Fourth Generation Biofuels: Metabolic Modelling of Synechocystis sp. PCC 6803 for Ethanol Production

Cyanobacteria are autotrophic unicellular microorganisms that are gaining significance as a medium to 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 of the cyanobacterium Synechocystis PCC 6803 for the production of ethanol. A genomic scale metabolic model of the network, which has 523 metabolites and 661 reactions, is considered (Knoop et 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 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 with the experimental results of the authors considered (Dexter and Fu 2009, Duhring et al. 2010, Gao et al. 2012, Dienst et al. 2014). Secondly, a bi-level optimization technique is used to determine what other modifications could be done to the cyanobacterium to improve its ethanol yield. For this, 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. This gives rise, after reformulating the problem with available techniques, to a mixed integer linear problem (MILP). All the models formulated in this work were solved using GAMS. Numerical results obtained provide useful insights on the biofuel production of this strain within the context of genomic-scale cyanobacterial metabolism.


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