Optimizing cyanobacteria metabolic Networks for etanol production.

PAULO, C. I.; DI MAGGIO, J.; ESTRADA , V.; DIAZ, M. S.

Mar del Plata, Argentina

Congreso; VI Congreso Argentino de Ingeniería Química; 2010

AAIQ-Asociación Argentina de Ingenieros Químicos

Alternatives to fossil fuels are being investigated to reduce the world’s dependence on non-renewable resources. Biofuels are currently considered as relevant sustainable technologies due to energetical reasons, environmental concerns, etc. The most common renewable fuel is ethanol derived from corn grain (starch) 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 (also called blue-green algae) are the oldest autotrophic prokaryotes that perform oxygenic photosynthesis, live in freshwater and marine environments and show a wide diversity of morphologies, metabolisms and structures. Cyanobacteria have several features that make them attractive to obtain commercial interest products: they have high cellular densities, simple growth requirements and they only require light, carbon dioxide, and other inorganic nutrients. Furthermore, 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. A few authors have studied ethanol production from cyanobacteria (Deng & Coleman, 1998; Woods et al., 2004; Yang et al., 2002; Dexter & Fu, 2009). However, current reported ethanol yields still require improvement to make this technology economically attractive. In this work, we formulate a mixed-integer linear programming model to represent gene additions or deletions in a metabolic network for the recombinant cyanobacteria Synechocystis sp. strain PCC6803, by integrating genomic (Kyoto Encyclopedia of Genes and Genomes (KEGG), Kanehisa and Goto, 2000), biochemical and physiological information available, to maximize ethanol production. The model includes more than 500 reactions from the glycolysis, the pentose phosphate pathway, citric acid cycle and Calvin cycle, as well as gene insertions corresponding to the pyruvate decarboxylase (pdc), which catalyzes the non-oxidative decarboxylation of piruvate to produce acetaldehyde and CO2, and for alcohol dehydrogenase II (adhII) which participates in reduction of acetaldehyde to ethanol from obligately Zymomonas mobilis into Synechocystis sp. strain PCC6803 (Dexter and Fu, 2009). 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 and photosynthesis, as well as designing of metabolic engineering strategies for ethanol production using carbon dioxide as carbon source. References Brooke A., D. Kendrick, A. Meeraus, & R. Raman (1998). GAMS Language Guide, Release 2.25, Version 92. GAMS Development Corporation Burja, A., S. Dhamwichukorn, P. Wright, Cyanobacterial postgenomic research and systems biology, Trends in Biotechnology, 21, 11, 540-510. Deng, Ming-De, J. Coleman (1999). Ethanol synthesis by genetic engineering in cyanobacteria, Applied and Environmental Microbiology, Feb., 523-528. Dexter, J. and Fu, P. (2009). Metabolic engineering of cyanobacteria for ethanol production. Energy and Environmental Sciences, DOI: 10.1039/b811937f. Woods, R., J. Coleman, M-de Deng (2004). Genetically modified cyanobacteria for the production of ethanol, the constructs and method thereof, US Patent 6699696. Yang, C et al. (2002) Integration of the information from gene expression and metabolic fluxes for the analysis of the regulatory mechanisms in Synechocistys, Appl. Microbiol. Biotechnol., 58, 813-822