CONICET | Buscador de Institutos y Recursos Humanos

Cyanobacteria are autotrophic unicellular microorganisms that are gaining significance as a mediumto obtain fourth generation hydrocarbon biofuels, owing to their capacity to use solar energy,atmospheric carbon dioxide and water to grow. In this work, we analyze the metabolic network ofthe cyanobacterium Synechocystis PCC 6803 for the production of ethanol. A genomic scalemetabolic model of the network, which has 523 metabolites and 661 reactions, is considered (Knoopet al. 2013). Firstly, the genetic modifications (i.e., knockout of genes, overexpression of genes)already made to the strain experimentally by different authors are considered. A Flux BalanceAnalysis (FBA) optimization technique is used to model these modifications and ascertain this waythe best ethanol production rates that can obtained from these modified strains. The results arecompared with the experimental results of the authors considered (Dexter and Fu 2009, Duhring etal. 2010, Gao et al. 2012, Dienst et al. 2014). Secondly, a bileveloptimization technique is used todetermine what other modifications could be done to the cyanobacterium to improve its ethanolyield. For this, the maximization of biomass production is set as the inner problem and themaximization of ethanol production is set as the outer problem. Biomass and ethanol productionpose a cellular objective and a biotechnological objective, respectively. Binary variables are added tomodel the possibility of a knockout. This gives rise, after reformulating the problem with availabletechniques, to a mixed integer linear problem (MILP). All the models formulated in this work weresolved using GAMS. Numerical results obtained provide useful insights on the biofuel production ofthis strain within the context of genomicscalecyanobacterial metabolism

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