Htp genomic engineering platform

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 alga, 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.

What is claimed is: 
     
         1 . A method, comprising:
 (a) generating, by a processor, data of a 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 first plurality of engineered host cells has a plurality of genetic variations;   (b) determining, by the processor and based on screening and selecting engineered host cells from the first plurality of engineered host cells for a phenotypic performance metric, and using a machine learning model, a set of genetic variation combinations, from the plurality of genetic variations, that are predicted to confer a greater degree of a desired phenotype associated with the phenotypic perfoimance metric than other combinations of genetic variations from the plurality of genetic variations; and   (c) generating, by the processor, data of a subsequent host cell library defining a subsequent plurality of engineered host cells that each has a combination of genetic variations selected from the set of genetic variation combinations, wherein each genetic variation in the combination of genetic variations is present in engineered host cells from the first plurality of engineered host cells.   
     
     
         2 . The method of  claim 1 , further comprising:
 (d) 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 a plurality of base host cells to create the subsequent plurality of engineered host cells.   
     
     
         3 . The method of  claim 1 , wherein the first plurality of engineered host cells comprises a genetic variation selected from the group consisting of a promoter swap, a SNP swap, start/stop codon microbial strain library, optimized sequence microbial strain library, a terminator swap microbial strain library, and any combination thereof. 
     
     
         4 . The method of  claim 2 , further comprising:
 repeating steps (b)-(d) one or more times in a linear or non-linear fashion, until determining that an engineered host cell in the subsequent plurality of engineered host cells has acquired a degree of the desired phenotype that is greater than a predetermined threshold; wherein each subsequent iteration of step (b) is based on screening and selecting engineered host cells from the plurality of engineered host cells created in any previous step, and each subsequent iteration of step (c) defines a further subsequent plurality of engineered host cells that each has a combination of genetic variations selected from any of the sets of genetic variation combinations defined in any previous step.   
     
     
         5 . The method of  claim 1 , wherein the subsequent plurality of engineered host cells includes at least one engineered host cell with at least a predicted 10% increase in the degree of the desired phenotype compared to that an engineered host cell from the first plurality of engineered host cells. 
     
     
         6 . The method of  claim 1 , wherein the subsequent plurality of engineered host cells includes at least one engineered host cell with a predicted 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. 
     
     
         7 . 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. 
     
     
         8 . The method of  claim 1 , 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.   
     
     
         9 . The method of  claim 1 , wherein each of the first plurality of engineered host cells and the subsequent 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. 
     
     
         10 . A method, comprising:
 (a) generating, by a processor, data of a host cell library having 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 first plurality of engineered host cells has a plurality of genetic variations;   (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 a plurality of base host cells to create the first plurality of engineered host cells;   (c) determining, by the processor and based on screening and selecting engineered host cells from the first plurality of engineered host cells for a phenotypic performance metric, and using a machine learning model, a set of genetic variation combinations from the plurality of genetic variations, that are predicted to confer a greater degree of a desired phenotype associated. with the phenotypic performance metric than other combinations of genetic variations from the plurality of genetic variations; and   (d) generating, by the processor, an output identifying the set of genetic variation combinations.   
     
     
         11 . The method of  claim 10 , wherein the instructions are first instructions, the method further comprising:
 sending, by the processor, second instructions to a thermal regulator to maintain a temperature of samples containing the first plurality of engineered host cells within a predetermined temperature range.   
     
     
         12 . The method of  claim 10 , further comprising:
 receiving, from a camera vision or spectrometer system, data indicative of color or absorption changes in samples containing the first plurality of engineered host cells,   the set of genetic variation combinations being determined at least in part based on the data received from the camera vision or spectrometer system.   
     
     
         13 . The method of  claim 10 , wherein sending the instructions to the automated liquid and particle handling robotics includes sending the instructions to the automated liquid and particle handling robotics that causes the automated liquid and particle handling robotics to perform liquid and particle manipulations including one or more of: aspiration, dispensing, mixing, diluting, washing, volumetric transfers, retrieving and discarding of pipette tips, or repetitive pipetting of identical volumes. 
     
     
         14 . The method of  claim 10 , wherein sending the instructions to the automated liquid and particle handling robotics includes sending the instructions to cause robotic arms of the automated liquid and particle handling robotics to manipulate liquid or particles added to or removed from the cultures. 
     
     
         15 . The method of  claim 10 , wherein sending the instructions to the automated liquid and particle handling robotics includes sending the instructions to cause a high-throughput transformation system of the automated liquid and particle handling robotics to transform a base host cell from the plurality of base host cells into an engineered host cell from the first plurality of engineered host cells. 
     
     
         16 . A processor-readable non-transitory medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:
 (a) generate data of a 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 first plurality of engineered host cells has a plurality of genetic variations;   (b) determine, based on screening and selecting engineered host cells from the first plurality of engineered host cells for a phenotypic performance metric, and using a machine learning model, a set of genetic variation combinations, from the plurality of genetic variations, that are predicted to confer a greater degree of a desired phenotype associated with the phenotypic performance metric than other combinations of genetic variations from the plurality of genetic variations; and   (c) generate data of a subsequent host cell library defining a subsequent plurality of engineered host cells that each has a combination of genetic variations selected from the set of genetic variation combinations, wherein each genetic variation in the combination of genetic variations is present in engineered host cells from the first plurality of engineered host cells.   
     
     
         17 . The processor-readable non-transitory medium of  claim 16 , wherein the code includes code to cause the processor to:
 (d) send 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 a plurality of base host cells to create the subsequent plurality of engineered host cells.   
     
     
         18 . The processor-readable non-transitory medium of  claim 16 , wherein the first plurality of engineered host cells comprises a genetic variation selected from the group consisting of a promoter swap, a SNP swap, start/stop codon microbial strain library, optimized sequence microbial strain library, a terminator swap microbial strain library, and any combination thereof, 
     
     
         19 . The processor-readable non-transitory medium of  claim 17 , wherein the code includes code to cause the processor to:
 repeat steps (b)-(d) one or more times in a linear or non-linear fashion, until determining that an engineered host cell in the subsequent plurality of engineered host cells has acquired a degree of the desired phenotype that is greater than a predetermined threshold; wherein each subsequent iteration of step (b) is based on screening and selecting engineered host cells from the plurality of engineered host cells created in any previous step, and each subsequent iteration of step (c) defines a further subsequent plurality of engineered host cells that each has a combination of genetic variations selected from any of the sets of genetic variation combinations defined in any previous step.   
     
     
         20 . The processor-readable non-transitory medium of  claim 16 , 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. 
     
     
         21 . A processor-readable non-transitory medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:
 (a) generate data of a host cell library having 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 first plurality of engineered host cells has a plurality of genetic variations;   (b) send 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 a plurality of base host cells to create the first plurality of engineered host cells;   (c) determine, based on screening and selecting engineered host cells from the first plurality of engineered host cells for a phenotypic performance metric, and using a machine learning model, a set of genetic variation combinations from the plurality of genetic variations, that are predicted to confer a greater degree of a desired phenotype associated with the phenotypic performance metric than other combinations of genetic variations from the plurality of genetic variations; and   (d) generate an output identifying the set of genetic variation combinations.   
     
     
         22 . The processor-readable non-transitory medium of  claim 21 , the code further includes code to cause the processor to:
 receive from a camera vision or spectrometer system, data indicative of color or absorption changes in samples containing the first plurality of engineered host cells,   the set of genetic variation combinations being determined at least in part based on the data received from the camera vision or spectrometer system.   
     
     
         23 . The processor-readable non-transitory medium of  claim 21 , wherein the subsequent plurality of engineered host cells includes at least one engineered host cell with at least a predicted 10% increase in the degree of the desired phenotype compared to that an engineered host cell from the first plurality of engineered host cells. 
     
     
         24 . The processor-readable non-transitory medium of  claim 21 , wherein the subsequent plurality of engineered host cells includes at least one engineered host cell with a predicted 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. 
     
     
         25 . The processor-readable non-transitory medium of  claim 21 , wherein:
 the instructions are first instructions,   the code includes code to cause the processor to send second instructions to a thermal regulator to maintain a temperature of samples containing the first plurality of engineered host cells within a predetermined temperature range.   
     
     
         26 . The processor-readable non-transitory medium of  claim 21 , wherein the code to cause the processor to send the instructions to the automated liquid and particle handling robotics includes code to cause the process to send the instructions to the automated liquid and particle handling robotics that causes the automated liquid and particle handling robotics to perform liquid and particle manipulations including one or more of: aspiration, dispensing, mixing, diluting, washing, volumetric transfers, retrieving and discarding of pipette tips, or repetitive pipetting of identical volumes, 
     
     
         27 . The processor-readable non-transitory medium of  claim 21 , wherein the code to cause the processor to send the instructions to the automated liquid and particle handling robotics robotics includes code to cause the processor to send the instructions to cause robotic arms of the automated liquid and particle handling robotics to manipulate liquid or particles added to or removed from the cultures. 
     
     
         28 . The processor-readable non-transitory medium of  claim 21 , wherein the code to cause the processor to send the instructions to the automated liquid and particle handling robotics includes code to cause the processor to send the instructions to cause a high-throughput transformation system of the automated liquid and particle handling robotics to transform a base host cell from the plurality of base host cells into an engineered host cell from the first plurality of engineered host cells.