ESTRADA Vanina Gisela
congresos y reuniones científicas
Metabolic Network Design for Ethanol Production By Synechocystis sp. PCC 6803
San Francisco, CA
Congreso; AIChE Annual Meeting 2016; 2016
In this work, we formulate a genomic scale metabolic network model of Synechocystis PCC 6803 within a bilevel opimization framework for ethanol production. The model, which is an extensión Knoop et al. (2013), has 523 metabolites and 661 reactions. As a first step, the genetic modifications (i.e., knockout of genes, overexpression of genes) already made to the strain experimentally by different authors are considered. A Flux Balance Analysis (FBA) optimization technique is used to model these modifications and ascertain this way the best ethanol production rates that can obtained from these modified strains. The results are compared to experimental data from literature (Dexter and Fu 2009, Duhring et al. 2010, Gao et al. 2012, Dienst et al. 2014). As a second step, a bilevel optimization strategy is applied to determine what other modifications could be done to the cyanobacterium to improve its ethanol yield. In this case, the maximization of biomass production is set as the inner problem and the maximization of ethanol production is set as the outer problem. Biomass and ethanol production pose a cellular objective and a biotechnological objective, respectively. Binary variables are added to model the possibility of a knockout. The bilevel optimization problem is reformulated to a single level one by replacing the inner problem by the constraints corresponding to the primal and its corresponding dual problem, plus equating both primal and dual objective function. The resulting problema is a mixed integer linear problem (MILP). Metabolic network models have been implemented in GAMS. Numerical results provide useful insights on the biofuel production of this strain within the context of genomic-scale cyanobacterial metabolism.ReferencesDexter, J. and Fu, P. (2009). Metabolic engineering of cyanobacteria for ethanol production. Energy and Environmental Sciences, 2: 857-864.Dienst, D., Georg, J., Abts, T., Jakorew, L., Kuchmina, E., Borner, T., Wilde, A., Duhring, U., Enke, H. and Hess, W. R. (2014). Transcriptomic response to prolonged ethanol production in the cyanobacterium Synechocystis sp. PCC 6803. Biotechnol Biofuels, 7: 1-21.Duhring, U., Enke, H., Kramer, D., Smith, C. R., Woods, R. P., Baier, K. and Oesterhelt, C. (2010) Genetically modified photoautotrophic ethanol producing host cells, method for producing the host cells, constructs for the transformation of the host cells, method for testing a photoautotrophic strain for a desired growth property and method of producing ethanol using the host cells. Patent Publication Number: US20100297736A1.Gao, Z., Zhao, H., Li, Z., Tan, X. and Lu, X. (2012) Photosynthetic production of ethanol from carbon dioxide in genetically engineered Cyanobacteria. Energy Environ Sci, 5: 9857â??9865.Knoop H., Gründel M., Zilliges Y., Lehmann R., Hoffmann S., Lockau W. and Steuer, R. (2013). Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803. PLoS Comput. Biol. 9:e1003081.10.1371/journal.pcbi.1003081.