Metabolic Network of Synechococcus Sp PCC 7002: Genome Scale Metabolic Model, Flux Analysis and Network Modifications for Biofuel Production

Synechococcus sp PCC 7002 is a model cyanobacterium with characteristics favorable for industrial scale bioprocess applications such as: shorter doubling time (~4h), ability to with stand high light intensities, tolerance to high salt concentrations, naturally transformable etc. In this study Genome Scale Metabolic model and Constraint Based Reconstruction Analysis (COBRA) are used to analyze the metabolism of this cyanobacterium. Gene essentiality and synthetic lethality analysis were used to analyse the robustness under gene perturbation. The number of essential and synthetic lethal genes revealed that the metabolic network of PCC 7002 is less robust than Synechocystis sp PCC 6803 under gene perturbation. Flux distributions under photoautotrophic, heterotrophic (in glycerol) and mixotrophic (in glycerol) conditions were predicted to gain insights into the metabolism of this organism. It was observed that the heterotrophic metabolism in glycerol is different from the glucose metabolism under dark. Heterotrophic growth in glycerol showed a complete TCA cycle and zero flux through the Oxidative Pentose Phosphate (OPP) Pathway. Minimization of Metabolic Adjustment (MOMA) algorithm, was used to identify candidate mutants that produce enhanced ethanol and butanol. An exhaustive analysis of Photoautotrophic flux distribution of all single and double gene reaction knock out mutants was done to identify mutants with high production rates of the target chemicals. The candidates with higher production rates were analysed in detail. It was found that double gene knock outs could divert only ~10% of the fixed carbon towards the target biofuel production.