Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic or semi-automatic bioprocess design, execution, and analysis. In one aspect, a system comprises receiving a request to execute a sequence of unit operations specified by a recipe representing a bioprocess, wherein each unit operation defines one or more steps to be performed, generating, for each step of each unit operation, a worksheet representing laboratory effects of performing the step and having a worksheet interface for displaying bioprocess data and receiving execution data, including initializing, for each worksheet, a corresponding electronic laboratory notebook object in an underlying electronic laboratory notebook subsystem, receiving execution data entered into the worksheet interface for a particular step of a first unit operation, and updating, in the electronic laboratory notebook subsystem, the corresponding electronic laboratory notebook object using the execution data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic or semi-automatic bioprocess design, execution, and analysis. In one aspect, a system comprises receiving a request to execute a sequence of unit operations specified by a recipe representing a bioprocess, wherein each unit operation defines one or more steps to be performed, generating, for each step of each unit operation, a worksheet representing laboratory effects of performing the step and having a worksheet interface for displaying bioprocess data and receiving execution data, including initializing, for each worksheet, a corresponding electronic laboratory notebook object in an underlying electronic laboratory notebook subsystem, receiving execution data entered into the worksheet interface for a particular step of a first unit operation, and updating, in the electronic laboratory notebook subsystem, the corresponding electronic laboratory notebook object using the execution data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatic or semi-automatic bioprocess design, execution, and analysis. In one aspect, a system comprises receiving a request to execute a sequence of unit operations specified by a recipe representing a bioprocess, wherein each unit operation defines one or more steps to be performed, generating, for each step of each unit operation, a worksheet representing laboratory effects of performing the step and having a worksheet interface for displaying bioprocess data and receiving execution data, including initializing, for each worksheet, a corresponding electronic laboratory notebook object in an underlying electronic laboratory notebook subsystem, receiving execution data entered into the worksheet interface for a particular step of a first unit operation, and updating, in the electronic laboratory notebook subsystem, the corresponding electronic laboratory notebook object using the execution data.

Abstract: 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.

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.

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.

Abstract: 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.

Abstract: The generation of a factory order includes receiving an expression indicating an operation on a first sequence operand and a second sequence operand. The first sequence operand represents multiple biological sequence parts, and the second sequence operand represents at least one biological sequence part. The expression is evaluated to a sequence specification, which represents modifications to at least one biological sequence, and comprises a data structure representing (a) the first and second sequence operands, (b) one or more first-level operations to be performed on one or more first-level sequence operands, and (c) one or more second-level operations, the execution of at least one of which resolves values of at least one of the first-level sequence operands. A factory order is generated based upon execution of at least one first-level operation and at least one second-level operation.

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.

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.

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.

Abstract: 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.

Abstract: 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.

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.

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.

Abstract: 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.

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.

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.

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.

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.