Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production

Abstract

Cyanobacteria are ideal metabolic engineering platforms for carbon-neutral biotechnology because they directly convert CO2 to a range of valuable products. In this study, we present a computational assessment of biochemical production in Synechococcus sp. PCC 7002 (Synechococcus 7002), a fast growing cyanobacterium whose genome has been sequenced, and for which genetic modification methods have been developed. We evaluated the maximum theoretical yields (mol product per mol CO2 or mol photon) of producing various chemicals under photoautotrophic and dark conditions using a genome-scale metabolic model of Synechococcus 7002. We found that the yields were lower under dark conditions, compared to photoautotrophic conditions, due to the limited amount of energy and reductant generated from glycogen. We also examined the effects of photon and CO2 limitations on chemical production under photoautotrophic conditions. In addition, using various computational methods such as minimization of metabolic adjustment (MOMA), relative metabolic change (RELATCH), and OptORF, we identified gene-knockout mutants that are predicted to improve chemical production under photoautotrophic and/or dark anoxic conditions. These computational results are useful for metabolic engineering of cyanobacteria to synthesize value-added products.