Abstract:
Global supplies of drinking water are increasingly under threat from toxic cyanobacterial blooms. Although the phenomena have been extensively studied, no factors have yet been shown to consistently govern toxin production. In this thesis we construct and use a stoichiometric metabolic network (SMN) model to investigate the effect of environmental parameters such as nitrogen, phosphorus, and light on growth and toxin production in cyanobacteria. Microcystis aeruginosa was chosen as the model organism as it has often been reported in literature for its ability to produce hepatotoxin compound microcystin-LR. The SMN model for M. aeruginosa comprises 197 reactions and 178 metabolites of the metabolic pathways for carbon and nitrogen assimilation, photosynthesis, amino acid synthesis, and cyanotoxin synthesis. Calibration showed that model was in good agreement with the values reported in literature with a high R2 value and low root mean square error value. Calibration yielded estimates for biomass and toxin production rates of 0.036 hr-1and 0.003 hr-1 respectively, which were in good agreement (root mean square error < 0.1) with values 0.017 hr-1 for biomass production rate and 0.0016 hr-1 for toxin production rate reported in literature. Model results showed that the environmental variables such as nitrogen, phosphorus and light can influence growth and microcystin synthesis in M. aeruginosa; wherein the influence on microcystin synthesis rate can be independent of the effects on growth. Sensitivity analysis indicated that phosphorus is the most significant parameter controlling M. aeruginosa growth. Any of the three factors directly affected microcystin production with nitrogen the most significant. Simulation results showed that the correlation between growth and microcystin rate were found only under specific nutrient or light limiting conditions. It is concluded from the model simulations that microcystin production in M. aeruginosa is under multiple regulation of environmental parameters, rather than a welldistinguishable control of single specific factor.