In silico strategies to couple production of bioethanol with growth in cyanobacteria

Cyanobacteria have been considered as promising candidates for sustainable bioproduction from inexpensive raw materials, as they grow on light, carbon dioxide, and minimal inorganic nutrients. In this study, we present a genome-scale metabolic network model for Synechocystis sp. PCC 6803 and study the optimal design of the strain for ethanol production by using a mixed integer linear problem reformulation of a bilevel programming problem that identifies gene knockouts which lead to coupling between growth and product synthesis. Five mutants were found, where the in silico model predicts coupling between biomass growth and ethanol production in photoautotrophic conditions. The best mutant gives an in silico ethanol production of 1.054 mmol·gDW ⁻¹·h ⁻¹.