was measured in the previous step, and
ii. one or more measured phenotypic performance output variables representing the phenotypic performance measurements of the engineered host cell that was measured in the previous step.

17. The method of claim 16 , wherein steps (a)-(f) are repeated until an engineered host cell exhibits a desired level of improved phenotypic performance, provided that on the last repetition, step (f) need not be performed.

18. A computer-implemented method for engineering a candidate host cell to have a beneficial combination of genetic alterations, said method comprising the steps of:
a) populating a predictive machine learning model with a training data set, containing: i) a plurality of genetic alteration input variables representing a plurality of genetic alterations that have been introduced into a host cell, and ii) a plurality of measured phenotypic performance output variables representing phenotypic performance measurements associated with the plurality of introduced genetic alterations;
b) generating, in silico, a pool of design candidate host cells incorporating the plurality of genetic alterations; and
c) utilizing the predictive machine learning model to predict the expected phenotypic performance of members of the pool of design candidate host cells that comprise a combinat

Assignees

• Zymergen Inc

Inventors

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

Classifications

• G01N2035/1053

  for separating part of the liquid, e.g. filters, extraction phase · CPC title

• G01N2035/1048

  using the transfer device for another function · CPC title

• G01N2035/1032

  Dilution or aliquotting · CPC title

• G01N2035/1027

  General features of the devices · CPC title

• C12M35/02

  Electrical or electromagnetic means, e.g. for electroporation or for cell fusion · CPC title