INVESTIGADORES
DIAZ Maria Soledad
congresos y reuniones científicas
Título:
Bioethanol from cyanobacteria. Metabolic network optimization by mathematical modeling.
Autor/es:
CECILIA I. PAULO; VANINA G. ESTRADA; MARIA SOLEDAD DIAZ
Lugar:
San Pedro
Reunión:
Congreso; 2nd Pan American Congress on Plants and Bioenergy; 2010
Institución organizadora:
Universidad Rio de Janeiro
Resumen:
The most common renewable fuel is ethanol derived from corn grain and sugar cane.
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. 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, 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. 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. In this work, we formulate a mixed-integer linear programming model in
which
binary variables have been associated to the existence or not of each reaction
path. This is in turn associated to gene addition and/or knockouts in the 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 fermentative
Zymomonas mobilis into Synechocystis sp. strain PCC 6803 under the control of the
strong light driven psbAII promoter, which allows ethanol production under light
conditions
(Deng and Coleman, 1998).
The model has been implemented in GAMS
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 and it can be seen that under
photoutotrophic
growth, the fermentation pathway has nonzero rates showing that the
engineered microorganism is able to make photosynthesis and fermentation at the
same timeinto Synechocystis sp. strain PCC 6803 under the control of the
strong light driven psbAII promoter, which allows ethanol production under light
conditions
(Deng and Coleman, 1998).
The model has been implemented in GAMS
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 and it can be seen that under
photoutotrophic
growth, the fermentation pathway has nonzero rates showing that the
engineered microorganism is able to make photosynthesis and fermentation at the
same time