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Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems./
Author:	Wan, Ni.
Description:	1 online resource (164 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Subject:	Mechanical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355623352

Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems.
Wan, Ni.

Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems. - 1 online resource (164 pages)

Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.

Thesis (Ph.D.)--Washington University in St. Louis, 2018.

Includes bibliographical references

Metabolic modeling is an important tool to interpret the comprehensive cell metabolism and dynamic relationship between substrates and biomass/bioproducts. Genome-scale flux balance model and 13C-metabolic flux analysis are metabolic models which can reveal the theoretical yield and central carbon metabolism under various environmental conditions. Kinetic model is able to capture the complex principles between the change of biomass growth and bioproducts accumulation with the time series. Machine learning model is a data driven approach to reveal fermentation behavior and further predict cell performance under complex circumstances. In my PhD study, modeling analysis and machine learning method have been used to exam non-conventional microbial systems. (1) decode the functional pathway and carbon flux distribution in Cyanobacteria and Clostridium species for bio productions, (2) characterize biofilm physiologies and biodiesel fermentations (engineered E.coli) under mass transfer limitations, and (3) optimize syngas fermentations by deciphering and overcoming rate limiting process factor.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355623352Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems.
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245 1 0 $a Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems.
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300 $a 1 online resource (164 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
500 $a Adviser: Yinjie Tang.
502 $a Thesis (Ph.D.)--Washington University in St. Louis, 2018.
504 $a Includes bibliographical references
520 $a Metabolic modeling is an important tool to interpret the comprehensive cell metabolism and dynamic relationship between substrates and biomass/bioproducts. Genome-scale flux balance model and 13C-metabolic flux analysis are metabolic models which can reveal the theoretical yield and central carbon metabolism under various environmental conditions. Kinetic model is able to capture the complex principles between the change of biomass growth and bioproducts accumulation with the time series. Machine learning model is a data driven approach to reveal fermentation behavior and further predict cell performance under complex circumstances. In my PhD study, modeling analysis and machine learning method have been used to exam non-conventional microbial systems. (1) decode the functional pathway and carbon flux distribution in Cyanobacteria and Clostridium species for bio productions, (2) characterize biofilm physiologies and biodiesel fermentations (engineered E.coli) under mass transfer limitations, and (3) optimize syngas fermentations by deciphering and overcoming rate limiting process factor.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
655 7 $a Electronic books. $2 local $3 554714
690 $a 0548
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Washington University in St. Louis. $b Mechanical Engineering. $3 1148609
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10744536 $z click for full text (PQDT)