This thesis presents the mathematical modeling of two new Escherichia coli platforms with economical potential for the production of biofuels and chemicals, namely glycerol fermentation and the reversal of the β-oxidation cycle. With the increase in traditional fuel prices, alternative renewable energy sources are needed, and the efficient production of biofuels becomes imperative. So far studies have focused on using glucose as feedstock for the production of ethanol and other fuels, but a recent increase in glycerol availability and its consequent decrease in price make it an attractive feedstock. Furthermore, the reversed β-oxidation cycle is a highly efficient mechanism for the synthesis of long-chain products. These two platforms have been reported experimentally in E. coli but their mathematical modeling is presented for the first time here.
Because mathematical models have proved to be useful in the optimization of microbial metabolism, two complementary models were used in this study: kinetic and stoichiometric. Kinetic models can identify the control structure within a specific pathway, but they require highly detailed information, making them applicable to small sets of reactions. In contrast, stoichiometric models require only mass balance information, making them suitable for genome-scale modeling to study the effect of adding or removing reactions for the optimization of the synthesis of desired products.
To study glycerol fermentation, a kinetic model was implemented, allowing prediction of the limiting enzymes of this process: glycerol dehydrogenase and di-hydroxyacetone kinase. This prediction was experimentally validated by increasing their enzymatic activities, resulting in a two-fold increase in the rate of ethanol production. Additionally, a stoichiometric genome-scale model (GEM) was modified to represent the fermentative metabolism of glycerol, identifying key metabolic pathways for glycerol fermentation (including a new glycerol dissimilation pathway). The GEM was used to identify genetic modifications that would increase the synthesis of desired products, such as succinate and butanol.
Finally, glucose metabolism using the reversal β-oxidation cycle was modeled using a GEM to simulate the synthesis of a variety of medium and long chain products (including advanced biofuels). The model was used to design strategies that can lead to increase the productivity of target products.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/71940 |
| Date | 16 September 2013 |
| Creators | Cintolesi Makuc, Angela |
| Contributors | Gonzalez, Ramon |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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