Date of Award

Spring 5-15-2018

Author's School

School of Engineering & Applied Science

Author's Department

Mechanical Engineering & Materials Science

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

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.

Language

English (en)

Chair

Yinjie Tang

Committee Members

Philip Bayly, David Peters, Spencer Lake, Fuzhong Zhang,

Comments

Permanent URL: https://doi.org/10.7936/K7HT2NS3

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