Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bioinformatic tools and techniques.

Abstract: Provided are native promoters comprising polynucleotides isolated from Corynebacterium glutamicum, and mutant promoters derived therefrom, which may be used to regulate, i.e., either increase or decrease, gene expression. Also provided are promoter ladders comprising a plurality of the promoters having incrementally increasing promoter activity. Also provided are host cells and recombinant vectors comprising the promoters, and methods of expressing genes of interest and producing biomolecules using the host cells.

Abstract: Systems, methods and computer-readable media are provided for designing experiments for organisms at a first scale to generate first-scale performance data used in predicting performance of the organisms at a second, larger scale. The design includes determining first-scale screening conditions based at least in part upon the contribution of second-scale conditions to performance parameters of an organism at the second scale. The first-scale screening conditions include one or more proxies for second-scale conditions that cannot be replicated at first scale. The design determines first-scale screening parameters based at least in part upon computer modeling of the metabolism of the organism at the second scale.

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: Provided are native promoters comprising polynucleotides isolated from Corynebacterium glutamicum, and mutant promoters derived therefrom, which may be used to regulate, i.e., either increase or decrease, gene expression. Also provided are promoter ladders comprising a plurality of the promoters having incrementally increasing promoter activity. Also provided are host cells and recombinant vectors comprising the promoters, and methods of expressing genes of interest and producing biomolecules using the host cells.

Abstract: The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

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 methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

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 methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

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: Systems, methods and computer-readable media storing executable instructions are provided for improving performance of an organism with respect to a phenotype of interest at a second scale based upon measurements at a first scale. First scale performance data based at least in part upon observed first performance of first organisms at a first scale and second scale performance data based at least in part upon observed second performance of second organisms at a second scale larger than the first scale are accessed. A prediction function based at least in part upon the relationship of the second scale performance data to the first scale performance data is generated. The prediction function may be applied to performance data observed for test organisms with respect to the phenotype of interest at the first scale to generate second scale predicted performance data for the test organisms at the second scale.