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The Design and Engineering of Microbial Metabolism Using Constraint-Based Models.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Design and Engineering of Microbial Metabolism Using Constraint-Based Models./
作者:	Purdy, Hugh M.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	103 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Chemical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28721783
ISBN:	9798538161058

The Design and Engineering of Microbial Metabolism Using Constraint-Based Models.
Purdy, Hugh M.

The Design and Engineering of Microbial Metabolism Using Constraint-Based Models. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 103 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.

This item must not be sold to any third party vendors.

With the perpetually increasing need for sustainable alternatives to substances that are currently derived from non-renewable resources, microbial chemical production has risen as a promising approach. Key to the engineering of microbial production strains is the ability to quantify and model their metabolic phenotypes. To this end, constraint-based genome-scale metabolic models have proven to be instrumental in both directly guiding strain engineering efforts and in aiding our understanding of fundamental aspects of metabolism. This work details both the application and development of several different constraint-based metabolic modeling methods, as well as an overview of the fundamental theory behind these methods and a survey of recent developments in the field. The first work described herein details the use of a genome-scale metabolic model of the cyanobacterium Synechococcus sp. PCC 7002 to identify metabolic interventions that enable the strain to show improved production of several branched-alcohols with fuel potential. The strain design strategy that was identified appears to be uniquely suited to improving cyanobacterial production of this class of biofuel alcohols. A second study described in this work centers on the development of a computational workflow for predicting novel biosynthesis pathways for large sets of target compounds. Using this workflow in conjunction with a large database of high-volume commodity chemicals, we identified a set of novel targets for metabolic engineering efforts. We additionally showed that many of the chemicals in our dataset did not have pathways identified for them, and that as such it is possible that entirely new classes of enzymatic reactions may be needed in order to produce many of the chemicals that are prevalent on the market today.

ISBN: 9798538161058Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Cyanobacteria

The Design and Engineering of Microbial Metabolism Using Constraint-Based Models.
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506 $a This item must not be sold to any third party vendors.
520 $a With the perpetually increasing need for sustainable alternatives to substances that are currently derived from non-renewable resources, microbial chemical production has risen as a promising approach. Key to the engineering of microbial production strains is the ability to quantify and model their metabolic phenotypes. To this end, constraint-based genome-scale metabolic models have proven to be instrumental in both directly guiding strain engineering efforts and in aiding our understanding of fundamental aspects of metabolism. This work details both the application and development of several different constraint-based metabolic modeling methods, as well as an overview of the fundamental theory behind these methods and a survey of recent developments in the field. The first work described herein details the use of a genome-scale metabolic model of the cyanobacterium Synechococcus sp. PCC 7002 to identify metabolic interventions that enable the strain to show improved production of several branched-alcohols with fuel potential. The strain design strategy that was identified appears to be uniquely suited to improving cyanobacterial production of this class of biofuel alcohols. A second study described in this work centers on the development of a computational workflow for predicting novel biosynthesis pathways for large sets of target compounds. Using this workflow in conjunction with a large database of high-volume commodity chemicals, we identified a set of novel targets for metabolic engineering efforts. We additionally showed that many of the chemicals in our dataset did not have pathways identified for them, and that as such it is possible that entirely new classes of enzymatic reactions may be needed in order to produce many of the chemicals that are prevalent on the market today.
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