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Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Chemical Engineering

Year Degree Awarded

2017

Month Degree Awarded

September

First Advisor

Michael A. Henson

Subject Categories

Biochemical and Biomolecular Engineering

Abstract

Gas fermentation is an attractive route to produce alternative fuels and chemicals from non-food feedstocks, such as waste gas streams from steel mills and synthesis gas (mainly CO and H2) produced from municipal solid waste through gasification. While commercial development of gas fermentation technology is underway, many research problems must be addressed to further advance the technology towards economic competitiveness. A particularly important challenge is to develop integrated metabolic and transport models that describe gas fermentation in industrially relevant bubble column reactors.

I have developed and evaluated a spatiotemporal metabolic model for bubble column reactors with the syngas fermenting bacterium Clostridium ljungdahlii as the microbial catalyst. My modeling approach involved combining a genome-scale reconstruction of C. ljungdahlii metabolism with multiphase transport equations that govern convective and dispersive processes within the spatially varying column. The reactor model was spatially discretized to yield a large set of ordinary differential equations (ODEs) in time with embedded linear programs (LPs). I used the MATLAB based code DFBAlab to efficiently and robustly solve the discretized model, which consisted of 900 ODEs and 600 LPs due to the use of lexicographic optimization. Column startup was dynamically simulated under different operating conditions. The resulting steady-state solutions were compared to analyze the effect of operating parameters on key measures of reactor performance including ethanol titer, ethanol-to-acetate ratio, and CO and H2 conversions. I showed that the bubble column configuration outperformed a traditional stirred tank reactor in terms of ethanol productivity when computationally evaluated at comparable operating conditions. In addition to providing new insights into bottlenecks to biochemical production in syngas bubble column reactors, the study established a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations.

I also performed in silico metabolic engineering studies using the genome-scale reconstruction of C. ljungdahlii metabolism and the OptKnock computational framework to identify gene knockouts that were predicted to enhance the synthesis of these native and non-native products, introduced through insertion of the necessary heterologous pathways. The OptKnock derived strategies were often difficult to assess because increase product synthesis was invariably accompanied by decreased growth. Therefore, the OptKnock strategies were further evaluated using my spatiotemporal metabolic model of syngas fermentation. Unlike conventional flux balance analysis, the bubble column model accounted for the complex tradeoffs between increased product synthesis and reduced growth rates of engineered mutants within the spatially varying column environment. The two-stage methodology for deriving and evaluating metabolic engineering strategies was shown to yield new C. ljungdahlii gene targets that offer the potential for increased product synthesis under realistic syngas fermentation conditions.

Clostridium autoethanogenum, an acetogenic bacterium, was developed by LanzaTech and shows high potential in production of ethanol and 2,3-butanediol from industry waste gas (mainly CO and CO2) via fermentation. I developed a spatiotemporal metabolic model using steady-state CO fermentation data collected from a laboratory-scale bubble column reactor at LanzaTech. The bubble column model provided good agreement with measured ethanol, acetate and biomass concentrations obtained at a single gas flow rate. To obtain satisfactory steady-state predictions over a range of gas flow rates, the upper bound of the proton exchange flux in the C. autoethanogenum genome-scale reconstruction was correlated with the gas flow rate as an indirect means to account for the effects of acetate secretion on extracellular pH. These results demonstrate that the modeling method established in this thesis have strong potential to facilitate commercial-scale design of gas fermentation processes for production of biofuel and biochemicals.

Available for download on Saturday, July 28, 2018

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