Patents by Inventor Erik Jedediah Dean
Erik Jedediah Dean has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10883101Abstract: 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.Type: GrantFiled: May 29, 2020Date of Patent: January 5, 2021Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20200370058Abstract: The present disclosure provides a HTP genomic engineering platform for improving Escherichia coli. 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.Type: ApplicationFiled: June 6, 2018Publication date: November 26, 2020Inventors: Matthew Davis, Christy Wisnewski, Patrick Westfall, Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Michael Flashman, Robert Haushalter, Stacy-Anne Morgan, Michael Blaisse, Prabha Ramakrishnan, Kyle Rothschild-Mancinelli, Youngnyun Kim
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Patent number: 10808243Abstract: 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.Type: GrantFiled: April 2, 2020Date of Patent: October 20, 2020Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20200291392Abstract: 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.Type: ApplicationFiled: May 29, 2020Publication date: September 17, 2020Inventors: ZACH SERBER, ERIK JEDEDIAH DEAN, SHAWN MANCHESTER, KATHERINE GORA, MICHAEL FLASHMAN, ERIN SHELLMAN, AARON KIMBALL, SHAWN SZYJKA, BARBARA FREWEN, THOMAS TREYNOR, KENNETH S. BRUNO
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Patent number: 10745694Abstract: 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.Type: GrantFiled: November 27, 2019Date of Patent: August 18, 2020Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20200239873Abstract: 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.Type: ApplicationFiled: April 2, 2020Publication date: July 30, 2020Inventors: Zach SERBER, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20200173900Abstract: The present disclosure provides a microbe engineering platform that permits optimization of microbe fitness levels to optimize a microbe's suitability for industrial fermentation. The disclosed platform identifies an association between microbe properties and microbe fitness levels. The association between microbe properties and microbe fitness levels may be used to identify candidate microbes with desired fitness levels. The identified candidate microbes may be used to further optimize the industrial fermentation process.Type: ApplicationFiled: May 4, 2018Publication date: June 4, 2020Applicant: ZYMERGEN INC.Inventors: WILLIAM SERBER, ERIK JEDEDIAH DEAN
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Publication number: 20200149035Abstract: 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.Type: ApplicationFiled: November 27, 2019Publication date: May 14, 2020Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Patent number: 10647980Abstract: 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.Type: GrantFiled: July 1, 2019Date of Patent: May 12, 2020Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20200131508Abstract: The present disclosure relates to methods of joining three or more double-stranded (ds) or single-stranded (ss) DNA molecules of interest in vitro or in vivo. The method allows the joining of a large number of DNA fragments, in a deterministic fashion. It can be used to rapidly generate nucleic acid libraries that can be subsequently used in a variety of applications that include, for example, genome editing and pathway assembly. Kits for performing the method are also disclosed.Type: ApplicationFiled: October 31, 2019Publication date: April 30, 2020Inventors: Erik Jedediah Dean, Kedar Patel, Aaron Miller, Kunal Mehta, Philip Weyman
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Publication number: 20200058376Abstract: Systems, methods and computer-readable media are provided to predict properties of a material that is related to a bioreachable molecule by generating a chemical model of the material based on physicochemical properties and predicting properties of the material based at least in part upon the chemical model and correlative modeling. The material may comprise in its chemical structure one or more instances of the bioreachable molecule. The material may comprise in its chemical structure the bioreachable molecule or at least one semi-synthetic molecule derived from the bioreachable molecule, or a combination thereof.Type: ApplicationFiled: August 15, 2019Publication date: February 20, 2020Applicant: Zymergen Inc.Inventors: Erik Jedediah Dean, Vanessa Blue Oklejas, Alexander Glennon Shearer, Vytas SunSpiral, Michelle L. Wynn, Lucas Andrew Zulauf
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Publication number: 20200048628Abstract: 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.Type: ApplicationFiled: October 28, 2019Publication date: February 13, 2020Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20190392919Abstract: Systems and methods for predicting the viability of producing target molecules in a host organism are provided. A starting metabolite set for the host and a reaction set are obtained. Included in a filtered reaction set are reactions that are indicated as catalyzed by one or more corresponding catalysts that are themselves indicated as likely available to catalyze the reactions in the host organism. In each processing step, pursuant to the reactions of the filtered reaction set, data representing the starting metabolites and metabolites generated in previous processing steps is processed, to generate data representing one or more viable target molecules.Type: ApplicationFiled: August 12, 2019Publication date: December 26, 2019Applicant: Zymergen Inc.Inventors: Erik Jedediah Dean, Alexander G. Shearer, Michelle L. Wynn
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Patent number: 10457933Abstract: 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.Type: GrantFiled: March 16, 2018Date of Patent: October 29, 2019Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20190316117Abstract: 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.Type: ApplicationFiled: July 1, 2019Publication date: October 17, 2019Applicant: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Patent number: 10336998Abstract: 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.Type: GrantFiled: March 16, 2018Date of Patent: July 2, 2019Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Patent number: 10047358Abstract: 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.Type: GrantFiled: March 16, 2018Date of Patent: August 14, 2018Assignee: Zymergen Inc.Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20180216099Abstract: 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.Type: ApplicationFiled: March 16, 2018Publication date: August 2, 2018Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20180216101Abstract: 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.Type: ApplicationFiled: March 16, 2018Publication date: August 2, 2018Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno
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Publication number: 20180216100Abstract: 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.Type: ApplicationFiled: March 16, 2018Publication date: August 2, 2018Inventors: Zach Serber, Erik Jedediah Dean, Shawn Manchester, Katherine Gora, Michael Flashman, Erin Shellman, Aaron Kimball, Shawn Szyjka, Barbara Frewen, Thomas Treynor, Kenneth S. Bruno