Maximization of growth and lipid production of a toxic isolate of Anabaena circinalis by optimization of various parameters with mathematical modeling and computational validation

Toxic cyanobacterial blooms are recurrent for few decades throughout the globe, due to climate change, atmospheric warming and various anthropogenic activities with severe impacts of potential toxins on various ecosystems finally affecting the entire environment. These cyanobacteria are merely unexplored regarding their biochemical components except toxins. Variable influences and interactions of different factors including nitrogen, carbon, and availability of light are well known to crucially regulate cyanobacterial growth and metabolism. Thus, current research work is motivated for the evaluation and optimization of the effects of the aforementioned vital factors for improvement of biomass and lipid production of a freshwater, toxic strain of Anabaena circinalis. The modelling and optimization of factors such as nitrogen, light intensity and bicarbonate concentration (source of carbon) to maximize growth and lipid production were based on 20 design point experiments by Response Surface Methodology (RSM) and optimized values were further improved and validated by Particle Swarm Optimization (PSO) algorithm. The maximum optima were obtained 1.829 g L-1 and 39.64 % for biomass production and lipid content respectively from PSO optimization with two different sets of optimal values of factors. It shows 0.44 % and 2.77 % higher values of responses than that of RSM optimization. These asynchronous findings pioneered the enhanced lipid accumulation as well as the growth of a toxic cyanobacterium by optimizing interaction effects of culture conditions through various statistical and computational approaches. •Influencing factors were optimized for the improvement of biomass and lipid production.•RSM was used to optimize the system parameters and validated by PSO algorithm.•Maximum optima were obtained as 1.829 g L-1 and 39.64 % for biomass production.•Results shows that 0.44 % and 2.77 % higher values than that of RSM optimization.