(567bg) An MILP Approach to the Optimization of Cyanobacteria Metabolic Network for Bioethanol Production
Different alternatives to fossil fuels are currently being studied to reduce the dependence on non-renewable resources. Biofuels constitute relevant sustainable complements and/or substitutes to petroleum fuels due to energy security reasons, environmental concerns, foreign exchange savings, and socioeconomic issues related to the rural sector. The most common renewable fuel is ethanol derived from corn grain and sugar cane (sucrose). The use of lignocellulosic biomass is an attractive alternative, as second generation pathways for bioethanol production. On the other hand, third generation biofuels are obtained through algae. Cyanobacteria are an abundant and diverse group of ancient autotrophic prokaryotes that perform oxygenic photosynthesis; they played a crucial role in the change of reductive to oxidative atmosphere in the Precambrian period. Cyanobacteria live in freshwater, marine and terrestrial environments and show a wide diversity of morphologies, metabolisms and cell structures, they have several features that make them attractive to obtain commercial interest products in pharmaceutical, nutraceutical, biofuels, and other industries. They reach high cellular densities in culture and have simple growth requirements: light, carbon dioxide, and other inorganic nutrients to growth. Few authors have studied ethanol production from cyanobacteria (Deng & Coleman, 1998; Dexter & Fu, 2009). Ethanol production through cyanobacteria allows the coupling of energy production with the capture of industrial carbon dioxide emissions to reduce greenhouse gasses pollution, without competing with food production. However, current reported ethanol yields still require improvement to make this technology economically attractive. For cost-effective production of ethanol, the metabolic pathways involved in its generation must be engineered and optimized. Advances in metabolic engineering and synthetic biology based on gene sequence, biochemical and physiological data availability in public databases with the constant improvement of the mathematical tools can help to accelerate the development of desired phenotypes for the production of economically viable biofuels (Lee et al. 2008; Picataggio, 2009). In this work, we formulate a mixed-integer linear programming model to represent gene deletions in a metabolic network for the recombinant cyanobacteria Synechocystis sp. strain PCC6803 to maximize ethanol production, while maximizing biomass growth. The model includes more than 800 reactions from the glycolysis, the pentose phosphate pathway, citric acid cycle and Calvin cycle, and gene insertions corresponding to pyruvate decarboxylase (pdc) and alcohol dehydrogenase II (adhII) from obligately Zymomonas mobilis into Synechocystis sp. strain PCC6803 (Dexter and Fu, 2009) to enable the ethanol producing pathway. The model has been implemented in GAMS (Brooke et al., 1997) and solved with CPLEX. Numerical results provide useful insights on understanding of cellular metabolism as well as designing of metabolic engineering strategies for ethanol production using carbon dioxide as carbon source.
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