Flux analysis techniques, including flux balance analysis (FBA) and 13C-metabolic flux analysis (MFA), can characterize carbon and energy flows through a cell’s metabolic network. By employing both 13C-labeling experiments and nonlinear programming, 13C-MFA provides a rigorous way of examining cell flux distributions in the central metabolism. FBA, on the other hand, gives a holistic review of optimal fluxomes on the genome scale. In this dissertation, flux analysis techniques were constructed to investigate the microbial metabolisms. First, an open-source and programming-free platform of 13C-MFA (WUFlux) with a user-friendly interface in MATLAB was developed, which allowed both mass isotopomer distribution (MID) analysis and metabolic flux calculations. Several bacterial templates with diverse substrate utilizations were included in this platform to facilitate 13C-MFA model construction. The corrected MID data and flux profiles resulting from our platform have been validated by other available 13C-MFA software. Second, 13C-MFA was applied to investigate the variations of bacterial metabolism in response to genetic manipulations or changing growth conditions. Specifically, we investigated the central metabolic responses to overproduction of fatty acids in Escherichia coli and the carbon flow distributions of Synechocystis sp. PCC 6803 under both photomixotrophic and photoheterotrophic conditions. By employing the software of isotopomer network compartmental analysis, we performed isotopically non-stationary MFA on Synechococcus elongatus UTEX 2973. The 13C-based analysis was also conducted for other non-model species, such as Chloroflexus aurantiacus. The resulting flux distributions detail how cells manage the trade-off between carbon and energy metabolisms to survive under stressed conditions, support high productions of biofuel, or organize the metabolic routes for sustaining biomass growth. Third, conventional FBA is suitable for only steady-state conditions. To describe the environmental heterogeneity in bioreactors and temporal changes of cell metabolism, we integrated genome-scale FBA with growth kinetics (time-dependent information) and cell hydrodynamic movements (space-dependent information). A case study was subsequently carried out for wild-type and engineered cyanobacteria, in which a heterogeneous light distribution in photobioreactors was considered in the model. The resulting integrated genome-scale model can offer insights into both intracellular and extracellular domains and facilitate the analysis of bacterial performance in large-scale fermentation systems. Both steady-state and dynamic flux analysis models can offer insights into metabolic responses to environmental fluctuations and genetic modifications. They are also useful tools to provide rational strategies of constructing microbial cell factories for industrial applications.
|Commitee:||Blankenship, Robert, Foston, Marcus, Moon, Tae Seok, Pakrasi, Himadri, Zhang, Fuzhong|
|School:||Washington University in St. Louis|
|Department:||Energy, Environmental and Chemical Engineering|
|School Location:||United States -- Missouri|
|Source:||DAI-B 77/09(E), Dissertation Abstracts International|
|Subjects:||Chemical engineering, Bioinformatics|
|Keywords:||13c metabolic flux analysis, Flux balance analysis|
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