國立虎尾科技大學 |

Language: English

Back

Application of Metabolic Modeling an...

Washington University in St. Louis.

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems./
Author:	Wan, Ni.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	164 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-07(E), Section: B.
Contained By:	Dissertation Abstracts International79-07B(E).
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10744536
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. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 164 p.

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

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

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.

ISBN: 9780355623352Subjects--Topical Terms:

557493
Mechanical engineering.

Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems.
LDR:02053nam a2200289 4500 001 890777
005 20180727091503.5
008 180907s2018 ||||||||||||||||| ||eng d
020 $a 9780355623352
035 $a (MiAaPQ)AAI10744536
035 $a (MiAaPQ)wustl:12420
035 $a AAI10744536
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wan, Ni. $3 1148684
245 1 0 $a Application of Metabolic Modeling and Machine Learning for Investigating Microbial Systems.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 164 p.
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.
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.
590 $a School code: 0252.
650 4 $a Mechanical engineering. $3 557493
690 $a 0548
710 2 $a Washington University in St. Louis. $b Mechanical Engineering. $3 1148609
773 0 $t Dissertation Abstracts International $g 79-07B(E).
790 $a 0252
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10744536

based on 0 review(s)

Multimedia

Reviews

Add a review and share your thoughts with other readers

Export

pickup library