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Machine Learning and Optimization Me...

Kumar, Prashant.

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Machine Learning and Optimization Methods to Engineer Microbes.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning and Optimization Methods to Engineer Microbes./
Author:	Kumar, Prashant.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	162 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
Contained By:	Dissertations Abstracts International81-08B.
Subject:	Chemical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10827628
ISBN:	9781392633786

Machine Learning and Optimization Methods to Engineer Microbes.
Kumar, Prashant.

Machine Learning and Optimization Methods to Engineer Microbes. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 162 p.

Source: Dissertations Abstracts International, Volume: 81-08, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018.

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

Microbes are a rich source of numerous industrially relevant chemicals. During my Ph.D., I worked on developing machine learning and optimization methods to engineer microbes. There are primarily three research projects and a review study mentioned in this work. I contributed to a book chapter on metabolic modeling for design of cell factories. In Chapter 2, I have described the work done on reconstructing the genome-scale metabolic model of two strains of Gluconacetobacter, namely hansenii ATCC 53582 and xylinus 399. I designed strains of these two strains with four and five gene knockouts to enhance the biocellulose yields from them. In Chapter 3, I describe a method, iRegMet which can make predictions about the cellular behavior under perturbation using the integrated regulatory and metabolic network of microbes. iRegMet was used to make predictions of transcription factor knockout gene expressions and cellular growth. In Chapter 4, I have described ActiveOpt, an active learning, and machine learning driven design of experiments tool. ActiveOpt was successfully validated on two separate datasets.

ISBN: 9781392633786Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Machine learning

Machine Learning and Optimization Methods to Engineer Microbes.
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245 1 0 $a Machine Learning and Optimization Methods to Engineer Microbes.
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300 $a 162 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-08, Section: B.
500 $a Advisor: Reed, Jennifer L.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2018.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Microbes are a rich source of numerous industrially relevant chemicals. During my Ph.D., I worked on developing machine learning and optimization methods to engineer microbes. There are primarily three research projects and a review study mentioned in this work. I contributed to a book chapter on metabolic modeling for design of cell factories. In Chapter 2, I have described the work done on reconstructing the genome-scale metabolic model of two strains of Gluconacetobacter, namely hansenii ATCC 53582 and xylinus 399. I designed strains of these two strains with four and five gene knockouts to enhance the biocellulose yields from them. In Chapter 3, I describe a method, iRegMet which can make predictions about the cellular behavior under perturbation using the integrated regulatory and metabolic network of microbes. iRegMet was used to make predictions of transcription factor knockout gene expressions and cellular growth. In Chapter 4, I have described ActiveOpt, an active learning, and machine learning driven design of experiments tool. ActiveOpt was successfully validated on two separate datasets.
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650 4 $a Engineering. $3 586835
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653 $a Optimization
653 $a Systems biology
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773 0 $t Dissertations Abstracts International $g 81-08B.
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